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Australian Journal of Information Technology and Communication Volume II Issue I
ISSN 2203-2843
Job Shop Problem To Minimize Makespan Time
With Ant Colony Optimization Approach
Er. Geetinder kaur
PTU, CTITR
Jalandhar , India
[email protected]
Abstract- This paper manages application of the Ant Colony
Optimization, a met heuristic methodology to the Job Shop
Scheduling issue which particularly manages the thought of
a memory in attempting to focus the best course, and
negligible schedule for predetermined set of jobs. For the
minimization of production run, this scheduling deal with
minimizing make span time, which is modeled using an Ant
Colony Algorithm by stimulating the behavior of ants. The
numerical endeavor of ACO were actualized in little JSP
containing an data set of jobs, machines and operations to
produce ideal or close ideal outcomes as diagram. The key
reason of this paper is to diminish the make span time for a
dataset of jobs to infringe the best outcome.
Keywords—Job Shop Problem; Ant Colony Optimization
Algorithm; Pheromone; Makespan.
I. INTRODUCTION
With an aim to minimize parameter like minimizing make
span time of tasks is always a key decision of the management
of available resources. In the real world. The system for
scheduling jobs onto the machines is all that much regular and
has been scrutinized by number of researchers. The problem
which is selected is quite significant as it is close to the
problems of real world.
This paper examines the problem Job Shop
Scheduling Problem (JSSP) which typically consists of a finite
number of general purpose machines, as opposed to special
purpose machines which would typically happen in an
assembly line. In this the variety of jobs comprising of
operations have been processed and sequenced by machines to
find the minimal total make span time and both the nature and
demand of jobs is unpredictable. Make span time is the total
length of work schedule (That is completion time of all the
jobs) and is objective function in our case with the aim to
minimize it using an ACO approach.
Ant Colony Optimization (ACO) is a probabilistic
technique, and heuristic optimization method propelled by
Er. Sarabjit Kaur
PTU,CTITR
Jalandhar , India
[email protected]
biological frameworks. It is a multiagent approach intended to
discover, create or select a lower level method that may
provide a good solution to a difficult combinatorial
optimization problems. In the ACO, the main idea is
determining the interaction of colony of agents based on
biological material named pheromone which is a kind of
distributed numeric information, as it is the medium through
which ants communicate with each other to follow a particular
route, that effectively frame a solution by various stepwise
decisions until the goal has been achieved. [1]
The remaining paper is coordinated as followers. In Section
II a literature review for JSSP and enforced algorithms has
been conferred. Section III illustrates the formal description of
Job Scheduling Problem and ACO algorithm. In Section IV,
problem definition and methodology is proposed. In Section V
Experiments and Results are computed and Finally, Section VI
makes the conclusion remarks together with some conceives
about the future research.
II. LITRATURE SURVEY
Numerous authors have contemplated the JSSP and have
been viewed as N-P hard.. With the utilization of ACO
procedure, a few systems were proposed by authors to tackle
this scheduling issue and among those routines that have
achieved best results are: In 1989,carlier and Prinson have
added to a technique limb(branch) and bound that have been
satisfactorily applied to items for the enhancements of best
arrangements. In 1988, Adams added to a Simulated
Annealing system for greater issues like (TS) Tabu Search. In
1985, Davis proposed Job Shop Problem with the application
of Genetic Algorithm. There are numerous such works
alongside the application of advancement techniques. Shortest
Processing Time (SPT) cross breed heuristic strategy has been
proposed by Zhon and Feng for taking care of scheduling
issue. Viable pheromone adjustment procedure for
development of essential ant framework which helps in
examination of the arrangement space is proposed by Zhang.J.
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Australian Journal of Information Technology and Communication Volume II Issue I
ISSN 2203-2843
[1]
Total Make span time of set of jobs is minimizing by using
the heuristic technique of SPT (Shortest Processing Time) and
Procedure of LMC (Largest Marginal Contribution) by
Aftab.M.T. [3] In 2012, Selvi.V has proposed an examination
concerning the utilization of an ACO to streamline the JSP, by
minimizing make span time. [4] In 2013, Edson.F have
proposed the Elitist Ant System Algorithm to optimize JSP.[5]
In 2014, Abidia.M.H proposed Combined Shifting Bottleneck
and ACO technique to solve JSP to reduce the make span and
total weighted tardiness of jobs by generating the initial
solution.[6]
Pseudo code for basic ACO procedure:
Generate the set of solutions over the search space.
Select the best K elements among the set of solutions
as the set of ants.
Repeat
Build pheromones from ants in S
Create new solutions according to pheromones
information
III. JOB SHOP PROBLEM
In computer science and operation research, JSP is an
optimization problem in which ideal jobs are assigned to
resources at particular times. The ideal answer for issue
including n jobs must be transformed on m machines, decides
the example of landing of jobs on each one machine so as to
finish all the jobs on all the machines in the base aggregate
time emulating the same handling operation request when
passing through the machines with no priority requests. [4] The
issue is to find the ideal jobs groupings, setup times on the
machines in least time by utilizing the ACO calculation. The
JSP should be an extremely perplexing issue. Numerically, the
greatest no. of conceivable successions with n jobs and m
machines is (n!)m i.e. greatly substantial. The issue is typically
explained by close estimation or heuristic strategies.
Take the best K elements among S and the new
solution as new S
Until Termination criterion is met.
V .EXPERIMENTAL STUDY
While going for implementation part, we solve the JSP by
taking a scenario of ‘n’ number of jobs where n=4 and ‘m’
number of machines, where m=3. Each job having m=3
operations and has applied ACO approach on it to find optimal
results.
Jobs
Operations
III.1 ANT COLONY OPTIMIZATION (ACO)
ALGORITHM
Machine
ACO Algorithms has been focused around emulating idea;
every way emulated by the ant is connected with the given
issue. At the point when the ant takes after a path, the measure
of pheromone saved on that path is corresponding to the nature
of the comparing competitor answer for the target issue. At the
point when ant passes through two or more paths, the path with
greater measure of pheromone has more paramount probability
of being picked by the ant. Subsequently, the ants in the long
run unite to a shorter path, assuredly the ideal or any close ideal
answer for the target issue. [2]
1
1
1
2
3
2
3
5
2
1
2
3
2
10
2
3
7
3
9
3
1
2
3
3
9
7
4
1
2
3
18
Fig.1. Matrix of Jobs, Operations and Machine
IV.PROBLEM DEFINATION AND METHODLOGY
JSSP is an extraordinary kind of scheduling issue. It
consists of ‘n’ number of jobs let say a=1…..n and ‘m’
number of machines M1……Mm. Each job a consists of set of
operations Oab(b=1……na) with the deterministic processing
times pab. Each operation has been processed on machines mab
ε {M1……Mm) in an uninterrupted processing form and each
job processed in the same sequence on ‘m’ machines, having
individual flow of patterns.
In This exploration work, ACO calculation is applied
to minimize the aggregate make span time in the Job Shop
Scheduling.
Fig.2. The results generated by ACO algorithm for the given problem in
MATLAB.
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Australian Journal of Information Technology and Communication Volume II Issue I
ISSN 2203-2843
VI. CONCLUSION AND FUTURE SCOPE
In this paper an effective adjustment of the metahueristic ACO
for a JSP to minimize the aggregate make span time of given
set of jobs is displayed. As conclusion land at concerning the
utilization of normal ants to tackle the Job Scheduling
Problem with the ACO methodology is better and hence fit for
discovering the ideal or close to ideal arrangements. In future
work we can solve this problem by using hybrid heuristics
techniques such as PSO (Particle Swarm Optimization) and
GA (Genetic Algorithm) and TS (Tabu Search).
REFERENCES
Fig.2. The optimal result of bestrute taken by the jobs
[1]
Zhang .J, Hu.X,Tan.X ,Zhong J.H and Huang.Q,”
Implementation of an Ant Colony Optimization
technique for job shop scheduling problem”, The
Institute of Measurement and Control, 2006.
[2]
Nada M.A. Al Salami, “Ant Colony Optimization
Algorithms”, Ubicc Journal, vol: 4, 2009.
[3]
Tahir.M,Aftab,Umer.M,Ahmad.R,”Job Scheduling and
Worker Assignment Problem to Minimize Make span
using Ant Colony Optimization Met heuristic “,Word
Academy of Science Engineering and Technology ,vol
:6,2012.
[4]
Selvi.V,Umarani.R,” An Ant Colony Optimization for
Job Scheduling to Minimize Make span Time”,
International Journal of Computer & Communication
Technology, vol: 3, 2012.
[5]
Edson.F, Wilfreds.G, Lola.B,” An Ant Colony
optimization Algorithm For Job Shop Scheduling
Problem” , International Journal Of Artificial
intelligence And Application , vol: 4,2013.
[6]
Abidia.M.H,AlHarkanb.I,ElTamimib.A.M,Al Ahmaria
.A.M,” Ant Colony Optimization for Job Shop
Scheduling to Minimize the Total Weighted
Tardiness”, Industrial and Systems Engineering
Research Conference, 2014.
Fig.3. The optimal result of setup times taken by jobs
Fig.3. The optimal result of machine time taken by jobs