A tutorial survey of job-shop scheduling problems using genetic algorithms—I. representation

Cheng, Runwei; Gen, Mitsuo; Tsujimura, Yasuhiro

doi:10.1016/0360-8352(96)00047-2

Cited by 527 publications

(246 citation statements)

References 12 publications

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“…(8), (10), (9), (11) Cheng et al [6], Kleeman and Lamont [9] introduced a taxonomy of how Evolutionary Algorithms (EA) represent job-shop problems. These representations can be classified as either directly encoded approaches or indirectly coded approaches.…”

Section: Encoding Representationsmentioning

confidence: 99%

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A novel Variable Neighborhood Particle Swarm Optimization for multi-objective Flexible Job-Shop Scheduling Problems

Liu

Abraham

Groşan

2007

2007 2nd International Conference on Digital Information Management

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Section: Encoding Representationsmentioning

confidence: 99%

“…Liouane et al [5] proposed a hybrid algorithm based on ant systems and local search optimization for FJSP. More researchers attempt to solve the FJSP using genetic algorithms [6,7]. Ho et al [8] proposed an architecture for learning and evolving of Flexible Job-Shop schedules to improve the computational time and quality of schedules.…”

Section: Introductionmentioning

confidence: 99%

A novel Variable Neighborhood Particle Swarm Optimization for multi-objective Flexible Job-Shop Scheduling Problems

Liu

Abraham

Groşan

2007

2007 2nd International Conference on Digital Information Management

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“…The main aim of JSSP is to find a schedule having minimum length [28]. A schedule may be defined as the mapping of jobs to machines, and the processing times of operations to minimize the makespan i.e.…”

Section: Job Shop Scheduling Problem (Jssp)mentioning

confidence: 99%

Makespan Optimization in Job Shop Scheduling Problem using Differential Genetic Algorithm

Kaur¹,

Khehra²,

Virk³

2017

IJCA

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Job shop scheduling problem belongs to a class of NP-Hard problems. Hence, finding an optimal solution for this problem is a difficult task. In this study, a hybrid method consisting of Genetic Algorithm (GA) and Differential Evolution (DE) algorithm has been proposed for solving the Job Shop Scheduling problem (JSSP). These algorithms are evolutionary algorithms for solving optimization problems which refine the candidate solutions iteratively. The results of previous studies show that the application of genetic algorithm and differential evolution algorithm individually for this problem yield results close to the upper bounds. The proposed algorithm implemented in MATLAB R2013a uses minimization of makespan as the objective function. This algorithm has been tested on 50 instances of Taillard series (TA01-50) benchmark problem. The simulation results obtained by the proposed algorithm are better than those obtained by the IPSO-TSAB algorithm.

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“…Cheng, Gen and Tsujimura [7] provide a survey on the different GA representations used for the JSP. They classify them into two categories: direct and indirect.…”

Section: Ga Representationsmentioning

confidence: 99%

“…In their tutorial paper, Cheng, Gen and Tsujimura [7] provide a literature survey on the different GA representations used for the JSP. However, there is a gap in the literature regarding the computational comparison of various representation schemes as only minor experimental comparison is reported by Gen and Cheng [3].…”

Section: Introductionmentioning

confidence: 99%

Representations in Genetic Algorithm for the Job Shop Scheduling Problem: A Computational Study

Abdelmaguid¹

2010

JSEA

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Due to the NP-hardness of the job shop scheduling problem (JSP), many heuristic approaches have been proposed; among them is the genetic algorithm (GA). In the literature, there are eight different GA representations for the JSP; each one aims to provide subtle environment through which the GA's reproduction and mutation operators would succeed in finding near optimal solutions in small computational time. This paper provides a computational study to compare the performance of the GA under six different representations.

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A tutorial survey of job-shop scheduling problems using genetic algorithms—I. representation

Cited by 527 publications

References 12 publications

A novel Variable Neighborhood Particle Swarm Optimization for multi-objective Flexible Job-Shop Scheduling Problems

A novel Variable Neighborhood Particle Swarm Optimization for multi-objective Flexible Job-Shop Scheduling Problems

Makespan Optimization in Job Shop Scheduling Problem using Differential Genetic Algorithm

Representations in Genetic Algorithm for the Job Shop Scheduling Problem: A Computational Study

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