Modified Genetic Algorithm for Flexible Job-Shop Scheduling Problems

Teekeng, Wannaporn; Thammano, Arit

doi:10.1016/j.procs.2012.09.041

Cited by 40 publications

(19 citation statements)

References 9 publications

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“…In order to solve the flexible job shop scheduling problem, many algorithms have been proposed, such as tabu search algorithm [8], genetic algorithm [9], ant colony algorithm [10], particle swarm optimization algorithm [11] and so on. Genetic algorithm shows great applicability in solving similar problems, so this paper uses the genetic algorithm to solve the problem [12][13][14].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Flexible job shop scheduling model with parallel processes based on genetic algorithm

Bao¹,

Zhang²,

Zhang³

2017

Proceedings of the 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017)

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This paper studies the flexible job shop scheduling problem under the condition that with parallel processes. In this paper, we analyses the characteristics of the process parallelism in the job shop scheduling problem, puts forward the concept of parallel efficiency, establishes the flexible job shop scheduling model with parallel processes based on the objective criteria of minimizing the makespan, and designs the model solution algorithm based on genetic algorithm. The feasibility of the scheduling model and the effectiveness of the algorithm are verified by simulation experiments. The parameters of process parallel rate are compared and analyzed. The experimental results show that it is effective to improve the scheduling efficiency by improving the parallelism of the process route.

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Section: Genetic Algorithmmentioning

confidence: 99%

Flexible job shop scheduling model with parallel processes based on genetic algorithm

Bao¹,

Zhang²,

Zhang³

2017

Proceedings of the 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017)

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“…Teekeng and Tammano [8] developed a modified GA to minimize the total makespan. Kim, Yun, Yoon, Gen, and Yamazaki [9] used a hybrid fuzzy GA to min imize the total make-span and total lateness penalty cost.…”

Section: Related Workmentioning

confidence: 99%

A Multi-objective Mathematical Model for Job Scheduling on Parallel Machines Using NSGA-II

Saeidi¹

2016

IJITCS

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Abstract-In the current industrial world, Time and cost are two the most important concepts affecting whole our planning, activit ies and scheduling. Effective use of these factors, will lead to increasing performance and profit. Solving the parallel-mach ine problem is one of the basic and important problems in industrial and service delivery systems. In this paper, a new mathematical mult iobjective linear p rogramming model is proposed for scheduling the parallel machines to minimize the total make-span and total mach ines cost. The proposed model is imp lemented in Matlab using the NSGA -II 1 approach and the results are co mpared with MOPSO 2 approach. The computational results show the effectiveness and superiority of the proposed model.

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“…The flexible job shop scheduling problem (FJSP) is an extension of the classical job shop scheduling problem (JSP) [1]. FJSP can be divided into two subproblems [2]: One is to determine operation sequences, and the other one is to select corresponding machine for these operations.…”

Section: Introductionmentioning

confidence: 99%

“…However, GA has a slow convergence rate and tends to fall into the local optimum; therefore many improved or hybrid genetic algorithms are proposed to deal with the above-mentioned disadvantages. Reference [1] proposed a modified genetic algorithm based on a fuzzy roulette wheel selection, new crossover operator, and new mutation operator for flexible job shop scheduling problems. Zhang et al [2] designed global and local selection methods in order to generate high-quality initial population for minimizing the makespan.…”

Section: Introductionmentioning

confidence: 99%

A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm

Wang

Luo

Cai

2017

Journal of Advanced Transportation

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In real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial scheduling scheme to be nonoptimal and/or infeasible. Hence, appropriate dynamic rescheduling approaches are needed to overcome the dynamic events. In this paper, we propose a dynamic rescheduling method based on variable interval rescheduling strategy (VIRS) to deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions. On the other hand, an improved genetic algorithm (GA) is proposed for minimizing makespan. In our improved GA, a mix of random initialization population by combining initialization machine and initialization operation with random initialization is designed for generating high-quality initial population. In addition, the elitist strategy (ES) and improved population diversity strategy (IPDS) are used to avoid falling into the local optimal solution. Experimental results for static and several dynamic events in the FJSP show that our method is feasible and effective.

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Modified Genetic Algorithm for Flexible Job-Shop Scheduling Problems

Cited by 40 publications

References 9 publications

Flexible job shop scheduling model with parallel processes based on genetic algorithm

Flexible job shop scheduling model with parallel processes based on genetic algorithm

A Multi-objective Mathematical Model for Job Scheduling on Parallel Machines Using NSGA-II

A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm

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