2014
DOI: 10.4236/jsea.2014.77053
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Comparative Study of Different Representations in Genetic Algorithms for Job Shop Scheduling Problem

Abstract: Due to NP-Hard nature of the Job Shop Scheduling Problems (JSP), exact methods fail to provide the optimal solutions in quite reasonable computational time. Due to this nature of the problem, so many heuristics and meta-heuristics have been proposed in the past to get optimal or near-optimal solutions for easy to tough JSP instances in lesser computational time compared to exact methods. One of such heuristics is genetic algorithm (GA). Representations in GA will have a direct impact on computational time it t… Show more

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Cited by 13 publications
(6 citation statements)
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“…With the job based representation [20], the previous study had shown favorable results. Therefore, by using the same representation in genetic algorithms, this study aims to establish the effect of initial population scheme on the overall convergence of each benchmark instance.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…With the job based representation [20], the previous study had shown favorable results. Therefore, by using the same representation in genetic algorithms, this study aims to establish the effect of initial population scheme on the overall convergence of each benchmark instance.…”
Section: Methodsmentioning
confidence: 99%
“…Genetic algorithms are well suited in such cases to find the best possible solution close to optimal solution in a computationally efficient manner. Different mathematical models may lead to different representations for the same problem [20]. Attempts to explain different available representations and explore the use of a better representation scheme for the job shop scheduling problems while using genetic algorithms was done and the study was conducted over 66 bench mark instances.…”
Section: Representation Of the Problem In Ga And Ga Operatorsmentioning
confidence: 99%
“…This structure is preserved in the vast majority of state-of-the-art techniques. The codification used to represent a possible solution (chromosome) of the problem can be done in many different ways, as highlighted in Reference [ 63 ].…”
Section: Multi-crossover Local Search Genetic Algorithm For Jsspmentioning
confidence: 99%
“…Encoding solutions into the chromosome is a process that is first performed in the GA. This process is not easy to do because of the determination of the proper representation of chromosomes will affect the overall process of the GA [20]. The effectiveness of the GA in the exploration of the search space will be strongly influenced by the representation of the chromosome used [21].…”
Section: Chromosome Representationmentioning
confidence: 99%