Flexible job shop scheduling with parallel machines using Genetic Algorithm and Grouping Genetic Algorithm

Chen, James C.; Wu, Cheng-Chun; Chen, Chia‐Wen; Chen, Kou-Huang

doi:10.1016/j.eswa.2012.01.211

Cited by 100 publications

(46 citation statements)

References 16 publications

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“…A standard Job Shop Scheduling Problem (JSSP) may be defined as: There are 'n' different jobs required to be processed on 'm' machines, which execute one of the 'm' operations per job [14,26]. The number of operations that each job consists of, is equal to the number of machines.…”

Section: Job Shop Scheduling Problem (Jssp)mentioning

confidence: 99%

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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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Section: Job Shop Scheduling Problem (Jssp)mentioning

confidence: 99%

“…Encoding: It is used to describe the chromosome. The representation may be direct or indirect [1,26]. In direct representation, the chromosomes represent the schedules for scheduling problem whereas in indirect representation, the chromosome does not directly represent a schedule.…”

Section: Differential Genetic Algorithm (Dga) 31 Genetic Algorithm (Ga)mentioning

confidence: 99%

Makespan Optimization in Job Shop Scheduling Problem using Differential Genetic Algorithm

Kaur¹,

Khehra²,

Virk³

2017

IJCA

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“…Su Zhaofeng [5] compared the serial scheduling model and the parallel scheduling model of the flexible job shop scheduling problem. James C. Chen [6] studied the job shop scheduling problem with parallel machines and rework. Xu Benzhu [7] proposed a new scheduling algorithm involved the parallel processes of sub-batch, and solve the batch processing scheduling problem effectively.…”

Section: Introductionmentioning

confidence: 99%

“…Formula (4) indicates the constraint of the processing time. Formula (5), (6) indicates the time constraints of the operation of jobs, and the time constraints of operations on each machine, and the iu s , iu e are the start and end time of the u th operation on machine i M . Formula (7) indicates the completion time of each job.…”

Section: （6）mentioning

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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“…Recently, Chen et al (2012) developed a scheduling algorithm based on GA. As to the integrated approach, Loukil, et al (2005) utilised the simulated annealing algorithm to solve the FJSP. Gao et al (2008) proposed a genetic algorithm hybridizing with the variable neighborhood search.…”

Section: Introductionmentioning

confidence: 99%

An elitist quantum-inspired evolutionary algorithm for the flexible job-shop scheduling problem

2015

J Intell Manuf

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search with a method that is inspired by the motion mechanism of the electrons around atomic nucleuses. Three novel algorithms are proposed and their effect on the whole search is discussed. The elitist strategy is adopted to prevent the optimal solution from being destroyed during the evolutionary process. The results show that the proposed algorithm outperforms the best-known algorithms for FJSPs on most of the FJSP benchmarks.

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Flexible job shop scheduling with parallel machines using Genetic Algorithm and Grouping Genetic Algorithm

Cited by 100 publications

References 16 publications

Makespan Optimization in Job Shop Scheduling Problem using Differential Genetic Algorithm

Makespan Optimization in Job Shop Scheduling Problem using Differential Genetic Algorithm

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

An elitist quantum-inspired evolutionary algorithm for the flexible job-shop scheduling problem

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