‫האגודה הישראלית לחקר ביצועים‬ ‫)איל"ב(‬ ‫הכינוס השנתי‬ ‫‪ 10-11‬במאי ‪2015‬‬ ‫בארגון‪:‬‬ ‫הטכניון — מכון טכנולוגי לישראל‬ ‫הפקולטה להנדסה תעשייה וניהול על שם ויליאם דוידסון‬ ‫הפקולטה להנדסת חשמל‬ ‫בתמיכת‪:‬‬ ‫הוועדה המארגנת‪:‬‬ ‫נחום שימקין )יו"ר(‬ ‫מיכל פן‬ ‫גייל גלבוע‪-‬פרידמן‬ ORSIS 2015: PROGRAM-AT-A GLANCE Haifa, 10-11 May 2015 Leonardo Hotel – Almog Building Sunday 09:00-09:30 09:30-09:40 09:40-10:30 10:30-10:50 10:50-11:10 11:10-12:25 ‫פנינה‬ ‫סוויטה‬ ‫אלמוג‬ ‫אקוומרין‬ 12:25-13:45 13:45-14:35 ‫אלמוג‬ 14:35-15:25 ‫פנינה‬ ‫אקוומרין‬ 15:25-15:45 15:45-17:25 ‫פנינה‬ ‫אלמוג‬ ‫אקוומרין‬ ‫סוויטה‬ 17:45-19:45 19:45-21:15 ‫לובי‬ Registration ‫אלמוג‬ Opening Session ‫אלמוג‬ S1 Naor Plenary Lecture: Nimrod Megiddo ‫אלמוג‬ Awards Ceremony Coffee Break S2 Parallel Sessions: a. Queues and their Applications b. Game Theory 1 c. Supply Chain Management 1 d. Combinatorial Optimization 1 ‫אקוואמרין‬ Lunch & Orsis General Assembly (13:25) S3 Plenary Lecture Yishay Mansour S4 Semi-Plenary Tutorials: a. Rami Atar b. Eran Hanany Coffee Break S5 Parallel Sessions: a. Topics in Continuous Optim. b. Supply Chain Management 2 c. OR in practice d. Health Care Management Evening Program: National Maritime Museum Evening Program: Dinner, with Guest Lecture Monday 09:00-09:30 09:30-11:10 ‫אלמוג‬ ‫פנינה‬ ‫אקוומרין‬ ‫סוויטה‬ 11:10-11:30 11:30-12:20 ‫פנינה‬ ‫אקוומרין‬ 12:20-13:20 13:20-14:10 ‫אלמוג‬ 14:15-15:55 ‫אלמוג‬ ‫פנינה‬ ‫סוויטה‬ ‫אקוומרין‬ 15:55-16:10 16:10-17:25 ‫אלמוג‬ ‫פנינה‬ ‫אקוומרין‬ 17:25-17:30 Registration ‫לובי‬ M1 Parallel Sessions: a. Combinatorial Optimization 2 b. Queues and Stochastic Systems 1 c. Game Theory 2 d. Police Applications Coffee Break M2 Semi-plenary Tutorials: a. Assaf Zeevi b. Iddo Eliazar Lunch M3 Plenary Lecture: Aharon Ben-Tal M4 Parallel Sessions: a. Prize Winners Session b. Scheduling c. Optimization d. Queues and Stochastic Systems 2 Coffee Break M5 Parallel Sessions: a. Transportation b. Water Management c. Strategic Behavior in Queues Farewell .10 ‫ רח' דוד אלעזר‬,(‫ הרחב יותר‬,‫ בניין "אלמוג" )הבניין הצפוני‬,‫ מלון לאונרדו חיפה‬:‫מקום הכנס‬ ."‫ סמוך לתחנת הרכבת "חוף הכרמל‬,‫המלון ממוקם על חוף הים בכניסה הדרומית לחיפה‬ ‫ כולל יציאה וחזרה‬,17:45-19:45 ‫ נפתח בסיור מודרך במוזיאון הימי הלאומי )בשעות‬:‫ יום א' בערב‬:‫תוכנית חברתית‬ ‫ ולאחריו ארוחת ערב מלווה בהרצאתו של ד"ר ג'ק סילברמן בנושא "השלכות סביבתיות של חיפושי גז‬,(‫למלון הכנס‬ ."‫ונפט בים העמוק באזור המים הכלכליים של מדינת ישראל‬ ‫ המרחק למלון הוא כקילומטר אחד בהליכה נוחה צפונה לאורך‬,"‫ יש לרדת בתחנת "חוף הכרמל‬-‫ ברכבת‬:‫תחבורה‬ .‫ שרות מוניות זמין ביציאה המערבית מהרכבת‬,‫ לחילופין‬.‫חוף הים‬ (‫ הכניסה מצפון דרך צומת דרך הים )מסעדת מקסים‬,(‫ יש להיכנס לרחוב דוד אלעזר )כביש פנימי לאורך החוף‬- ‫במכונית‬ .‫ הכניסה אליו מצפון לבניין אלמוג‬,‫ ניתן לחנות בחניון הפנימי של הבניין‬.(‫או מדרום דרך צומת מת"ם )מול דרך פרויד‬ Supported by The organizing Committee: Nahum Shimkin (Chair) Michal Penn Gail Gilboa-Friedman 1 Detailed Program ‐ Sunday, 10.5 09:00-09:30 Registration 09:30-09:40 S1 Opening Session 09:40-10:30 Naor Plenary Lecture ‫אלמוג‬ Nimrod Megiddo, IBM Almaden The State-of-the-Art of the Theory of Linear Programming Chair and Opener: Aharon Ben-Tal 10:30-10:50 Awards Ceremony ‫אלמוג‬ ‫אלמוג‬ 10:50-11:10 Coffee Break 11:10-12:25 S2a Queues and Their Applications Chair: Uri Yechiali ‫פנינה‬ S2b Game Theory 1 Chair: Eran Hanani S2 Parallel Sessions: Amir Elalouf, Yael Perlman, Uri Yechiali Who Will Receive the Kidney? A Queueing Model for Live Organ Allocation Gabi Chanukov, T. Avinadav, T. Chernonog, U. Spiegel, U.Yechiali Utilization of Server’s Idle Time for Greater Efficiency in Queueing Systems Nir Perel, Uri Yechiali Noam Goldberg An Unlimited Batch-Service Multi-Queue System with Uniform and with Geometric Group-Joining Policies Nonzero-sum Nonlinear Network Interdiction Gal Cohensius, Ella Segev Sequential First Price Auction Shoshana Anily Total Balancedness of Regular Cooperative Games Reut Noham, Michal Tzur Network Design in Humanitarian Supply Chains: Pre and Post Disaster Decision Making The Two-Phase Stochastic Lotsizing Problem with Optimal Timing of Additional Replenishment ‫סוויטה‬ S2c Supply Chain Management 1 Chair: Michal Tzur Dina Smirnov, Yale T. Herer, Retsef Levi, Assaf Avrahami Ohad Eisenhandler, Michal Tzur The Humanitarian Pickup and Distribution Problem S2d Combinatorial Optimization 1 Niv Buchbinder, Moran Feldman, Seffi Naor, Roy Schwartz Approximation Algorithms for Submodular Maximization Chair: Seffi Naor Michael Dreyfuss, Yahel Giat Fill Rate Window as a Criterion for Spares Allocation Yaarit M. Cohen, Liron Yedidsion The Traveling Repairman Problem on a Single Line with Release Times ‫אלמוג‬ ‫אקוומרין‬ 2 12:25-13:45 Lunch, & Orsis General Assembly (13:25) 13:45-14:35 S3 Plenary Lecture ‫אלמוג‬ Yishay Mansour, Tel Aviv University Robust Inference and Local Algorithms Chair: Nahum Shimkin 14:35-15:25 S4 Semi-Plenary Tutorials Rami Atar, Technion ‫פנינה‬ Moderate Deviations and Heavy Traffic Chair: Haya Kaspi Eran Hanany ‫אקוומרין‬ Dynamic Decisions under Ambiguity Chair: Gail Gilboa Freedman 15:25-15:45 Coffee Break 15:45-17:25 S5a: Topics in Continuous Optimization Chair: Shoham Sabach ‫פנינה‬ S5b Supply Chain Management 2 Chair: Yael Perlman ‫אלמוג‬ S5 Parallel Sessions: Shimrit Shtern, Amir Beck Shoham Sabach, Amir Beck Marc Teboulle Linearly Convergent Conditional Gradient Variants for Non-strongly Convex Functions An Alternating Semi-Proximal Method for Nonconvex Problems Jérôme Bolte, Edouard Pauwels Nonsmooth Optimization with Semi-algebraic Data: Convergence Beyond the Proximal Setting Nir Halman Yigal Gerchak Approximation Schemes for Stochastic Dynamic Programs with Continuous State and Action Spaces Consignment Contract for Mobile Apps Between a Single Retailer and Competitive Developers with Different Risk Attitudes Strategic Inventory with Demand Uncertainty Yale Herer, Avinoam Tzimerman 3rd Party Logistics Coordinator Mechanism for Transshipments in a Decentralized System Tal Avinadav, Tatyana Chernonog, Yael Perlman The Effect of Risk Sensitivity on Supply Chain Management under a Consignment Contract with Revenue Sharing and Quality Investment Tal Avinadav, Tatyana Chernonog, Yael Perlman (S5 continues on the next page) 3 S5c OR in Practice Vladimir Lipetz Chair: Michael Masin Evgeny Shindin, Odellia Boni, Michael Masin ‫אקוומרין‬ S5d Health Care Management Chair: Amir Elalouf ‫סוויטה‬ Closing the Gap between OR Models and Real Applications Robust Optimization of System Design David Amid, Ateret AnabyTavor Watson Tradeoff Analytics - Decision Making Made Accessible for Everyone Zilla Sinuany-Stern OR/MS in the Higher Education Developing an Optimal Appointment Scheduling for Healthcare Systems with Non-flexible Uptake Time Under Pre-determined Service Levels ‫מודל החלטה דינמי לקבלת כליה מן המת – בהתאם‬ ‫ מיכאל בנדרסקי‬,‫ישראל דויד‬ ‫לפונקציית הכשל והתפלגות החיים של המועמד להשתלה‬ Using a "Floating Patients" Approach for Improving Guy Wachte, Amir Elalouf Patients-Flow in Hospitals and Emergency Departments Challenges in Medical Quality Improvement Eliaz Miller, MD Illana Bendavid, Yariv N. Marmor, Boris Shnits Evening Program (Sunday) 17:45-19:45 National Maritime Museum (bus leaves at 17:45) 19:45-21:15 Dinner, with Guest Lecture: ‫ השלכות סביבתיות של חיפושי גז ונפט בים העמוק באזור‬.‫ המכון הלאומי לאוקיאנוגרפיה‬,‫ד"ר ג'ק סילברמן‬ .‫המים הכלכליים של ישראל‬ 4 Detailed Program ‐ Monday, 11.5 09:00-09:30 Registration 09:30-11:10 M1a Combinatorial Optimization 2: In Memory of Uri Yovel Chair: Rafi Hassin ‫אלמוג‬ M1b: Queues and Stochastic Systems 1 Chair: Galit Yom-Tov ‫פנינה‬ M1c Game Theory 2 Chair: Gail Gilboa Freedman ‫אקוומרין‬ M1d OR Applications in the Israeli Police Chair: Mali Sher ‫סוויטה‬ M1 Parallel Sessions: Rafi Hassin, Uri Yovel Sequential Scheduling on Identical Machines Tal Raviv, Uri Yovel, Tom Cherchy New Integer Programming Formulation of Routing Problems in Dense Networks Dorit S. Hochbaum, Asaf Levin Weighted Matching with Pair Restrictions Enrique Gerstl, Gur Mosheiov Single Machine Scheduling to Minimize Total Earliness-Tardiness with Unavailability Period Nitzan Carmeli, Haya Kaspi, Avishai Mandelbaum Modeling and Analyzing IVR Systems, as a Special Case of Self-services Zhenghua Long, Nahum Shimkin, Jiheng Zhang Optimal Priority Control for Multiclass Many-server Queues with General Patience Distributions Galit B. Yom-Tov, Jing Dong, The Impact of Delay Announcements on Hospital Network Coordination and Waiting Times Elad Yom-Tov Eitan Bachmat Geometric Queueing Theory Shiran Rachmilevitch Egalitarian-Utilitarian Bounds in Nash's Bargaining Problem Payoff Externalities and Social Learning Itai Arieli Gail Gilboa Freedman, Rann Smorodinsky Axiomatization of Privacy Gadi Fibich, Arieh Gavious Revenue Equivalence of Large Asymmetric Auctions ,‫ תהילה הירש‬,‫חיננית אפרתי‬ ‫ מלי שר‬,‫אירית נוביק‬ ‫ייעול עבודת הסיירים ביחידת המקומות הקדושים של‬ ‫ישראל‬-‫משטרת‬ ‫ אורטל‬,‫שירלי ארקוסין‬ ‫ מלי‬,‫ איריס פורמה‬,‫סעידיאן‬ ‫שר‬ ‫השמת עובדים אופטימאלית‬ ,‫ ינון סבתו‬,‫אלי אורבך‬ ‫ מלי שר‬,‫מיכאל דרייפוס‬ ‫ישראל‬-‫ייעול פריסת מצלמות האכיפה במשטרת‬ Mali Sher, Nicole Adler, Shalom Hakkert A Traffic Enforcement Camera Operational Model 11:10-11:30 Break 5 11:30-12:20 M2 Semi-Plenary Tutorials Assaf Zeevi, Columbia University ‫פנינה‬ Chasing Bandits: Exploration and Exploitation in a Non-stationary Environment Chair: Michal Penn Iddo Eliazar, Intel ‫אקוומרין‬ From Inequality to Big Data: The Sociogeometry of Sizes Chair: Uri Yechiali 12:20-13:20 Lunch 13:20-14:10 M3 Plenary Lecture ‫אלמוג‬ Aharon Ben-Tal Some Remedies for Some Intractable Optimization Problems Chair: Avishai Mandelbaum 14:15-15:55 M4 Parallel Sessions: M4a: Opimization and Stochastics Amir Beck, Nadav Hallak (Rothblum Award) On the Minimization Over Sparse Symmetric Sets: Projections, Optimality Conditions and Algorithms Josh Reed, Yair Shaki A Fair Policy for the G/GI/N Queue with Multiple Server Pools Prize Winners Session Chair: Moshe Haviv ‫אלמוג‬ (Rothblum Award) Liron Ravner (Mehrza Award) Equilibrium Arrival Times to a Queue with Order Penalties Arik Sadeh An Asymptotically Optimal Online Algorithm to Choosing Binary Factors Affecting Purchasing in eCommerce Public Transportation with Flexible Timetable M4b Scheduling Tal Grinshpoun, Elad Shufan, Hagai Ilani Chair: Dvir Shabtay Enrique Gerstl, Gur Mosheiov Scheduling with Two Competing Agents to Minimize Total Weighted Earliness Baruch Mor, Gur Mosheiov Minimizing Maximum Earliness and Minimizing the Number of Early Jobs on a Proportionate Flowshop An Asymptotically Optimal Online Algorithm to Minimize the Total Completion Time on Two Multipurpose Machines with Unit Processing Times ‫פנינה‬ Dvir Shabtay, Shlomo Karhi (M4 continues on the next page) 6 M4c Optimization Chair: Gideon Weiss ‫סוויטה‬ Saleh Soltan, Mihalis Yannakakis, Gil Zussman Renata Poznanski, Refael Hassin Rafiq Mansour, Yair Censor Evgeny Shindin, Gideon Weiss M4d: Queues and Stochastic Systems 2 Chair: Yoav Kerner Yoav Kerner, Opher Baron Ruth Sagron, Gad Rabinowitz, Israel Tirkel Barron Yonit ‫אקוומרין‬ Tal Avinadav, Tatyana Chernonog, Yael Lahav, Uriel Spiegel Joint Cyber and Physical Attacks on Power Grids: Graph Theoretical Approaches for Information Recovery Optimal Multi-Period Network Flows with Coupling Constraints New Douglas-Rachford Algorithmic Structures and Their Convergence Analyses A simplex-type Algorithm for Continuous Linear Programming Queueing Model for Safety Stock Inventory Model with Perishable Items and General Distribution Hybrid Simulation-Regression Approximation for Tandem Queues with Downtime Events Clearing Control Policies for MAP Inventory Process with Partially Satisfied Demand Dynamic Pricing and Promotion Expenditures in an EOQ Model of Perishable Items 15:55-16:10 Break 16:10-17:25 M5 Parallel Sessions: M5a Transportation Mor Kaspi, Tal Raviv, Michal Tzur Regulating One-Way Vehicle Sharing Systems through Parking Reservation Policies Chair: Tal Raviv Sharon Datner, Tal Raviv, Michal Tzur Setting Inventory Levels in Bike-Sharing Networks Hila Hindi-Ling, Hillel BarGera, Arie Sachish The Effect of a Quayside Cranes Buffer on Ships Unloading Process ‫אלמוג‬ Modeling Combined Technological, Environmental M5b Amos Bick, Ioannis K. Water Management Kalavrouziotis, Gideon Oron and Economic Considerations in Domestic Sludge Chair: Amos Bick ‫פנינה‬ M5c: Strategic Behavior in Queues Chair: Moshe Haviv ‫אקוומרין‬ David Raz, Ariel Daliot Beni Lew, Olga Tarnapolski, Vladimir Yudachev, Amos Bick Moshe Haviv, Binyamin Oz Moshe Haviv, Liron Ravner Nahum Shimkin Reuse via the Analytic Hierarchy Process (AHP) A Generic Modeling Language for Water Supply Systems Optimization Membrane Treatment of Brackish Groundwater for Unrestricted Use for Irrigation and Sustainable Agricultural Production: Decision Analysis via The Hasse Diagram Technique (HDT) Self-Regulation of a Queue via Random Priorities Accumulating Priority Queue with Strategic Customers What to (Truthfully) Tell Customers to Make Them Join a Queue 17:25-17:30 Fairwell 7 ABSTRACTS Plenary Lectures S1: Naor Plenary Lecture. Chair and Opener: Aharon Bental Speaker: Nimrod Megiddo, IBM Almaden Research Center The State-of-the-Art of the Theory of Linear Programming The talk will survey the following topics: (i) interior-point methods, (ii) worst-case, probabilistic and smoothed analysis of the simplex method, (iii) strongly polynomial bounds, and (iv) the diameter of a polytope. S3: Plenary Lecture. Chair: Nahum Shimkin Yishay Mansour, Tel Aviv University Robust Inference and Local Algorithms Robust inference is an extension of probabilistic inference, where some of the observations may be adversarially corrupted. We limit the adversarial corruption to a finite set of modification rules. We model robust inference as a zero-sum game between an adversary, who selects a modification rule, and a predictor, who wants to accurately predict the state of nature. There are two variants of the model, one where the adversary needs to pick the modification rule in advance and one where the adversary can select the modification rule after observing the realized uncorrupted input. For both settings we derive efficient near optimal policy runs in polynomial time. Our efficient algorithms are based on methodologies for developing local computation algorithms. We also consider a learning setting where the predictor receives a set of uncorrupted inputs and their classification. The predictor needs to select a hypothesis, from a known set of hypotheses, and is tested on inputs which the adversary corrupts. We show how to utilize an ERM oracle to derive a near optimal predictor strategy, namely, picking a hypothesis that minimizes the error on the corrupted test inputs. Based on joint works with Uriel Feige, Aviad Rubinstein, Robert Schapira, Moshe Tennenholtz, Shai Vardi. 8 M3: Plenary Lecture. Chair: Avishai Mandelbaum Aharon Ben-Tal, Technion Some Remedies for Some Intractable Optimization Problems The need to solve real-life optimization problems poses frequently a severe challenge, as the underlying mathematical programs threaten to be intractable. The intractability can be attributed to any of the following properties: large dimensionality of the design dimension; lack of convexity; parameters affected by uncertainty. In problems of designing optimal mechanical structures (truss topology design, shape design, free material optimization), the mathematical programs typically has a large dimensional Semi Definite Program. Some Signal Processing and Estimation problems may result in nonconvex formulations. In the wide area of optimization under uncertainty, some classical approaches, such as chance (probabilistic) constraints, give rise to nonconvex NP-hard problems. In all the above applications, we explain how the difficulties were resolved. In some cases this was achieved by mathematical analysis (notoriously duality theory) which converted the problems (or its dual) to a tractable convex program. In the Robust Control example, a reparameterization scheme is developed under which the problem is converted to a tractable deterministic convex program. Semi-Plenary Tutorials Semi-Plenary Session S4 Rami Atar, Technion. Chair: Haya Kaspi Moderate Deviations and Heavy Traffic There is a vast literature on heavy traffic analysis of controlled queueing models at the diffusion scale. We will motivate the study of these models at the moderate deviations scale, and describe recent developments in this direction. Eran Hanany, Tel Aviv University. Chair: Gail Gilboa-Freedman Dynamic Decisions under Ambiguity Preferences that allow for decision makers to care about ambiguity have drawn increasing interest in recent years. Dynamic consistency is the fundamental requirement that contingent plans made at an initial time should remain optimal at later times. This requirement leads to Bayesian updating under ambiguity neutrality, but to different updating rules under ambiguity aversion. I will present recent advances in this field, including a theory of belief polarization as an optimal response to ambiguity, and equilibrium notions for incomplete information games involving players who perceive ambiguity about the types of others. 9 Semi-Plenary Session M2 Assaf Zeevi, Columbia University. Chair: Michal Penn Chasing Bandits: Exploration and Exploitation in a Non-stationary Environment In a stochastic multi-armed bandit (MAB) problem a gambler needs to choose at each round of play one of K arms, each characterized by an unknown reward distribution. Reward realizations are only observed when an arm is selected, and the gambler¹s objective is to maximize his cumulative expected earnings over some given horizon of play T. To do this, the gambler needs to acquire information about arms (exploration) while simultaneously optimizing immediate rewards (exploitation). Bandit problems have been studied extensively since their inception. The bulk of this literature is concerned with settings where the reward distributions are stationary, namely, the statistical properties of the arms do not change over time. In this talk we will consider a formulation that relaxes this restriction, highlight some of the key theoretical findings that characterize this setting, and discuss an emerging application domain in the space of online services that motivates our work. Iddo Eliazar, Intel Corporation. Chair: Uri Yechiali From Inequality to Big Data: The Sociogeometry of Sizes In the era of big data we are inundated with large datasets of sizes – non-negative numerical values representing count, score, length, area, volume, duration, mass, energy, etc. These datasets display numerous types of statistical variability, or intrinsic randomness, commonly quantified either by standard deviation, or by entropy. The standard deviation measures the sizes' Euclidean divergence from their mean, entropy measures the sizes' divergence from the benchmark of pure determinism, and both these gauges are one-dimensional. In this talk we overview a sociogeometric framework of quantifying the randomness of datasets of sizes. The framework follows a socioeconomic approach of measuring the sizes' inequality – their divergence from the benchmark of pure egalitarianism – and yields a rich and multidimensional methodology of gauging the sizes' statistical variability via sociogeometric inequality indices. The aim of this overview is to serve both researchers and practitioners as a crash-intro to the sociogeometry of sizes, and as a crashmanual to the implementation of this methodology. References: I. Eliazar, The sociogeometry of inequality: Part I, Physica A (2015) in press. DOI: 10.1016/j.physa.2014.12.021 I. Eliazar, The sociogeometry of inequality: Part II, Physica A (2015) in press. DOI: 10.1016/j.physa.2015.01.016. 10 Parallel Sessions (in Chronological Order) Sunday, 10.5 S2a: Queues and Their Applications. Chair and Organizer: Uri Yechiali Amir Elalouf, Yael Perlman, Uri Yechiali Who Will Receive the Kidney? A Queueing Model for Live Organ Allocation We consider models of dynamically allocating randomly arriving kidneys to candidates waiting for transplant. Two main models are investigated: (i) a kidney is allocated only to a candidate with the same blood type, and (ii) a kidney with blood type O is allocated to either O or A type of candidate, while type A kidney is allocated only to type A candidate. The allocation is based on the measure Expected Value of Transplantation (EVT) which takes into consideration Human Leukocyte Antigen (HLA) matching. We also study the case where a fraction of the available kidneys is kept for future candidates. Finally, we treat the case of exponential life time of a kidney and an exponential residual life time of candidates. Gabi Chanukov, T. Avinadav, T. Chernonog, U. Spiegel, U.Yechiali Utilization of Server’s Idle Time for Greater Eficiency in Queueing Systems We consider an M/M/1-type queueing system in which the server utilizes his/her idle time to partially prepare future services off-line in order to reduce the on-line service duration. Such a routine is applicable for service systems in which part of the service (termed “start”) can be prepared without the presence of the customer. The “starts” are produced and stored during periods of time when there are no customers in the system. When a customer arrives, the server stops producing new “starts” and completes an inventoried “start” (if any) in a faster processing rate. When all inventoried “starts” are used, the service rate returns to normal until the next time the server is idle and returns producing new “starts” again. The decision variable is the maximal number of inventoried "starts" in order to optimize an economical measure (e.g., minimal total cost rate or average waiting time of customers). Five model variants are formulated and analyzed: (i) The duration of a full service and of each of its two parts is exponentially distributed; (ii) The duration of each service part is exponentially distributed, and the duration of a full service is a simple summation of its two parts; (iii) The customers know when inventoried “starts” are available and increase their arrival rate; (iv) Some of the inventoried “starts” suffer from decay, thus losses, since they cannot be used; and (v) The server faces two types of customers: one that is willing to pay more for a fast service and another that pays less for a normal service rate. Nir Perel, Uri Yechiali An Unlimited Batch-Service Multi-Queue System with Uniform and with Geometric Group-Joining Policies We study a multi-queue single-server system with unlimited-size batch service under either Uniform or Geometric group-joining policy, where the next queue to be served is the one with the most senior customer. We derive a set of performance measures related to sojourn times, group sizes and busy periods, and present a corresponding set of numerical results. The effects of the system's parameters on the performance measures are investigated. 11 S2b: Game Theory 1. Chair: Eran Hanani Noam Goldberg Nonzero-sum Nonlinear Network Interdiction A novel nonzero-sum game is presented for a variant of a classical network interdiction problem. In this model an interdictor (e.g. an enforcement agent) decides how much of an inspection resource to spend along each arc in the network in order to capture the evader (e.g. a smuggler). The evader selects a probability distribution on paths from source nodes to destinations. The evasion probabilities nonlinearly decrease in the inspection resources spent by the interdictor. We show that for logarithmically convex functions, Nash equilibria of this game can be efficiently computed. A special case of exponential functions is further analyzed and presented with examples. Gal Cohensius, Ella Segev Sequential First Price Auction We study asymmetric first price auctions in which bidders place their bids sequentially, one after the other and only once. We show that with a strong bidder and a weak bidder (in terms of first order stochastic dominance of their valuations distribution functions), already with small asymmetry between the bidders, the expected revenue in the sequential bidding first price auction (when the strong bidder bids first) is higher than in the simultaneous bidding first price auction. Moreover it is higher than the expected revenue in the second price auction. The expected payoff of the weak bidder is also higher in the sequential first price auction. Therefore a seller interested in increasing revenue facing asymmetric bidders may find it beneficial to order them and let them bid sequentially instead of simultaneously. In terms of efficiency, both the simultaneous first price auction and the sequential first price auction cannot guarantee full efficiency (as opposed to a second price auction). The sequential bidding auction when the stronger bidder bids first achieves lower efficiency than the simultaneous auction. However, when the order is reversed and the asymmetry is large enough the sequential first price auction achieves higher efficiency than the simultaneous auction. Shoshana Anily Total Balancedness of Regular Cooperative Games The paper "Cooperation in service systems" by Anily and Haviv (Operations Research 2010) attracted the attention of the research community as it proves that the most basic model of cooperation in queueing, where a number of M/M/1 systems cooperate by forming a single M/M/1 system whose arrival (service) rate is the sum of the individual respective rates, is totally balanced. The interest in the paper was also a result of the original proof's technique: the authors define an auxiliary game that is monotone and whose core is contained in the core of the game and proved that the auxiliary game is concave (submodular), implying that its core is totally balanced, and therefore also the original game is totally balanced. The idea of the auxiliary game can easily be applied to any non-monotone game in order to generate a monotone new game whose core is a subset of the core of the primary game. However, the proof that the characteristic function of the auxiliary game is concave is custom-made to the specific given set function. Suchlike proofs are usually tedious. Several researchers have raised the question whether the idea behind the auxiliary game could be further generalized beyond that special game as a tool in proving total balancedness of other cooperative games. In this paper we consider regular games where each player is associated with a real vector, and the characteristic function of a coalition depends only on the collection of the real vectors that are associated with the players. Under a few conditions the characteristic function of regular games, which is a set function, can be replaced by a mapping of real vectors into the real numbers. We prove that if this mapping satisfies the law of diminishing returns then the auxiliary game is concave, and therefore the game itself is totally balanced. We demonstrate the simplicity of the technique on the above described game, as well as on other games. 12 S2c: Supply Chain Management 1 Chair: Michal Tzur Reut Noham, Michal Tzur Network Design in Humanitarian Supply Chains: Pre and Post Disaster Decision Making Humanitarian logistics is an emerging field that addresses the major challenges in providing humanitarian relief operations for both natural and man-made disasters. Strategies to overcome these challenges include disaster preparedness and response, under high uncertainty and limited availability of resources and infrastructure to address needs. Existing models in the academic literature address network design and resource allocation challenges that are relevant to pre- and post-disaster situations, respectively. However, they adopt a global optimization point of view, which may not be attainable, due to the actual decision making process. The latter is based mostly on practitioners' knowledge and experience, simple rules of thumb, and the local population behavior. In our work, we develop new mathematical models that represent practical considerations such as those mentioned above. For small/medium instances of the considered problem we present an efficient optimal solution method while for large instances we use a heuristic algorithm which is based on Tabu search. Our preliminary results demonstrate that the effectiveness of network design decisions (made at the pre-disaster phase) is sensitive to post-disaster decisions, and therefore, to the extent possible, it is critical to accurately model/predict post-disaster decisions during the pre-disaster phase. Dina Smirnov, Yale T. Herer, Retsef Levi, Assaf Avrahami The Two-Phase Stochastic Lotsizing Problem with Optimal Timing of Additional Replenishment Recent advances in Information Technology have provided decision makers in the supply chain with extensive, often real time and accurate, data. Wisely used data can assist in improving the performance of inventory systems. In particular, for single-period systems it has become possible to receive actual and accurate sales data more frequently than once in the sales period. This has enabled decision makers to execute an additional replenishment based on early sales information. We are given a single sales period and a single possibility to review the inventory level after the start of sales and to execute an additional replenishment if necessary. The items from the additional replenishment are intended to be sold during that same sales period. It is desirable to find the optimal quantity to replenish before the start of sales, the optimal moment to perform the additional review, and the optimal quantity to replenish at the moment of additional review. This problem has not been addressed with respect to this set of policy decisions as of yet. In this work we use analytical tools and propose an exact and tractable algorithm for simultaneously determining these policy decisions. Our model is applicable to a wide range of businesses such as bakeries, retail of seasonal, perishable or technology-related goods, and print industry. Ohad Eisenhandler, Michal Tzur The Humanitarian Pickup and Distribution Problem Food rescue, i.e., the collection of perishable products from food suppliers who are willing to make donations and their distribution to welfare agencies that serve individuals in need, has become increasingly widespread in recent years. This is due to economic crises that have increased the demand for nutritional aid, and the benefit to donors who can avoid in this way the costs of destroying excess production while reflecting a social-aware image. The problem we study focuses on the logistic challenges of a food bank coordinating this operation on a daily basis, using vehicles with limited capacity whose travel time cannot exceed an imposed maximal duration (defined by the driver's working hour regulations). We model this problem as a routing–allocation problem, with the aim of maintaining equitable allocations to the different agencies in each period, while delivering as much as possible in total. We discuss an appropriate objective function that promotes effectiveness and equity. We show how these two measures can be combined in a way that satisfies desired properties of the allocation, that is easy to compute and implement within a mathematical formulation, and that balances effectiveness. 13 S2d: Combinatorial Optimization 1 Chair: Seffi Naor Niv Buchbinder, Moran Feldman, Seffi Naor, Roy Schwartz Approximation algorithms for Submodular Maximization The study of combinatorial problems with submodular objective functions has recently attracted much attention, and is motivated by the principle of economy of scale, prevalent in real world applications. Submodular functions are also commonly used as utility functions in economics and algorithmic game theory. In combinatorial optimization submodular functions and submodular maximization play a major role as several well-known examples of submodular functions include cuts in graphs and hypergraphs, rank functions of matroids and covering functions. In this talk I will present several recent results for the problem of maximizing a general submodular function. Michael Dreyfuss, Yahel Giat. Fill Rate Window as a Criterion for Spares Allocation The biggest problem for the successful adoption of electric cars is the frequent need to recharge the battery and the waiting time associated with it. One of the suggestions to overcome this problem is that carmakers retain ownership of batteries and provide service stations in which customers replace their depleted batteries with recharged batteries in lieu of waiting for their battery to recharge. Motivated by this approach, we consider spare allocations in an exchangeable-item multi-location repair system with Poisson arrivals and ample servers with general repair time distribution. Customers expect to be served within a certain time window and penalize the service provider if they have to wait more than that window of time. Accordingly, instead of minimizing average waiting time, we suggest that firms should consider maximizing the fill rate window, i.e. the probability that customers wait less than a predetermined time window. We derive the entire system’s fill rate window for any time window, and characterize its functional form. For each location the fill rate window can be either concave or S-shaped. For a sufficiently large time window, we show that the system’s problem is concave in which case the “biggest bang for the buck” approach can be used to solve the optimal spares allocation. When the window is not large enough, at least some of the locations have an S-shaped fill rate window. In this case we define a concave covering function. Since the covering function is concave we can efficiently derive its optimal solution. To find the optimal solution of the original problem we need to execute a correction procedure, which is provided. We provide a numerical example motivated by the recent unsuccessful attempt to introduce electric cars into Israel. Yaarit M. Cohen, Liron Yedidsion The Traveling Repairman Problem on a Single Line with Release Times The Traveling Repairman problem (TRP) is a well-known NP-hard problem (also referred to in the literature as The Delivery Man Problem and The Minimum Latency Problem). In the classical TRP, a repairman has to visit each of n stationary targets exactly once, in order to minimize the sum of the targets' flow time. Where the flow time of target i (Fi) is the time since the target appears till the time it is intercepted. In this research we consider a special case of TRP where all targets are confined to a single line. However, some targets might not be available at time zero and appear later on. We denote this problem as the Single Line TRP (SL-TRP). Algorithms for SL-TRP have real-life applications that can help civil and military needs. Consider a line of machines each one requires a delivery of raw material delivered by a robotic arm moving along a production line; or a border patrol unit that has to reach numerous check points along the border based on alerts of invasion. Note that the border doesn't have to be strait to be considered a "strait line" as long as the patrol unit travels alongside the border line. In both of these problems the robot or the border patrol unit has to visit each point exactly once and should complete its tour and minimize the sum of the targets' flow time. We proved this problem to be NP-hard by using a reduction from a special case of the partition problem. Next, we suggest several heuristics as well as exact Branch and Bound (B&B) algorithm for solving the SL-TRP. We use computational experiments in order to evaluate and compare their performances as well as the efficiency of the B&B algorithm when using large instances. 14 S5a: Topics in Continuous Optimization Chair: Shoham Sabach, Organizer: Amir Beck Shimrit Shtern, Amir Beck Linearly Convergent Conditional Gradient Variants for Non-strongly Convex Functions The conditional gradient method was presented by Frank and Wolfe in 1956. Its aim is to minimize a smooth function over a compact convex set, where each iteration of the method consists of minimizing a linear function over the feasible set. Generally, the method’s rate of convergence is 1/k, and is linear only in specific cases, where the function optimized is strongly convex and the optimal solution lies in the relative interior of the feasible set, or when the set is uniformly (strongly) convex and the gradient of the objective function is bounded away from zero. Lately, two variants of the conditional gradient algorithm -- the away step conditional gradient and the local conditional gradient -- were proven to converge linearly for minimizing strongly convex functions over polyhedral sets. We extend these results, and prove that these algorithms also admit a linear rate of convergence for well-structured functions, which are not strongly convex. Moreover, for the version that incorporates away steps, we provide a new convergence rate with computable constants that also enables the comparison between the two algorithms. Shoham Sabach, Amir Beck, Marc Teboulle An Alternating Semi-Proximal Method for Nonconvex Problems We consider a broad class of regularized structured total-least squares problems (RSTLS) encompassing many scenarios in image processing. This class of problems results in a nonconvex and often nonsmooth model in large dimension. To tackle this difficult class of problems we introduce a novel algorithm that blends proximal and alternating minimization methods by benefi cially exploiting data information and structures inherently present in RSTLS. The proposed algorithm, which can also be applied to more general problems, is proven to globally converge to critical points, and is amenable to e_fficient and simple computational steps. Jérôme Bolte, Edouard Pauwels Nonsmooth Optimization with Semi-algebraic Data: Convergence Beyond the Proximal Setting We focus on convergence of iterative schemes for non-smooth non-convex optimization in finite dimension. Most of current results are given for "prox-friendly" data: the nonsmooth part can be handled through efficiently computable operators. Many methods and applications do not fit this setting. We focus on Sequential Quadratic Programming ideas for general Nonlinear Programs. Despite their large usage, these methods lack satisfactory convergence analysis. This work constitutes a step toward the obtention of such theoretical guaranties. We combine properties of local tangent majorizing models with results from algebraic geometry to analyse the asymptotic properties of two recent methods for solving general Nonlinear Programming problems. Nir Halman Approximation Schemes for Stochastic Dynamic Programs with Continuous State and Action Spaces Dynamic optimization problems are generally NP-hard to solve, thus approximated solutions are of interest. The main approaches to tackle dynamic optimization problems are optimal control (Pontryagin's minimum principle) and dynamic programming (Bellman's optimality equation). Taking the latter approach, I'll consider a broad subfamily of dynamic programs (DP's) that is still NP-hard. DP's of this class with discrete state and action spaces are known to admit relative-error approximation schemes. I'll show that such DPs with *continuous* state and action spaces are harder to approximate than their discrete counterparts. In particular, they do not necessarily admit constant factor approximations. Despite this discouraging result, I'll be successful at designing for them approximation schemes by using a novel measure of error 15 S5b: Supply Chain Management 2 Chair and Organizer: Yael Perlman Tal Avinadav, Tatyana Chernonog, Yael Perlman Consignment Contract for Mobile Apps Between a Single Retailer and Competitive Developers with Different Risk Attitudes Mobile applications (apps) are software programs designed to run on smartphones and tablets. They are commonly downloaded through application distribution platforms, such as the Apple (iTunes) App Store, Google Play, the Windows Phone Store and BlackBerry App World. As suggested by Apple’s central marketing message—“there’s an app for that”—the market for apps is crowded and diverse (BBC Trust, 2010). At the same time, there is intense competition among companies marketing similar apps. For example, the iTunes App Store offers at least twelve device finder apps, similar to “Find My iPhone”; these apps compete with one another in terms of both price and quality (Myers, 2012). Clearly, the question of how to manage brand competition and channel competition is important both for app developers (the suppliers) and the platform distributor (i.e., the app retailer). Yigal Gerchak Strategic Inventory with Demand Uncertainty We consider a decentralized two-period problem with uncertain demands. The manufacturer determines the second period's wholesale price based on the amount of inventory left at the retailer from the first period. Research with deterministic demands has shown that the retailer will over-order in the first period, so that what is not sold then will cause the manufacturer to set a low wholesale price, hence the name Strategic Inventory. We examine the veracity of this phenomenon in a setting with random demands. We do so in a setting with fixed retail prices, as well as in one with price-sensitive demand. Yale Herer, Avinoam Tzimerman 3rd Party Logistics Coordinator Mechanism for Transshipments in a Decentralized System We investigate a multi-retailer single period stochastic lot sizing problem. The retailers are independent and experience stochastic demand for a single item. Moreover, the retailers act separately, doing what is in their own best interest, i.e., decentralized control. We add to this constellation the option of transshipments. Transshipments have been widely studied with central control, but much less so in a decentralized setting. We introduce transshipments by proposing a mechanism that involves the presence of a third party whom we call 3PLC, 3rd Party Logistics Coordinator. 3PLC incentivizes the retailers to take part in the mechanism by providing each retailer with payments for their holding and shortage costs (the incentive), and in return, 3PLC is allowed to reap the benefits of transshipments. Using 3PLC we are able to implement transshipments and coordinate the supply chain. The 3PLC mechanism answers this challenge without sharing private information such as cost and demand data among the retailers. We also show how the mechanism can be used to split supply chain profits among the players in any way desired. Most importantly, this mechanism both retains the independent nature of the entities and completely exploits the benefits of the centralized system. Tal Avinadav, Tatyana Chernonog, Yael Perlman The Effect of Risk Sensitivity on Supply Chain Management under a Consignment Contract with Revenue Sharing and Quality Investment We analyze pricing and quality investment strategies in a two-echelon supply chain of mobile applications (apps) under a consignment contract with revenue sharing. Specifically, we focus on how risk-sensitive behavior of supply chain members affects chain performance. The platform provider sets the level of revenue sharing, and the app developer determines the investment in quality and the selling price of the app. The demand for an app, which depends on both price and quality investment, is assumed to be uncertain, so the risk attitude of the supply chain members has to be considered. The members′ equilibrium strategies are analyzed under different attitudes toward risk: averse, neutral and seeking. We show that the retailer's utility function has no effect on the equilibrium strategies, and suggest schemes to identify these strategies for any utility function of the developer. We find that (i) the revenue sharing contract circumvents the double marginalization effect associated with vertical competition and therefore yields the best selling 16 price for the customer; (ii) a decentralized supply chain sometimes performs better than a centralized one; and (iii) a risk-seeking developer may obtain a higher expected profit than does a risk-neutral developer. S5c: OR in Practice Chair and Organizer: Michal Massin Vladimir Lipetz Closing the Gap Between OR Models and Real Applications Optimization can help and provide productivity boost in many domains,however, its practical usage is pretty limited. In this presentation we focus on features and tools that help to make optimization usable by end users. The users are typically domain experts, but usually have no optimization and programming background. We focus on performance, usability, and maintainability aspects and how these aspects can change the underlying optimization models. Finally, we demonstrate the process of transferring scheduling models into reusable tools in two real-life examples. The first one is an integrated part of IBM tool (Maximo Asset Manager) for technicians scheduling during maintenance and emergency activities. The second example is a stand-alone enterprise level application for El Al staff scheduling Evgeny Shindin, Odellia Boni, Michael Masin Robust Optimization of System Design The data of real-world optimization problems are usually uncertain, that is especially true for early stages of system design. Data uncertainty can significantly affect the quality of the nominal solution. Robust Optimization (RO) methodology uses chance and robust constraints to generate a robust solution immunized against the effect of data uncertainty. RO methodology can be applied to any generic optimization problem where one can separate uncertain numerical data from the problem's structure. Since 2000, the RO area is witnessing a burst of research activity in both theory and applications. However, RO could lead to over-conservative requirements, resulting in typical-case bad solutions or even empty solution spaces. This drawback of the classical RO methodology can be overcome by distinguishing between real decision variables and so-called /state/ variables. While the first type should satisfy the chance or robust constraints and their value cannot depend on a specific realization of the uncertain data, the state variables are adjustable (i.e., their value can depend on the specific realization of the uncertain data), since most of the constraints defining state variables merely “calculate” their exact value, and hence are always satisfied. In this paper we summarize how adjustable RO approach can be applied to a general uncertain linear optimization problem. Then, using an allocation example we demonstrate how this approach can be integrated in the design optimization process and its impact on the optimal system design. David Amid, Ateret Anaby-Tavor Watson Tradeoff Analytics - Decision Making Made Accessible for Everyone The Watson Tradeoff Analytics service helps people optimize their decisions while striking a balance between multiple, often conflicting, objectives. The service can be used to help make complex decisions like what mortgage to take or which laptop to purchase. Tradeoff Analytics uses Pareto filtering techniques to identify the optimal alternatives across multiple criteria. It then uses various analytical and visual approaches to help the decision maker explore the pros and cons of their alternatives. Zilla Sinuany-Stern OR/MS in the Higher Education Much has been written on Operations Research (OR) and Management Science (MS) in many other areas such as health care and energy. I see the Higher Education (HE) industry/sector as a positive leading catalyst for economic development and change. We are witnessing the rise of tuition and the oversupply of college graduates - a new era of e-learning and new accreditation of online institutions, globalization of HE, and new outlook on learning outputs and proficiencies required for the workplace. The HE sector is changing fast and requires more efficiency, more agile and lean planning, and new strategies, and quality assurance. Namely, there is a need for more applications of OR/MS, and IE methodologies, and business approaches, which are already occurring in the HE sector. 17 The old type leaders of HE are not always equipped to foresee these processes. They need the help of our trade and tools in general and quantitative methods in particular to survive. As many of the OR/MS experts are members of HE institutions. Many of us wish to improve our institutions. Research students in our fields should be more encouraged to apply OR/MS and IE methodologies in HE. Areas in HE where OR can be used include: facility planning and scheduling, faculty outputs and compensation, budgeting, quality assurance, international comparisons, ranking universities, students choice of institution, students admission. OR methodologies used for HE are: optimization models, scheduling, forecasting, simulation, Data Envelopment Analysis, game theory, multi-criteria decision analysis, etc. We will present several examples: 1. Measuring the differentiability of faculty salaries in Israeli universities by rank, by institution, and by faculty outputs, using statistical measures 2. Budget allocation in HE institution using quadratic optimization model, and linear programming. 3. Measuring the efficiency of academic departments using Data Envelopment Analysis (DEA). S5d: Health Care Management Chair and Organizer: Amir Elalouf Illana Bendavid, Yariv N. Marmor, Boris Shnits Developing an Optimal Appointment Scheduling for Healthcare Systems with Non-flexible Uptake Time under Pre-determined Service Levels One of the critical steps in patient care path is diagnosis. The demand for advance imaging tests, such as CT, MRI and PET, increased dramatically in the past 15 years. Since imaging equipment remains relatively expensive, in order to fit the demand, the imaging resources must be managed effectively. In most healthcare systems, where examination length is uncertain (stochastic), the goal of the appointment scheduling need to balance between resource utilization and patient waiting times. In some imaging scans, such as PET, a radiopharmaceutical (radioactive substance) is injected to the patients in order to perform the diagnosis. In these systems, the time between the substance injection and the scan is non-flexible (for example, due to short half-life duration). This constraint makes the patient appointment scheduling more challenging, because, on the one hand, there is a predetermined time required between the injection of the radiopharmaceutical and the scan – the uptake time (time that it takes to the substance injected to be absorbed into the body), while on the other hand, if at the end of the expected uptake time the scanner is not available, the quality of the scan is jeopardized. Of course, the availability of the scanner is a consequence of appointments and durations of prior scans. Therefore, the aim of this work is to develop a method for determining a patient appointment scheduling in a system with non-flexible uptake time in order to minimize the end of day and increase resource utilization while keeping minimal pre-determined service levels. To this end, we consider the following setting: a given sequence of patients is to be scheduled on one scanner machine; the durations of scans are normally distributed with various expectations and variances; a minimal probability for each appointment to start on time is required (service level). In order to solve this stochastic problem, we formulate its equivalent deterministic problem, based on simulated data, as a mixed-integer linear programming. To overcome the dimensionality limitations, we also develop a simulation-based sequential model. We found that a constant slot per scan, as a benchmark, is inferior to our method both in achieving stable service level and reducing the end of day. ‫ מיכאל בנדרסקי‬,‫ישראל דויד‬ ‫מודל החלטה דינמי לקבלת כליה מן המת – בהתאם לפונקציית הכשל והתפלגות החיים של המועמד להשתלה‬ ‫ ידועה בעיית הפער החמור בין ביקוש‬.‫ספיקת כליות סופית‬-‫השתלת כלייה חיה מהווה את הפיתרון הטוב ביותר למצב של אי‬ .‫ דווקא על רקע זה חשוב לגבש מדיניות הקצאה אופטימלית‬.‫להיצע במערכת ההשתלות המנוהלת ע"י מקבל ההחלטות הציבורי‬ ‫ שכן מגיעים אליו היצעים‬,("‫ עומדת בעיית החלטה גם בפני "המועמד הבודד" )לצורך המחשה – זה שב"ראש התור‬,‫למעשה‬ .‫ עם חלוף הזמן חלה הרעה במצבו ועליו להיות פחות בררן‬.(‫ כתהליך בזמן )מעין בעיית מזכירה‬,‫בדרגות התאמה שונות‬ ‫ שהיא כמובן קלה יותר לפיתרון אנליטי מבעיית‬,‫במאמר זה אנו מסתכלים על הבעייה אכן מנקודת מבטו של המועמד הבודד‬ ‫ מנקודת הראות של מקבל ההחלטות הציבורי‬.‫ אנו אף מציעים בהתאם כלי תומך החלטה מבוסס אקסל‬.‫ההקצאה והעיתוי הכללית‬ ‫ וכמו כן ככלי מחקרי‬,‫יוריסטית לניהול תור ההשתלות המלא‬-‫כלי זה הוא בעל חשיבות כ"אבן לגו" בבניין מדיניות אופטימלית‬ ‫ התפתחה בשנים האחרונות‬,‫ במקביל‬.‫ לגנטיקה של האוכלוסיות המעורבות ועוד‬,‫ לעוצמת תהליך התרומה‬:‫לבדיקות רגישות שונות‬ 18 ‫ לפיה החולה נוטל חלק פעיל בהחלטות הנוגעות לו )ולפעמים שוכר לשם כך מומחה‬,"Patient Choice" ‫פרקטיקה רפואית של‬ .‫ מבוססת נתוני אמת ומודל אנליטי הסתברותי‬,‫ הכלי הנוכחי תומך בקבלת החלטות מושכלת שכזו‬.(‫חיצוני‬ ‫ מבוסס תיכנון‬,‫ ואת מודל ההחלטה המתמטי הבסיסי‬,‫נציג את הגורמים הפיסיולוגיים והסטטיסטיים הרלבנטים להצלחת השתלה‬ ‫ וכמובן את הפעלת‬,‫ נסקור את מקורות הנתונים ששימשו אותנו בדוגמאות ואשר מגולמים בחוברת העבודה של האקסל‬.‫דינמי‬ ‫ מרכיב חדשני מן המאמר האחרון בנושא עוסק במידול ההסתברותי הדינמי של הדטריורציה של‬.(‫גיליון האקסל עצמו )המימשק‬ ‫ דבר המאפשר גמישות‬,‫ משפחה זו מאופיינת בשני פרמטרים‬.‫ באמצעות משפחת התפלגויות החיים גאמה‬,(‫החולה )תחת דיאליזה‬ .‫בהתאמתה לנתונים סטטיסטיים לפי מאפיינים שונים ואוכלוסיות‬ :‫מבוסס על המאמרים‬ M. Bendersky and I. David, "The Full-Information Best-Choice Problem with Uniform or Gamma Horizons", submitted to Optimization. M. Bendersky and I. David, "Deciding Kidney-Offer Admissibility Dependent on Patients' Lifetime Failure Rate", submitted to The European Journal of Operations Research. Guy Wachtel, Amir Elalouf Using a "Floating Patients" Approach for Improving Patients-Flow in Hospitals and Emergency Departments Overcrowding in hospitals along with long length of stay, high arrival rates, budget constraints, and increasing demand for high service quality create challenges for the workflow and patient flow of hospital emergency departments (EDs). From a managerial perspective, overcrowding can cause substantial profit loss to the ED and the other departments. In order to prevent this profit loss, we assume that the hospital management determines a maximal (fixed or dynamic) value for patients’ length of stay and for crowding levels in the various departments, and that patients who cannot be evaluated in the ED in a timely fashion are redirected for treatment in other hospital departments. The latter approach (referred to as the "floating patient” method) has been practiced, for example, in Israel. This paper proposes two algorithmic approaches that are designed to enable ED decision makers (specifically, in the triage) to optimally schedule evaluations for patients who are waiting for treatment in the ED. The algorithms have been developed gradually and embedded in a simulation model. To build the algorithms, we first solve a problem in which the triage decision maker has full information on patients’ conditions and on how long their evaluations expect to take. We then extend this problem to incorporate uncertainty as in real life scenarios: The triage decision maker (physician) needs to carry out initial examinations to obtain information on the patient's situation and, at each point in time, decides whether to continue to examine patients or to stop the process (halting rule) and ”float” the remaining patients to other departments. Next, the physician determines the optimal schedule for the full ED evaluations of the examined patients. We embed the algorithms into simulation procedures and run simulations using empirical data collected from Bnei-Zion hospital in Israel. We checked the method of "floating patients" when applied for different rates of crowding in the ED and the other departments (i.e. the ED management can decide to use this method only in above 90% crowding in the ED). The simulation took into consideration also the crowding situation in the other departments in order to see the marginal effect of the "floating patients" method of the patients-flow in the hospital as a whole system. Implementation of the "floating patient" method is shown to reduce patients' length of stay, queues for beds in departments and the ED, and cumulative treatment time in the ED. These improvements reflect a better balance of work-rate and crowding between the ED and the other departments. 19 Monday, 11.5 M1a: Combinatorial Optimization 2 Chair and Organizer: Rafi Hassin Rafi Hassin, Uri Yovel Sequential Scheduling on Identical Machines We study a sequential version of the well-known KP-model: Each of $n$ agents has a job that needs to be processed on any of $m$ machines. Agents sequentially select a machine for processing their jobs. The goal of each agent is minimize the finish time of his machine. We study the corresponding sequential price of anarchy for m identical machines under arbitrary and LPT orders, and suggest insights into the case of two unrelated machines. Keywords: sequential price of anarchy, machine scheduling, congestion games, load balancing, subgameperfect equilibrium, makespan minimization. Tal Raviv, Uri Yovel, Tom Cherchy New Integer Programming Formulation of Routing Problems in Dense Networks A typical road network can be represented by a relatively sparse graph. However, in the arc based formulations that are currently in use for many vehicle routing problems the underlining graph is extended into a complete graph. Thus, one binary variable is defined for each pair of nodes. In this study, we introduce a new modeling approach that exploits the sparsity of the networks. In the proposed models the number of integer decision variables can be significantly reduced since the variables correspond only to direct links between nodes. On the other hand the assumptions that each node should be visited exactly once and each arc can be traversed at most once no longer hold. Our solution method thus starts by finding some Eulaerian subsets of the graph that span the required set of nodes and may include an arbitrary number of copies of each arc. The Eulerian circles in these sub-graphs are then reduced into a set of Hamiltonian circles to derive an exact optimal solution. A similar approach can be used to obtain approximated solutions where the accuracy of the solution is controlled by the number of unattractive arcs (respectively, integer decision variables) that are deleted from the network (respectively, the model). The effectiveness of our formulation when solved by a branch and cut algorithm is demonstrated by a numerical experiment. The talk will be concluded with some adaptations of the approach for classic vehicle routing problems. *We started this research project together with Uri short time before he got ill. Dorit S. Hochbaum, Asaf Levin Weighted Matching with Pair Restrictions The weighted matroid parity problems for the matching matroid and gammoids are among the very few cases for which the weighted matroid parity problem is polynomial time solvable. In this work we extend these problems to a general revenue function for each pair, and show that the resulting problem is still solvable in polynomial time via a standard weighted matching algorithm. We show that in many other directions, extending our results further is impossible (unless P=NP). One consequence of the new polynomial time algorithm is that it demonstrates, for the first time, that a prize-collecting assignment problem with ``pair restriction" is solved in polynomial time. The prize collecting assignment problem is a relaxation of the prize-collecting traveling salesman problem which requires, for any prescribed pair of nodes, either both nodes of the pair are both matched or none of them are. 20 Enrique Gerstl, Gur Mosheiov Single Machine Scheduling to Minimize Total Earliness-Tardiness with Unavailability Period We study several versions of a single-machine scheduling problem, where the machine is unavailable for processing for a pre-specified time period. In the basic problem, a common due-date for all the jobs is assumed, and the objective function is minimizing total earliness-tardiness. We consider first the setting that no idle times are allowed. We then extend the problem to general earliness and tardiness cost functions, to the case of job-dependent weights, and to the setting that idle times are allowed. All these problems are known to be NP-hard. We introduce in all cases efficient pseudo-polynomial dynamic programming algorithms. M1b: Queues and Stochastic Systems 1 Chair: Galit Yom-Tov Nitzan Carmeli, Haya Kapi, Avishai Mandelbaum Modeling and Analyzing IVR Systems, as a Special Case of Self-services Call centers play a very important role in today's economy, serving as the main customer contact channel in many different enterprises. Call centers are highly labor intensive. Typically, 60%-70% of the overall operating expenses of call centers are derived from agents’ employment costs. Reducing the number of agents handling calls, without degrading service level, is thus of interest and importance. Enabling customers self-service is one of the basic means for doing so, with Interactive Voice Response (IVR) systems being one of the main self-service channels. The goal of our research is to improve and enhance IVR systems, as a special case of self-service systems. To do so, we model and analyze customer flow within an IVR system. The model features were established and inspired by an Exploratory Data Analysis (EDA) of real IVR transactions in a call center of a large Israeli bank, based on more than one year of data, including millions of calls. An IVR system usually offers several services. Customers enter the IVR and then follow a series of menus in order to reach a desired service or services. We represent the IVR system as a rooted tree and model customer flow within it as a stochastic search. We model the search of group of customers, which is commonly characterized by its perceived rewards and costs, its success probabilities, its service time distribution, and its patience distribution. The goal of our search is to find the optimal path on the IVR tree, which will result in maximal expected discounted revenue for customers within each group. We show that, at each stage, an index can be assigned to each feasible option, and the optimal policy is to choose the option with the highest index at each stage. One of the main observations derived from the EDA was that some customers leave the IVR system without getting any relevant information. These customers may either leave the call center or join the agents queue (opt-out) to receive the desired service. In both cases, we say that these customers abandon the IVR service. When customers are self-served, finding whether their service was successful or not is not an easy task. The subject of identifying abandonments from self-service systems, such as IVR, is thus of interest and we are addressing this issue in our work. We also discovered that there is a learning process, which means that as customers gain more experience within the system, their response time is getting shorter. This fact was incorporated into our model. Our model enables the comparison between alternative IVR designs, both from the customer point of view and from the enterprise point of view, thus supplementing existing research from other fields such as Human-Factor-Engineering and Telecommunication Engineering. Although this research focuses on IVR systems, we believe that both the theoretical model and some of the methods presented in our EDA can be easily implemented to other self-service systems, which now become highly relevant, such as Internet websites. 21 Zhenghua Long, Nahum Shimkin, Jiheng Zhang Optimal Priority Control for Multiclass Many-server Queues with General Patience Distributions We consider the problem of server scheduling in an overloaded multiclass queueing system with multiple homogeneous servers and customer abandonment. In the case of exponential reneging, an indexed priority policy, called the cμ/θ rule, is known to be asymptotically optimal in the many-server heavy traffic regime, where the arrival rates and number of servers increase proportionally. For general patience distributions, we aim to find an asymptotically optimal control policy among the class of fixed priority rules. We first describe a fluid model, which is known to be the limit of the stochastic system under priority rules, and show its convergence to an equilibrium state in the case of exponential service. We then formulate an optimization problem in terms of these equilibrium states, which leads to a nonlinear program of a certain type which we term as the Fractional 0-1 Knapsack Problem. A dynamic programming algorithm is developed to efficiently solve this new type of knapsack-like problem Galit B. Yom-Tov, Jing Dong, Elad Yom-Tov The Impact of Delay Announcements on Hospital Network Coordination and Waiting Times We investigate the impact of delay announcements on the coordination within hospital networks using a combination of empirical observations and numerical experiments. We show that patients take delay information into account when choosing emergency service providers and that such information can help increase coordination in the network, leading to improvements in performance of the network, as measured by Emergency Department wait times. Our numerical results indicate that the level of coordination that can be achieved is limited by the patients’ sensitivity to waiting, the load of the system, the heterogeneity among hospitals, and, importantly, the method hospital use to estimate delays. We show that delay estimators that are based on historical average may cause oscillation in the system and lead to higher average waiting times when patients are sensitive to delay. We provide empirical evidence that suggests that such oscillations occurs in hospital networks in the US. Eitan Bachmat Geometric Queueing Theory Queueing theory and PERT/CPM project management are two of the pillars of OR. In the talk we will present an approximate analogue of the Pollaczek-Khinchine formula from queueing theory in the theory of PERT/CPM, when the precedence relation have a geometric interpretation. This provides a vast generalization of the context to which formulas of the Pollaczek-Khinchine type can be applied and leads to a wealth of interesting new examples and problems. It also shows that PERT/CPM problems with a geometric interpretation have an approximate modular symmetry. Our motivating example for the theory is an analysis of the following airplane boarding policy which was recently implemented by a few airlines: Passengers with no luggage for the overhead bins have boarding priority over passengers who do. We will analyze such policies, show why they are not very efficient and how they can be made more efficient. The key is to "cloak" slow passengers by constructing, thin focal lenses in space-time geometry. M1: Game Theory 2 Chair and Organizer: Gail Gilboa Freedman Shiran Rachmilevitch Egalitarian-Utilitarian Bounds in Nash's Bargaining Problem For every 2-person bargaining problem, the Nash bargaining solution selects a point that is “between” the relative (or normalized) utilitarian point and the relative egalitarian (i.e., Kalai-Smorodinsky) point. Also, it is “between” the (non-normalized) utilitarian and egalitarian points. I improve these bounds. I also derive a new characterization of the Nash solution which combines a bounds-property together with strong individual rationality and an axiom which is new to Nash's bargaining model, the sandwich axiom. The sandwich axiom is a weakening of Nash's IIA. 22 Itai Arieli Payoff Externalities and Social Learning We consider a social learning model with payoff externalities in which one of two stage games is chosen at random and then played repeatedly by a different group of agents in every period. The current ``generation'' of every period is informed of the history of actions chosen by the preceding generations and receives conditionally independent private signals about the realized game. We show that with probability one, the play converges to the set of equilibria of an appropriate convex combination of the two underlying stage games. We identify a range of private signals, as a function of the stage games, for which asymptotic learning holds. We show that in some cases, unlike the classical model with no payoff externalities, asymptotic learning holds for a wide range of bounded private signals. Gail Gilboa Freedman, Rann Smorodinsky Axiomatization of Privacy In many cases, conveying data of the individual agents is useful for achieving a particular objective. For example, the participating of individuals in a clinical data base may be useful for clinical research, while this data is sensitive from the individuals' perspectives. Thus, an inherent trade-off between effectiveness and privacy prevails. How much privacy is lost during a process? Can two systems be rigorously compared on the privacy loss aspect? Can one propose a benchmark for privacy loss, possibly to be adopted as a standard? The absence of unequivocal answers to those questions is conspicuous. Especially, since privacy-enhancing technologies are intensively developed without a rigorous method for approximating their necessity nor their marginal contribution. A remarkable example for a privacy benchmark for handling statistical databases is the notion of differential-privacy. In our study, we formalize the conceptual notion of privacy, and suggest an alternative measure. We view the privacy-preserving problem as a function, form the set of component-level-inputs into the set of possible-outcomes/consequences. The inputs and outputs are often referred to as `secrets’ and `signals’, respectively. By construction, each outcome is correlated with the individuals' inputs. As a result, it jeopardizes privacy to some level. We aim to quantify this level, formally. Our measure is inspired by the f-divergence, which is an information-theoretic quantity, associated with a pair of probability distributions. We study a set of necessary and sufficient behavioral axioms which uniquely define our measure and so we show that it is a natural construction.The applicability of our research is for having a rigorous methodology for prioritizing systems by the levels of preserving their privacy. Gadi Fibich, Arieh Gavious Revenue Equivalence of Large Asymmetric Auctions Using asymptotic analysis, we calculate the seller's expected revenue in large asymmetric firstprice and second-price auctions, as well as in optimal auctions, with risk-neutral players. These calculations show that the revenue difference between asymmetric first-price and second-price auctions scales as ε2/n3, where n is the number of players and ε is the level of asymmetry (heterogeneity) among the cumulative distribution functions of players' valuations. This scaling law explains previous numerical findings that the revenue difference between first-price and second-price auctions is extremely small even with as few as n=6 bidders, and shows that bidders' asymmetry has a negligible effect on revenue ranking of large auctions. Furthermore, our asymptotic calculations show that the revenue differences between asymmetric first- or secondprices auctions and the optimal mechanism also scale as ε2/n3. Hence, asymmetric first-price and second-price are asymptotically optimal. 23 ‫‪M1: OR Applications in the Israeli Police Chair and Organizer: Mali Sher‬‬ ‫חיננית אפרתי‪ ,‬תהילה הירש‪ ,‬אירית נוביק‪ ,‬מלי שר‪.‬‬ ‫ייעול עבודת הסיירים ביחידת המקומות הקדושים של משטרת‪-‬ישראל‬ ‫משטרת ישראל מופקדת על אכיפת החוק‪ ,‬שמירת הסדר הציבורי וביטחון הפנים במדינת ישראל‪ .‬פרויקט זה התמקד בייעול עבודת‬ ‫הסיירים ביחידת המקומות הקדושים‪ ,‬במרחב דוד במחוז ירושלים‪ .‬יחידת המקומות הקדושים אחראית‪ ,‬בין היתר‪ ,‬על אבטחת הר‬ ‫הבית ושמירה על השקט בתחומו‪ .‬העבודה בהר הבית הינה קשה‪ ,24/7 ,‬וכוללת זקיפות בשערי הכניסה למתחם‪ ,‬לבוש הגנה מתאים‬ ‫ונשיאת נשק ארוך לאורך כל שעות המשמרת‪ .‬העבודה הינה מורכבת ודרוכה‪ ,‬כהגנה על אזור רגיש ונפיץ‪ .‬כתוצאה מהעבודה‬ ‫המורכבת והקשה מחד והקושי באיוש תפקידים אלו מאידך הוחלט על תגמול מיוחד לשוטרים המופקדים על משימה זו וכן על‬ ‫הפעלת שיטת משמרות ייחודית‪ .‬האיוש כיום מתבסס בעיקר על שוטרים המתגוררים בצפון הארץ‪ .‬עובדה זו מקשה מאוד על שיבוץ‬ ‫המשמרות ביחידה‪ .‬כך נוצר מצב ייחודי ביחידה בו משמרת רגילה של העובדים הרחוקים )מהצפון( הינה על בסיס ‪ 24‬שעות‬ ‫למשמרת אחת ולאחריה ‪ 48‬שעות מנוחה‪ .‬יש לציין כי ביחידה משרתים מעט סיירים המתגוררים באזור ירושלים והמרכז‪ ,‬המועסקים‬ ‫במשמרות רגילות‪ .‬הפרויקט בחן מספר היבטים‪ (1) :‬תכנון המשמרות השבועי ושיבוץ השוטרים למשמרות‪ (2) ,‬בחינת העומס על‬ ‫השוטרים מבחינת סך שעות עבודת השוטרים ביחידה ו‪ (3) -‬בחינת מאפיינים אישיים של שוטרים‪ .‬בכך לתת למשטרה מידע מקדים‬ ‫על איכות עבודת השוטר הצפויה לצורך קבלת החלטה מושכלת בנוגע לקבלתו לעבודה‪ .‬התוצאות שהתקבלו הנן )‪ (1‬מודל תכנון‬ ‫לינארי בשלמים המשבץ מיטבית את השוטרים למשמרות עפ"י אילוצי "קו אדום" למשמרת‪ ,‬מגורי השוטרים ונהלי העבודה‬ ‫במשטרת‪-‬ישראל‪ .‬פתרון המודל הביא לשיפור של ‪ 70%‬מהמצב כיום‪ (2) .‬שעות עבודת השוטר ביחידה נבחנו מול משטרות אחרות‬ ‫בעולם ונמצא כי שוטרי היחידה עובדים מספר רב של שעות‪ ,‬בפער של עד כ‪ 20 -‬שעות שבועיות ממשטרות אחרות‪ .‬לפיכך‪ ,‬הומלץ‬ ‫למשטרה לבחון מחדש את שעות עבודת השוטרים ביחידה‪ (3) .‬נמצא כי הנתונים האישיים המשפיעים על איכות עבודת השוטר הם‬ ‫גיל‪ ,‬מצב משפחתי וותק במשטרה‪.‬‬ ‫שירלי ארקוסין‪ ,‬אורטל סעידיאן‪ ,‬איריס פורמה‪ ,‬מלי שר‪.‬‬ ‫השמת עובדים אופטימאלית‬ ‫פרויקט זה מבוצע עבור אגף משאבי אנוש של המשטרה בשיתוף עם אגף התנועה הארצי במטרה לבצע השמה אופטימלית של‬ ‫עובדים‪ .‬אגף משאבי אנוש של משטרת ישראל פועל להעצמת המשאב האנושי ‪ -‬איכותו‪ ,‬מקצועיותו ורווחתו‪ ,‬תוך התאמתו ליעדים‬ ‫ולצרכים של המשטרה‪ .‬משטרת ישראל שמה לעצמה כמטרה לפעול להעצמת השוטרים והמשרתים בארגון והגברת הזדהותם עם‬ ‫הארגון‪ .‬כיום תהליך שיבוץ השוטרים במשטרה נעשה ע"י אנשים – שוטרים באגף משאבי אנוש של המשטרה שאמונים על שיבוץ‬ ‫עובדים לתפקידים השונים ע"פ המשרות הפנויות‪ .‬אופן השיבוץ הינו מלאכת מחשבת אנושית שמתייחסת להתניות‪ ,‬הכשרות‪ ,‬נתונים‬ ‫ורצונות ע"פ שיקול דעת בריא‪ .‬מטרת הפרויקט הינה ייעול ושיפור תהליך שיבוץ השוטרים על ידי מידול הבעיה ומציאת פתרון‬ ‫אופטימלי בהתאם לפרמטרים המוגדרים ותוך התחשבות בצרכי הארגון‪ ,‬רצון המועמד והתאמתו לתפקיד‪ .‬במסגרת הפרויקט בוצע‬ ‫ניתוח של התפקידים המוצעים ע"י המשטרה‪ ,‬תוך זיהוי של התפקידים בעלי אופי דומה שיאפשרו העברה של שוטרים מתפקיד‬ ‫לתפקיד‪ .‬בהתאם לכך נבנו שני מודלים לשיבוץ אופטימלי של העובדים לתפקידים בין תחנות שונות‪ :‬האחד ‪ -‬מודל תכנון ליניארי‬ ‫ הפועל לשיבוץ השוטר לתפקיד תוך צמצום המרחק בין מקום מגורי העובד לתחנה בה הוא משרת וצמצום הפער בין התקן הארצי‬‫של התפקיד )מוגדר ע"י המשטרה( לבין תקן המצבה )מספר השוטרים המשובצים לתפקיד בפועל(‪ ,‬נכתב בתוכנת ‪IBM ILOG‬‬ ‫‪ .CPLEX‬והשני ‪ -‬מודל שידוך יציב ‪ -‬הפועל לשיבוץ תוך התייחסות לרצון‪/‬העדפת העובד )לפי שרירות ליבו( ורצון‪/‬העדפת‬ ‫המשטרה )שמיוצגת ע"י לוגיקה לצמצום המרחקים בין תחנת המשטרה למקום מגורי העובד(‪ .‬המודל מיישם את אלגוריתם גייל‪-‬‬ ‫שפלי לשידוך יציב )‪ ,(Matching Problem‬נכתב בתוכנת ‪ .MATLAB‬עיקרי הפתרון המוצע כוללים ניתוח של הנתונים‬ ‫שהתקבלו ממשטרת ישראל‪ ,‬שימוש בנתונים אלו לצורך הפעלת המודלים‪ ,‬וניתוח פלטי המודלים בכדי להעריך יעילות ואפקטיביות‬ ‫השיבוץ החדש בהשוואה לשיבוץ שמתבצע כיום במשטרה מה שתורם להתייעלות הארגון‪.‬‬ ‫אלי אורבך‪ ,‬ינון סבתו‪ ,‬מיכאל דרייפוס‪ ,‬מלי שר‪.‬‬ ‫ייעול פריסת מצלמות האכיפה במשטרת‪-‬ישראל‬ ‫כחלק מהחלטות הממשלה למאבק בתאונות הדרכים‪ ,‬תוקצב פרויקט א‪) 3‬אכיפה אלקטרונית אוטומטית( במשטרת ישראל‪ ,‬באגף‬ ‫התנועה‪ .‬פרויקט זה החל לפעול מבצעית בשנת ‪ 2012‬וכולל כיום כ‪ 150 -‬עמדות לאכיפת מהירות מעל המותר ומעבר צומת באור‬ ‫אדום‪ .‬העמדות ממוקמות בתחום העירוני והבינעירוני‪ ,‬בצמתים ובקטעי הדרך‪ .‬כיום מספר המצלמות קטן ממספר העמדות והמצלמות‬ ‫מנוידות בין העמדות באופן אקראי‪ .‬מחקר זה מטרתו לפתח מודל לשיבוץ מצלמות האכיפה במיקום ובזמן המיטביים לצמצום מרבי‬ ‫של תאונות הדרכים‪ .‬המודל מתבסס על נתוני תאונות הדרכים בכל רחבי הארץ בשנים האחרונות‪ .‬בשלב ראשון של המחקר בוצע‬ ‫ניתוח נתוני כל תאונות הדרכים ב ‪ 5‬שנים האחרונות ומיקומם ביחס למיקום עמדות א‪ .3‬בשלב השני של המחקר בוצע חיזוי מספר‪,‬‬ ‫חומרה ומיקום של תאונות הדרכים לתקופת התכנון‪ .‬בשלב השלישי‪ ,‬בהינתן התחזית ונתינת "ציונים" לכל תאונה עפ"י חומרתה‪,‬‬ ‫נקבע מיקום המצלמות האופטימלי‪ ,‬ע"פ המודל תוך התחשבות במספר אילוצים‪.‬‬ ‫‪24‬‬ Mali Sher, Nicole Adler, Shalom Hakkert. A Traffic Enforcement Camera Operational Model. Police enforcement resources impact safety levels by changing driver behavior. The existence of an enforcement camera reduces the number and severity of offences which in turn reduces the number of road accidents and serious injuries. After measuring the impact of a set of cameras over the last year in Israel, we note that traffic parameters were decreased on average. Based on the enforcement camera recordings, tickets are issued for red-light and speed offences. The owner of the vehicle is either (i) sent a fine, (ii) a fine with points or (ii) a court summons, according to the severity of the offence and the available police/court resources. The time halo effect causes a camera’s productivity to be reduced over time, once the tickets issued have been served, and it is therefore worthwhile moving the camera after a period of time. This research investigates the traffic police enforcement policy with respect to the use of semi-fixed cameras on a road network. In the first stage, a public committee choose the location of a set of fixed camera poles. Subsequently, the police force decided on a monthly basis which poles will contain active cameras. In the final stage, the decision is made as to the most appropriate operational policy such that a specific speed threshold determines the issuing of tickets. An integer linear program model was developed to determine the lowest enforcement speed per camera site over the planning period such that ticket issuances are maximized according to an analysis of the most important traffic parameter (average speed, variance etc.) at each site. The constraints are budget constraints as well as limitations on the processing capabilities of the police back office and the judicial system. There are three administrative level restrictions: the police back-office producing the tickets, a separate unit handling requests from the car owners and the courts that are limited by the number of judges. Finally, a municipal limitation restricts the number of tickets issued within a specific geographic region. The results of a year-long study show that the number of offences was reduced by up to 50% wherever cameras operated, highlighting the importance of this tool in the traffic enforcement field. In addition, we found that the time halo effect exists for approximately two months which will impact future decisions and the frequency with which cameras will be relocated. M4a: Optimization and Stochastics, Prize Winners Session Chair:Moshe Haviv Amir Beck, Nadav Hallak: Uriel Rothblum Award for 2015 On the Minimization Over Sparse Symmetric Sets: Projections, Optimality Conditions and Algorithms In this paper we consider the problem of minimizing a general continuously differentiable function over symmetric sets under sparsity constraints. These type of problems are generally hard to solve as the sparsity constraint induces a combinatorial constraint into the problem, rendering the feasible set to be nonconvex. We begin with a study of the properties of the orthogonal projection operator onto sparse symmetric sets. Based on this study, we derive efficient methods for computing sparse projections under various symmetry assumptions. We then introduce and study three types of optimality conditions: basic feasibility, L-stationarity and coordinate-wise optimality. A hierarchy between the optimality conditions is established by using the results derived on the orthogonal projection operator. Methods for generating points satisfying the various optimality conditions are presented, analyzed, and finally tested on specific applications. Yair Shaki (joint work with Josh Reed): Uriel Rothblum Award for 2015 A Fair Policy for the Servers in the G/GI/N Queue We consider the G/GI/N queue with multiple server pools, each possessing a pool-specific service time distribution. We then consider the class of non-idling routing policies referred to as u-greedy policies. These policies route incoming customers to the server pool with the longest weighted cumulative idle time. Our first set of results demonstrate that asymptotically in the Halfin-Whitt regime and under any u-greedy policy, the diffusion scaled cumulative idle time processes of each of the server pools are held in fixed proportion to one another. We next move on to providing a heavy-traffic limit theorem for the process 25 keeping tracking of the total number of customers in the system. Our limit may be characterized as the solution to a stochastic convolution equation. Liron Ravner: Abraham Mehrez Award for 2015 Equilibrium Arrival Times to a Queue with Order Penalties Suppose customers need to choose when to arrive to a congested queue with some desired service at the end, provided by a single server that operates only during a certain time interval. We study a model where the customers incur not only congestion (waiting) costs but also penalties for their index of arrival. Arriving before other customers is desirable when the value of service decreases with every admitted customer. This may be the case for example when arriving at a concert or a bus with unmarked seats or going to lunch in a busy cafeteria. We provide game theoretic analysis of such queueing systems with a given number of customers, specifically we characterize the arrival process which constitutes a symmetric Nash equilibrium. Arik Sadeh Choosing Binary Factors Affecting Purchasing in e-Commerce Purchase intention of potential buyers in e-commerce may be affected by numerous factors. The business and ethical conduct of a given e-store has an important role on purchase intention. In this study, binary factors are used to describe the e-store characteristics. Naturally, not all possible combinations of k factors (2k) can be included in one questionnaire. An algorithm was developed to help choosing relevant combinations of level of factors to be included in questionnaires. In this study, success is defined as a potential consumer intends to purchase a given product. Having results from the survey, a second algorithm is used to determine what values of factors that are included in a combination would lead to better success rate. This suggested mechanism can be applied in various applications in engineering, economics and management. M4: Scheduling Chair and Organizer: Dvir Shabtay Tal Grinshpoun, Elad Shufan, Hagai Ilani Public Transportation with Flexible Timetable In a regular bus service, every bus line has a given route and a published timetable. Passengers who know in advance the stations along the bus's route and the expected schedule plan their ride correspondingly. Another model of public transportation is the DARP (Dial A Ride Problem) model where, in opposition, the passengers' requests for traveling are known in advance and the bus's route and schedule are planned accordingly. We hereby present a transportation model, which is a mix of the two mentioned models. The route of the bus is known in advance but the timetable is set according to the passengers' requests. For a given operation cost, the aim is to maximize user satisfaction, by minimizing the sum of passengers' waiting times. We introduce algorithms for solving two variants of the fixed route DARP -- one for a fleet of infinite capacity vehicles, and one for the more general case of vehicles with heterogeneous capacities. Contrary to general DARP, which is NP-Hard, the presented algorithms are polynomial in the number of ride requests. Enrique Gerstl, Gur Mosheiov Scheduling with Two Competing Agents to Minimize Total Weighted Earliness We study a single machine scheduling problem with two competing agents and earliness measures. Given a common deadline for all the jobs of both agents, the objective function is minimizing the total weighted earliness of the first agent, subject to an upper bound on the maximum earliness of the jobs of the second agent. This problem was recently proved to be NP-hard, leaving the question of the complexity class open. We introduce a pseudo-polynomial dynamic programming algorithm, implying that the problem is NP-hard in the ordinary sense. An extensive numerical study indicates that the dynamic programming is very effective for solving medium size instances. We also propose an efficient heuristic, which is shown numerically to produce very close-to-optimal schedules. The dynamic programming algorithm is extended 26 to any (given) number of agents, proving NP-hardness in the ordinary sense of the general multi-agent setting. Finally, we study the inverse problem of minimizing the maximum earliness of one agent subject to an upper bound on the maximum weighted earliness of the second agent. We introduce a pseudopolynomial dynamic programming algorithm, a simple greedy-type heuristic and a lower bound. Our numerical tests verify that the heuristic produces very small optimality gaps. Baruch Mor, Gur Mosheiov Minimizing Maximum Earliness and Minimizing the Number of Early Jobs on a Proportionate Flowshop A proportionate flowshop is a special case of the classical flowshop, where the job processing times are machine-independent. Most classical scheduling objective functions have been studied in the context of a proportionate flowshop. In most cases, the solution was shown to be identical to that of the single machine version. We introduce two rare cases where the extension to a proportionate flowshop leads to different solutions. Specifically, we study the problems of minimizing maximum earliness and minimizing the number of early jobs. We show that the problems remain polynomially solvable and introduce algorithms that guarantee an optimal solution in O(n ) and O(n ) time, respectively, where n is the number of jobs. Dvir Shabtay, Shlomo Karhi An Asymptotically Optimal Online Algorithm to Minimize the Total Completion Time on Two Multipurpose Machines with Unit Processing Times In the majority of works on online scheduling on multipurpose machines the objective is to minimize the makespan. We, in contrast, consider the objective of minimizing the total completion time. For this purpose, we analyze an online-list scheduling problem of n jobs with unit processing times on a set of two machines working in parallel. Each job belongs to one of two sets of job types. Jobs belonging to the first set can be processed on any of the two machines while jobs belonging to the second set can only be processed on the second machine. We present an online algorithm with a competitive ratio of ρ(LB) + O , where ρ(LB) is a lower bound on the competitive ratio of any online algorithm and is equal to 1 + where α = + 116 − 6√78 asymptotically optimal. / + √ × / √ , / ≈ 1.918. This result implies that our online algorithm is M4: Optimization Chair: Gideon Weiss Saleh Soltan, Mihalis Yannakakis, Gil Zussman. Joint Cyber and Physical Attacks on Power Grids: Graph Theoretical Approaches for Information Recovery Recent events demonstrated the vulnerability of power grids to cyber and physical attacks. Therefore, we focus on joint cyber and physical attacks and develop methods to retrieve the grid state information following such an attack. We consider a model in which an adversary attacks a zone by physically disconnecting some of its power lines and blocking the information flow from the zone to the grids' control center. We use tools from linear algebra and graph theory and leverage the properties of the power flow DC approximation to develop methods for information recovery. Using information observed outside the attacked zone, these methods recover information about the disconnected lines and the phase angles at the buses. We identify sufficient conditions on the zone structure and constraints on the attack characteristics such that these methods can recover the information. We also show that it is NP-hard to find an approximate solution to the problem of partitioning the power grid into the minimum number of attack-resilient zones. However, since power grids can often be represented by planar graphs, we develop a constant approximation partitioning algorithm for these graphs. Finally, we numerically study the relationships between the grid's resilience and its structural properties, and demonstrate the partitioning algorithm on real power grids. The results can provide insights into the design of a secure control network for the smart grid. 27 Renata Poznanski, Refael Hassin. Optimal Multi-Period Network Flows with Coupling Constraints Suppose we have two networks defined on duplicates of the same directed graph, with given source and sink nodes, but with different edge capacities. Our problem is to compute feasible flows such that the sum of flows is maximized subject to coupling constraints that force identical flows on duplicate copies of the same edge for a subset of edges. When the subset consists of a single edge, we prove an existence of an integral optimal solution and provide an efficient algorithm. For multiple edges we prove that there exists an integral solution for all possible capacity functions if and only if the graph is series parallel. We also consider variations to other minimum cost flow problems. Rafiq Mansour, Yair Censor. New Douglas-Rachford Algorithmic Structures and Their Convergence Analyses. We study new algorithmic structures for the Douglas-Rachford (DR) algorithm to solve convex feasibility problems. For a finite family of closed convex sets in a Hilbert space, with nonempty intersection, the convex feasibility problem (CFP) is to find an element in the nonempty intersection of all the sets. There are many algorithms in the literature for solving CFPs, in particular, two algorithmic structures that encompass many specific feasibility-seeking algorithms are the String-Averaging Projections (SAP) method and the Block-Iterative Projection (BIP) method. We do two things: (i) create new algorithmic structures with the 2-sets-DR algorithmic operator, and (ii) define and study an “m -sets-DR operator”. First we employ the two-sets-DR algorithmic operator and embed it in the SAP and BIP algorithmic structures. In doing so we obtain two new families of DR algorithms, of which the two-sets-DR original algorithm and the recent cyclic-DR algorithm are special cases. Finally, we propose and investigate a generalization of the DR algorithmic operator itself. We propose to allow the algorithmic operator to perform a finite number of consecutive reflections into the sets and only then take the midpoint between the current iterate and the end-point of the consecutive reflections as the next iterate. We show how this “m -sets-DR operator” works algorithmically. We study the convergence of all three algorithmic schemes by using properties of strongly quasi-nonexpansive operators and firmly nonexpansive operators. Evgeny Shindin, Gideon Weiss A simplex-type Algorithm for Continuous Linear Programming We consider continuous linear programs over a continuous finite time horizon T, with a constant coefficient matrix, linear right hand side functions and linear cost coefficient functions, where we search for optimal solutions in the space of measures or of functions of bounded variation. These models generalize the separated continuous linear programming models and their various duals, as formulated in the past by Anderson, by Pullan, and by Weiss. In previous papers we have shown that these problems possess optimal strongly dual solutions. We also have presented a detailed escription of optimal solutions and have defined a combinatorial analogue to basic solutions of standard LP. In this paper we present an algorithm which solves this class of problems in a finite bounded number of steps, using an analogue of the simplex method, in the space of measures. M4 Queues and Stochastic Systems 2 Chair and Organizer: Yoav Kerner Yoav Kerner, Opher Baron Queueing Model for Safety Stock Inventory Model with Perishable Items and General Distribution We consider and inventory model in which the supply always regenerates the inventory to a given level. The lead time of the supply is random and hence an order is placed whenever the inventory goes below a predefined level. In addition, the inventory has a perish time, independent on everything else. We analyze the steady state distribution of the inventory, the queue, and the remaining lead time for the case where the perish times and/or the lead times follow a general distribution. 28 Ruth Sagron, Gad Rabinowitz, Israel Tirkel Hybrid Simulation-Regression Approximation for Tandem Queues with Downtime Events In this work, we introduce the Hybrid Simulation-Regression method for approximating the class-departure variability in tandem queues with downtimes. Existing decomposition methods reach partial success, due to the non-renewal process caused by the downtime events. Analytic approximations lack accuracy, and one that combines simulation tools as well, requires very high computation efforts. The proposed hybrid method paves a new approach for both reducing efforts and improving accuracy, by integrating existing decomposition methods with two variability functions. One depends on downstream traffic intensity for the within-class effect, and another on a departure from a single queue and downstream queue's waiting-time for the between-class effect. This method enables modeling different policies of downtimes (e.g. FCFS, Priority). Numerical experiments demonstrate relative errors about four times smaller than existing analytic procedures, and two times smaller than another method that combines analytic and simulation tools. Yonit Barron Clearing Control Policies for MAP Inventory Process with Partially Satisfied Demand We consider a production/clearing process in a random environment where a single machine produces a certain product into a buffer continuously. The demands arrive according to a Markov Additive Process (MAP) governed by a continuous-time Markov chain, and their sizes are independent and have phase-type distributions depending on the type of arrival. Negative inventory is not allowed, thus, the demand is partially satisfied. The production process switches between predetermined rates which depend on the state of the environment. In addition, the system is totally cleared at stationary renewal times and staring anew at level zero immediately. Several clearing policies are considered; clearing at random times, clearing at crossings of a specified level and a combination of the above policies. We assume the total cost includes a fixed clearing cost and variable holding and lost demand costs. By applying the regenerative theory, we use tools from exit-time theorem for fluid process and martingales to obtain cost functionals under both the discounted and average criterion. Finally, illustrative examples and a comparative study are provided. Tal Avinadav, Tatyana Chernonog, Yael Lahav, Uriel Spiegel Dynamic Pricing and Promotion Expenditures in an EOQ Model of Perishable Items This study considers dynamic decisions of a retailer with regard to the selling price and promotion expenditures associated with a perishable item. We propose an EOQ model in which the retailer faces a general demand function that is separable into multiplicative components of selling price, items' age and promotion expenditure. We find analytical expressions for the optimal price and promotion trajectories; and we show that the former increases and the latter decreases in the items' age, and that both are independent of the cycle length. Moreover, we show that the selling price is independent of the promotion expenditure, but not vice versa. It is also proved that under these optimal trajectories, the profit rate is strictly pseudo-concave in the cycle length. A comparison between dynamic and stationary strategies is given. M5a: Transportation Chair and Organizer: Tal Raviv Mor Kaspi, Tal Raviv, Michal Tzur Regulating One-Way Vehicle Sharing Systems through Parking Reservation Policies One-way vehicle sharing systems allow users to rent vehicles in one of many unmanned stations scattered in the city, use them for a short ride and return them at any station. The demand processes for vehicles and parking spaces are typically unbalanced. Consequently, shortages in vehicles or parking spaces may occur in some of the stations along the day. We propose implementing passive regulations as means for redirecting the demand so as to improve the performance of the system. In particular, we focus on parking reservation policies. Under such policies, the users may be required, upon renting a vehicle, to reserve a parking space at their destination and these spaces are kept for them. We measure the performance of the vehicle sharing system in terms of the total excess travel time users spend due to shortages of vehicles or parking spaces. We formulate a Markovian model of the system and use it to compare two extreme policies: a complete 29 parking reservation (CPR) policy under which all users are required to reserve a parking space and a baseline policy entitled no-reservation (NR). Through this model, we prove that under realistic demand rates, the CPR policy outperforms the NR policy. We also devise mathematical programming based bounds on the total excess travel time under any passive regulation and in particular under any parking space reservation policy. These bounds are compared to the performance of: the above policies, several partial parking reservation policies and a utopian parking space overbooking policy. A detailed user behavior model for each policy is presented and a discrete event simulation is used to evaluate the performance of the system under various settings. The analysis of two case studies of real-world systems demonstrates that: (1) a significant improvement of what theoretically can be achieved is obtained by the CPR policy. (2) The performance of the proposed partial reservation policies monotonically improve as more reservations are required. (3) Parking overbooking is not likely to be beneficial. In conclusion, our results demonstrate the effectiveness of the simple CPR policy and suggest that parking space reservations should be used in practice, even if only a small share of the users are required to place reservations Sharon Datner, Tal Raviv, Michal Tzur Setting Inventory Levels in Bike-Sharing Networks Bike-sharing systems allow people to rent a bicycle at one of many automatic rental stations scattered around a city, use them for a short journey, and return them at any other station in that city. A crucial factor in the success of such a system is its ability to meet the fluctuating demand for both bicycles and vacant lockers at each station. Setting the inventory levels of each station is a complicated task, due to the nature of users' behavior. If bicycles are not available at the desired origin of a user's journey, the user may either abandon the system, possibly use other means of transportation, or she may look for available bicycles in a neighboring station. If, on the other hand, a locker is not available at the destination, the user is obliged to find a station with available space in order to return the bicycle to the system. In this study we introduce a method to determine the inventory levels, who considers the interaction described between neighboring stations in the bike sharing network. Using a simulation based guided local search, we set inventory levels that would improve the systems' quality of service. Hila Hindi-Ling, Hillel Bar-Gera, Arie Sachish The effect of a quayside cranes buffer on ships unloading process Container terminals are essential intermodal interfaces in the global transportation network. Efficient container handling at terminals is important for reducing transportation costs and keeping shipping schedules. The temporary storage of the inbound and outbound containers is one of the most important services at the container terminal. The storage area in the terminal is divided into the several blocks of containers. Each block consists of a number of side by side lanes. Each lane is formed by a row of container stacks. Every stack may hold up to 4–5 tiers of containers (one on top of the other). The fast storage and retrieval of containers at the blocks is essential for the economic performance of container terminals and also shipping companies. These issues affect directly the traffic of the handling equipment and consequently the dwell and turnaround time of vessels. In the unloading phase, containers are unloaded and transported from the vessel to the storage yard. The equipment involved generally includes quay cranes (QC) at berths, yard cranes at storage yards, and terminal trucks. The QCs are in charge of lifting and moving containers from the vessels to the trucks. A number of trucks travel in a dedicated closed loop to pick up containers at the berth and drop them at the storage yard. Each truck usually handles one container premises at a time. Once the container arrives at the yard, a yard crane lifts it and stores it inside the yard. Delays can occur if trucks are queued at the berth and/or the yard, depending on the number of available cranes and the arrival rate of the trucks. This presentation will examine the potential of a container buffer between the QC and the terminal trucks. We will present a quantitative analysis of the effect of buffer size, fleet size, and operation rate parameters on queues, delays, overall vessel dwell time, and total operation costs. 30 M5b: Water Management Chair: Amos Bick Amos Bick, Ioannis K. Kalavrouziotis, Gideon Oron Modeling Combined Technological, Environmental and Economic Considerations in Domestic Sludge Reuse via the Analytic Hierarchy Process (AHP) An optional method of using recycling and reuse efficiently of sludge is presented. Sludge can be originated from a number of sources, although the one obtained from wastewater is the most common one. It composes around 30% percent of the wastewater treated by activated sludge or other similar methods. It always creates problems related to the environmental. A multi-objective function is defined. It consists of treating the sludge by four alternatives: (i) disposal of the sludge untreated to the sea; (ii) incineration of sludge for energy generation; (iii) treatment of the sludge via anaerobic digestion, and; (iv) using the treated sludge as a soil amendment, primarily for agriculture. Comparisons of the treatment and disposal methods were made. Comparison is based on the Analytic Hierarchy Process (AHP) model and Total Order Ranking Methods (Absolute Reference, Dominance Functions, and Hasse Average Ranking). The comparison criteria included similarly also five criteria. These criteria included the cleanness of the environment (environmental criteria), the cost of treatment according to the final product, benefit to society in terms of energy generation, benefits to agriculture in terms of food production and public acceptance. There is no doubt that the solutions will be location dependent however, they demonstrate the options in each region. David Raz, Ariel Daliot A Generic Modeling Language for Water Supply Systems Optimization We look at modeling Water Supply Systems (WSS) systems for the purpose of optimizing energy costs. Energy costs are responsible for more than 90% of the operational costs of such systems and as such are the most important factor governing WSS operation. The major constraints for such an optimization are water volume constraints such as water supply and demand, tank minimum and maximum water levels etc. This is in contrast to Water Distribution Systems (WDS) which may also be governed by water pressure constraints. Existing modeling tools, such as the EPAnet software, are focused on WDS and as such are very complex, focusing on the physical properties of the system (pipes, valves etc.) rather than on water flow and electrical power consumption and costs. We propose a simple generic modeling language for WSS. Although simple and generic, in our experience it suffices for describing WSS in enough details for energy cost optimization. The language has the major benefit that it easily translates into an LP model for optimizing energy costs. We describe the language and a graphic tool for building WSS models. We demonstrate how real life constructs translate easily into the modeling language and into an LP model. We discuss how the model may be efficiently solved. We show how the language may be easily expanded to incorporate other constraints such as pressure and other operational constraints. We express our hope that such a modeling language may be used to share network models by interested researchers and enrich the OR community. Beni Lew, Olga Tarnapolski, Vladimir Yudachev, Amos Bick Membrane Treatment of Brackish Groundwater for Unrestricted Use for Irrigation and Sustainable Agricultural Production: Decision Analysis via The Hasse Diagram Technique (HDT) Field experiments are in progress for brackish groundwater upgrading for unrestricted use for irrigation and sustainable agricultural production at the Arava valley (Israel). The treatment system is based on implementing of potable water and two main treatment stages: Nanofiltration and Reverse-Osmosis membrane processes. The feed are subsequently applied for irrigation of pepper crops. The comparison criteria include pepper yield and pepper quality. Data analysis is based on Hasse Diagram Technique (HDT), that is the application of the partial order theory referring to the objects, and characterized by vector based quantities with an easy visualization of the obtained results. This study demonstrates the possibility and appropriateness of providing a systematical decision making framework with several characteristics: 31 (i) different technological performances can be evaluated using multiple attributes - both quantitative and qualitative - rather than profitability alone, (ii) the use of ratings makes it possible to evaluate the applicability of different options for the end user, (iii) the HDT is a useful tool with an easy visualization of the obtained results, and; (iv) the proposed approach forms the basis for a continuous process of planning and managing technology selection, so that the priorities of the technologies can easily be modified and updated. M5c: Strategic Behavior in Queues Chair and Organizer: Moshe Haviv Moshe Haviv, Binyamin Oz Self-Regulation of a Queue via Random Priorities We consider an unobservable M/M/1 queue where customers are homogeneous with respect to their reward (due to service completion) and with respect to their cost per unit of time of waiting. Left to themselves, it is well known that in equilibrium they will join the queue at a rate that is higher than it is socially optimal. Hence, regulation schemes, under which the resulting equilibrium joining rate coincides with the socially optimal one, should be considered. In this talk we suggest a classification of regulation schemes, based on a few desired properties, and use it to classify schemes from existing literature. To the best of our knowledge, there is no existing scheme that possesses all properties, and in this talk we suggest such one. This novel scheme is based on assigning random priority to each customer, prior to the decision whether or not to join. We also introduce variations of this regulation scheme as well as additional schemes based on randomization. Moshe Haviv, Liron Ravner Accumulating Priority Queue with Strategic Customers In an accumulating priority M/G/1 queue each customer is assigned a positive priority coefficient. This assignment can be either class dependent or a choice made by the customers themselves. The actual (accumulated) priority of a waiting customer is a linear function of his time since arrival whose slope coincides with his priority parameter. The service regime is such that upon service completion the next customer to be admitted is the one who has accumulated the most priority. We study a non-cooperative game where customers can purchase their own priorities. For the case of homogeneous customers with respect to their waiting cost parameter, we explicitly compute the unique pure strategy Nash equilibrium. We further show that this model can display both avoid the crowd and follow the crowd behaviour, for different levels of bidding. For a game with heterogeneous customer types that differ in their waiting costs, we characterize the Nash equilibrium as the solution to a set of polynomial equations and suggest using an iterated best response dynamics in order to compute it. We further consider a non-atomic version of this game where each class of customers can coordinate their bids. We construct a unique pure strategy Nash equilibrium for the resulting game. Nahum Shimkin What to (Truthfully) Tell Customers to Make Them Join a Queue We consider a service system, such as a queueing system, to which customers arrive sequentially. Upon arrival, each customer receives from the system manager some information about his or her expected quality of service (for example, the expected waiting time in the queue, based on the current queue size which is unobservable by the arriving customer), and may then decide whether to balk or join the system. The manager is committed to truth telling, but can provide partial information (e.g., a range of possible waiting times, which must include the true one). We ask what information should be provided to arriving customers to maximize the throughput, namely the fraction of customers that choose to join. This question is formulated as an optimization problem, in terms of the service demand curve and the probability distribution of the service quality. Concrete solutions are derived, whose form depends on the convexity or concavity properties of the demand curve. 32 Name List of Participants: Speakers and Chairs* Email Affiliation Amid David Anily Shoshana Arieli Itai Atar Rami Avinadav Tal Bachmat Eitan Barron Yonit Bendavid Illana Ben-Tal Aharon Bick Amos Buchbinder Niv Carmeli Nitzan Chanukov Gabi Chernonog Tatyana Cohen Yaarit.M. Cohensius Gal Datner Sharon David Israel Dreyfuss Michael Eisenhandler Ohad Elalouf Amir Eliazar Iddo Gavious Arieh Gerchak Yigal Gerstl Enrique Gilboa Freedman Gail Goldberg Noam Halman Nir Hanany Eran Hassin Rafi Haviv Moshe Herer Yale Hindi-Ling Hila Ilani Hagai Kaspi Haya Kaspi Mor Kerner Yoav Levin Asaf Lipetz Vladimir Long Zhenghua Mansour Yishay IBM Research [email protected] Tel Aviv Univ. [email protected] Technion [email protected] Technion [email protected] Bar-Ilan Univ. [email protected] Ben Gurion Univ. [email protected] Ariel Univ. [email protected] Ort Braude [email protected] Technion [email protected] Bick & Assoc. [email protected] Tel Aviv Univ. [email protected], Technion Bar-Ilan Univ. [email protected] Bar Ilan Univ. [email protected] Technion [email protected] Ben Gurion Univ. [email protected] Tel Aviv Univ. [email protected] Ben Gurion Univ. [email protected] Jerusalem College of Technology [email protected] Tel Aviv Univ. [email protected] Bar-Ilan Univ. [email protected] Tel Aviv Univ. [email protected] Ben Gurion Univ. [email protected] Tel Aviv Univ [email protected] Hebrew Univ. [email protected] Technion [email protected] Bar-Ilan Univ. [email protected] Hebrew Univ. [email protected] Tel Aviv Univ [email protected] Tel Aviv Univ. [email protected] Hebrew Univ. [email protected] Technion [email protected] Ben Gurion Univ. [email protected] Shamoon College. [email protected] Technion [email protected] Tel Aviv Univ. [email protected] Ben Gurion Univ. [email protected] Technion IBM Research [email protected] Technion [email protected] Tel Aviv Univ. 33 Session S5c S2b M1c S4 M4d M1b M4d S5d M3, S1* M5b, M5b* S2d M1b S2a S5b S2d S2b M5a S5d S2d S2c S2a, S5d* M2 M1c S5b M4b M1c, S4* S2b S5a S4, S2a* M1a, M1a* M5c, M4a* S5d M5a M4b S4* M5a M4d, M4d* M1a S5c M1b S3 Mansour Rafiq Megiddo Nimrod Masin Michael Miller Eliaz Mor Baruch Mosheiov Gur Naor Seffi Noham Reut Oz Binyamin Pauwels Edouard Perel Nir Perlman Yael Poznanski Renata Rachmilevitch Shiran Raviv Tal Ravner Liron Raz David Sabach Shoham Sadeh Arik Sagron Ruth Shabtay Dvir Sher Mali [email protected] Haifa Univ. [email protected] IBM Almaden [email protected] IBM Research Shimkin Nahum Shindin Evgeny Shtern Shimrit Sinuany-Stern Zilla Smirnov Dina Tzur Michal Wachtel Guy Weiss Gideon Yechiali Uri Yom-Tov Galit Zeevi Assaf Zussman Gil ‫אורבך אלי‬ ‫אפרתי חיננית‬ ‫ארקוסין שירלי‬ ‫הירש תהילה‬ ‫סבתו ינון‬ ‫סעידיאן אורטל‬ Tel Aviv Medical Ctr Ariel Univ. [email protected] Hebrew Univ. [email protected] Technion [email protected] Tel Aviv Univ. [email protected] Hebrew Univ. [email protected] Technion Shenkar [email protected] Bar-Ilan Univ. [email protected] Tel Aviv Univ. [email protected] Haifa Univ. [email protected] Tel Aviv Univ. [email protected] Hebrew Univ. [email protected] Holon Inst. of Tech. [email protected] Technion [email protected] Holon Inst. of Tech. [email protected] Ben Gurion Univ. [email protected] Ben Gurion Univ. [email protected] Israel Traffic Police [email protected] Technion [email protected] Haifa Univ. & IBM [email protected] Technion [email protected] Ben Gurion Univ. [email protected] Technion [email protected] Tel Aviv Univ. [email protected] Bar-Ilan Univ. [email protected] Haifa Univ. [email protected] Tel Aviv Univ. [email protected] Technion [email protected] Columbia Univ. [email protected] Columbia Univ. ‫מכון לב‬ ‫מכון טל‬ ‫מכללת אפקה‬ ‫מכון טל‬ ‫מכון לב‬ ‫מכללת אפקה‬ 34 M4c S1 S5c, s5c* S5d M4b M1a S2d* S2c M5c S5a S2a S5b, S5b* M4c M1c M1a, M5a* M4a M5b S5a, s5a* M4a M4d M4b, M4b* M1d, M1d* M5c, S3* M4c S5a S5c S2c S2c* S5d M4c* S2a, M2* M1b, M1b* M2 M4c M1d M1d M1d M1d M1d M1d Operations Research Society of Israel (ORSIS) Annual Meeting May 10-11, 2015 Organized by Technion—Israel Institute of Technology The William Davidson Faculty of Industrial Engineering and Management Faculty of Electrical Engineering Sponsored by The Organizing Committee: Nahum Shimkin (Chair) Michal Penn Gail Gilboa-Friedman 35