How to Obtain Fair Managerial Decisions in Sugarcane Harvest Using NSGA-II State University of Pernambuco – Recife (Brazil) Diogo F. Pacheco Tarcísio D. P. Lucas Fernando B. de L. Neto HIS’2007 – Kaiserslautern – Germany 1 Agenda I. Motivation & Problem II. Productivity Factors and Indicators III. Previous works IV. Additional background V. Fair harvest decisions for sugarcane VI. Simulation and Results VII. Conclusion HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 2 Part I Motivation & Problem HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 3 Sugarcane strategic importance • • • Sugarcane is a major source of carbohydrates for human feeding Sugarcane is also growing fast as a source of renewable fuel (e.g. 20-30% of the 23M vehicles of Brazilian fleet) Tendencies are of marked increase on sugarcane demand worldwide HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 4 The managerial problem at hand • • • During one harvest season, the manager has to select daily a variable number of lots (cultivated with sugarcane) to be harvested Every distinct species (not to mention the hybrids) have different maturation curves Various agronomical and industrial requirements are sometimes orthogonal between them HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 5 Part II Productivity Factors and Indicators HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 6 Productivity factors x indicators • • Factors: - Can be controlled directly - Exist in great numbers - Are contextual (time-space) Indicators: - Can only be controlled indirectly, thru productivity factors - Exist in small numbers - Can be inferred through induction HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 7 Examples of productivity factors -Cane variety (type); -Soil/Topology; -Climate; -Sowing date; -Age; etc HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 8 Examples of productivity indicators i. ii. iii. TCH (sucrose) – measure the sugarcane tonnage per hectare; PCC (biomass) – measure the apparent percentage of sugar in the cane juice; Fiber (quality of) – measure the calorific potential in the fibrous residue remaining after the extraction of juice. HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 9 Part III Previous works HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 10 Landmark work • • A Computational Intelligence technique – ANN was successfully used to model sugarcane maturation [Lima Neto, 1998] This initial work utilized historical data to help training MLP ANNs to predict productivity indicators that were identified as important to support decision makers HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 11 General idea (Generation 0) [Lima Neto, 1998] C. variety TCH Soil type Climate ANN PCC Sowing date Age Fiber Year HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 12 Follow-up work – Generation 1 • • • … Pacheco et al. (2005) refined predictions of indicators Pacheco et al. (2006) applied linear decision models HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 13 Some results – Generation 1 PCC PCC - Real x Predição 17 16 15 14 13 12 Real Previsão - Real 1 2 3 4 5 6 7 Fibra 8 9 10 11 12x13Predição 14 15 16 17 18 19 20 Fibra 25 20 Real 15 Previsão 10 1 2 3 4 5 6 7 8 9 -10 11 12x 13 14 15 16 17 TCH Real Predição 18 19 20 TCH 170 120 Real Previsão 70 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 14 Follow-up work – Generation 2 • • Oliveira et al. (2006) used GA to achieve better decisions and introduced a framework to evaluate decision quality Oliveira et al. (2007) used Fuzzy-Logic controllers to achieve even better decisions HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 15 Comparisons of generations 0, 1 & 2 Method Plots-ID TCH Avg. Fiber Avg. PCC Manual selection 34, 56, 102, 169, 199, 238, 365, 385, 404 649.0026 15.8376 16.5478 Linear method [Pacheco, 2006] 26, 34, 56, 102, 131, 169, 199, 365, 385, 404 667.0466 15.8349 16.5431 Framework + G.A. [Oliveira, 2006] 22, 314, 290, 335, 194, 147 649.8212 15.1324 16.1012 HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 16 Part IV Additional Background HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 17 Sugarcane • • • • “Sugarcane” encompass 37 different species (not to mention the hybrids) Each specie has its own agronomical behavior along the one year cycle Several factors interfere with sugarcane maturation Modeling maturation curves of species can have a profound economical impact HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 18 Sugarcane harvest • • • Sugarcane harvest is a non-trivial decision process due to the many variables interfering with its maturation The decision process encompass many simultaneous objectives to be cared of In many sugarcane plantations the harvest decision is empirical because of some inappropriateness of current systems HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 19 Decision Support Systems • • • • Help on the decision making process of semi-structured problems Generally used on mid-managerial level Users can select among possible scenarios via decision dialogues DSS should be friendly, fast and flexible to consider daily basis variables, e.g. sugarcane demand for the day. HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 20 Multi-Objective Optimization • • Real problems are usually multi-criterion (objective) A regular multi-objective optimization problem (MOOP) presents at least two challenges: (i) to find solutions as close as possible to the Pareto-optimal front and (ii) to obtain these solutions well spread over this front HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 21 NSGA-II: a fast non-dominated sorting approach [Deb et. al, 2000] • • • A MOEA algorithm with elitism Faster than PAES (notion of nondominating solutions) Produces better distributions on the Pareto front (notion of crowding) HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 22 NSGA-II: a fast non-dominated sorting approach [Deb et. al, 2000] HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 23 Framework to harvest decisions [Lima Neto et. al, 2007] Decision Space over Problem P encompassing decisions, components & attributes HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 24 Relevance of decision components • In this way, a suitable solution would be to search thoroughly among the possible solutions by assessing the relevance of every component of each decision ∑ ( w * f i (a i ) ) R(C j) = ∑ w n i =0 i n i =0 i HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Regarding attribute: fi(ai) Mapping function W = {w1,w2,...,wn} weights Page 25 Evaluation of decision components (past) * Area * PCC TCH f ( PCC ) = MAX (TCH * Area * PCC ) i pcc f i i * Area * Fiber TCH ( Fiber ) = MAX (TCH * Area * Fiber) i Fiber i i i i Decision maker has to inform his/her: a) Business preferences: W = { w-fiber, w-pcc} b) Needs: {desired ton of sugarcane, Maximum Area, Minimum PCC, Minimum Fiber} HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 26 Evaluation of decision components (past) R(C j) = f Fibra ( Fiber i ) * wFiber + f w +w Fiber HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] pcc ( PCC i ) * wPCC PCC Page 27 Evaluation of decisions (past) Maximizing overall relevance F(d k) = ∑ R(C j) n j =0 Minimizing overall penalty 1 F (d k ) = ∑ R(C j) n j =0 HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 28 Part V Fair harvest decisions for sugarcane HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 29 This work approach – overview HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 30 Representing candidate solutions The individuals utilized represent the available (i.e. not harvested) lots; A bit stream representation is used, where 0 indicates assigned to be harvested, and 1 means the opposite; At each generation, the number of lots available decrease so does the size of individuals. Genes of an individual (i.e. available lots) 1001001000 1110000010 1000110001 0010101001 Figure . Individual representation of a suggestion of 15 lots in 40 available ones HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 31 GA Initial Population Individuals are randomly generated according to the quantity of available lots in the field Each bit in the genotype has a 50% likelihood of being activated HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 32 This approach animation Predictions of N available lots Generates ANN – Multilayer Perceptron Process End Mapping lots into genotype Chooses a solution HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Optimal Pareto-front Evolves Page 33 Part VI Simulation and Results HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 34 Objectives of this work 1) To incorporate agronomical performance indicators into an Intelligent Decision System to help decision makers (to better deciding on sugarcane harvest) 2) To produce and test a prototype of a computer system that couples in a fairway the sugarcane-mill demands with acceptable agronomical indicators HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 35 The problem (new) definition To find the combination of lots that maximizes the production of PCC and Fiber, constrained to a minimal tonnage (that guarantees energy power for the sugar mill to operate without interruption) HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 36 Two flavors for the problem simulation A) considers 2 objectives and uses a penalty function; B) converts the constraint into another objective. Hence, the optimization considers 3 objectives. HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 37 Formalism • Approach A: • Approach B: HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 38 NSGA-II Parameters The parameters used in NSGA-II were experimentally chosen; A new stop criterion was used in parallel to the number of generations. HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 39 Experimental Setup Data set: 418 training patterns obtained from a sugarcane mill from Brazil; We assumed a hypothetical scenario: • The minimal desired tonnage for each prediction was fixed at 4000 tons. • The heuristic is applied accepting boundaries from less 0.5% to more 5% tons; • Two predictions per month, i.e. one for each fortnight; • The harvest must be finished at most in 12 interactions (6 months); • In the12th interaction, if the limit of 4200 ton is achieved and there are still remaining lots to be cropped, they will all be harvested. HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 40 Results of Experiments Approach A Approach B HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 41 Comparisons with other techniques HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 42 Part VII Conclusion HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 43 Conclusion The application of EA within a MO formulation can be very beneficial to the sugarcane harvest decision process NSGA-II as utilized can aggregate fairness to the manager decision of the problem The consideration of a constraint as another goal was found to be a good avenue of thinking (as the attribution of a penalty function may cause the same problems faced by MO classical methods) HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 44 Future work To model the sugarcane harvest considering, not only agronomical factors, but also logistics To consider fuzzy-logic to soften the thresholds used; Further investigations on better transforming penalty functions into objectives Further investigations on how to incorporate decision preferences a posteriori (upon the Pareto landscape) HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 45 Related references F.B. Lima Neto, “Managerial Decision Support, based on Artificial Neural Networks” (in Portuguese), Master Dissertation. Department of Informatics, Federal University of Pernambuco, Recife - Brazil, 1998. D.F. Pacheco, F.S. Regueira and F.B.L. Neto, “Using Artificial Neural Networks in Sugar Cane Harvest to Predict PCC, TCH and Fiber” (in Portuguese), Alcoolbrás Magazine, S. Paulo - Brazil, v. 90, 2005, pp. 60-63. D.F. Pacheco, “An Intelligent Decision Support System for Agriculture Harvest” (in Portuguese), Technical Report presented as Graduation Monograph, Department of Computing Systems, Polytechnic School of Engineering, Pernambuco State University, Recife - Brazil, 2006. F.R.S. Oliveira, D.F. Pacheco and F.B.L. Neto, “Intelligent Support Decision in Sugarcane Harvest”, Proceedings 4th World Congress of Computers in Agriculture, Orlando, Florida, 2006, pp. 456-462. F.R.S. Oliveira, D.F. Pacheco and F.B.L. Neto, “Hybrid Intelligent Suite For Decision Support in Sugarcane”, 6th Brazilian Congress of AgroInformatics (SBIAgro), São Pedro (SP) – Brasil, 2007. F.B. Lima Neto, F.R.S. Oliveira, D.F. Pacheco “HIDS: Hybrid Intelligent Suite for Decision Support”. In: Seventh International Conference on Intelligent Systems Design and Applications (ISDA), Rio de Janeiro - Brasil, 2007. HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 46 Other references [1] Biodiesel and Ethanol Investing, “Ethanol Fuel Benefits”, available on http://www.biodieselinvesting.com/ethanol-fuel-benefits/, accessed in May 2007. [2] Food and Agricultural Organization of United Nations, “Major Food and Agricultural Commodities and Producers”, The Statistics Division, Economic and Social Department, available on http://www.fao.org/es/ess/top/commodity.ht ml?lang=en&item=156&year=2005, accessed in May 2007. [5] C.H. Papadimitriou and K. Steiglitz, “Combinatorial Optimization: Algorithms and Complexity”. Prentice-Hall, Inc., 1982. [8] S. Haykin, “Neural Networks – A Comprehensive Foundation”. Prentice-Hall International Editions. New Jersey, USA, 1994. [9] A.E. Eiben and J.E. Smith, “Introduction to Evolutionary Computing”, Springer, New York, 2003. [10] K. Deb. “Multi-Objective Optimization using Evolutionary Algorithms”, John Wiley & Sons, UK, 2001. [11] I. Linkov, et. al., “Multi-criteria decision analysis: A framework for structuring remedial decisions at the contaminated sites”, Springer, New York, 2004, pp. 15-54. [12] J. Fülöp, “Introduction to decision-making methods”, BDEI-3 Workshop, Washington, 2005. [13] J. Figueira et. al., “Multiple Criteria Decision Analysis: State of the Art Surveys”, Springer, New York, 2004. [14] C.A.C. Coello, “Metaheuristics for Multiobjective Optimization”, Tutorial on IEEE Symposium Series on Computational Intelligence, 2007. [15] K. Deb et. al., “A Fast Elitist Non-Dominated Sorting Genetic Algorithm for MultiObjective Optimization: NSGA-II”, KanGAL report 200001, Indian Institute of Technology, Kanpur, India, 2000. HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 47 Thank you ! Fernando Buarque http://www.fbln.pro.br [email protected] HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 48 Extra: Oliveira, 2006-GA parameters Desired scenario: • Validity criteria • PCC (minimum) = 16 • Fiber (minimum) = 15 • TCH (target) = 650 T • Area (maximum) = 10 plots • Weights of attributes: • wpcc = 10 • wfiber = 5 HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 49 Extra: Oliveira, 2006-conclusion Contributed DSS is a great advance when compared current harvest decision process because: • Speed-up the decision process • Reduce the number of plots selected with neither compromising quality nor biomass • Help on reducing human error Contributed approach allows re-runs generating different suggestions and distinct scenarios HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 50 Extra: Oliveira, 2006-steps 1. Gather candidate decisions (ANN) 2. Define components and attributes 3. Set validity criteria 4. Proceed the search (gDSS) 5. Evaluate decision 6. Finish or re-start from step 4. HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 51 Extra: Oliveira, 2006-references Diogo Pacheco. An Intelligent Decision Support System for Agriculture Harvest (in Portuguese), Technical Report presented as Graduation Monograph to Department of Computing Systems – Polytechnic School of Engineering – Pernambuco State University, 2006. Efrain Turban. Decision Support Systems and Expert Systems, 4th. Edition , Prentice-Hall International Editions. New Jersey, USA, 1995. Fernando Buarque de Lima Neto. Managerial Decision Support, based on Artificial Neural Networks (in Portuguese), Master Dissertation presented to Department of Informatics, Federal University of Pernambuco, Recife, Brazil, 1998. Randy L. Haupt and Sue E. Haupt. Practical Genetic Algorithms. 2nd ed. WileyInterscience, 2004. Simon Haykin. Neural Networks – A Comprehensive Foundation. Prentice-Hall International Editions. New Jersey, USA, 1994. Ralph Sprague Jr. and Hugh J. Watson. Decision Support for Management. Prentice-Hall International Editions. New Jersey, USA, 1996. Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach.2nd Edition. Prentice-Hall International Editions. New Jersey, USA, 2003. HIS’2007 – Kaiserslautern – Germany © Fernando Buarque – [email protected] Page 52
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