A Self-Adaptive Differential Evolution Algorithm for Scheduling a Single Batch-Processing Machine With Arbitrary Job Sizes and Release Times

Batch-processing machines (BPMs) can process a number of jobs at a time, which can be found in many industrial systems. This article considers a single BPM scheduling problem with unequal release times and job sizes. The goal is to assign jobs into batches without breaking the machine capacity const...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 51; H. 3; S. 1430 - 1442
Hauptverfasser: Zhou, Shengchao, Xing, Lining, Zheng, Xu, Du, Ni, Wang, Ling, Zhang, Qingfu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive algorithms
Algorithms
Batch-processing machine (BPM)
differential evolution (DE)
Dynamic programming
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Heuristic algorithms
Job shop scheduling
Mutation
operator adaptation
Optimal scheduling
parameter adaptation
Scheduling
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:Batch-processing machines (BPMs) can process a number of jobs at a time, which can be found in many industrial systems. This article considers a single BPM scheduling problem with unequal release times and job sizes. The goal is to assign jobs into batches without breaking the machine capacity constraint and then sort the batches to minimize the makespan. A self-adaptive differential evolution algorithm is developed for addressing the problem. In our proposed algorithm, mutation operators are adaptively chosen based on their historical performances. Also, control parameter values are adaptively determined based on their historical performances. Our proposed algorithm is compared to CPLEX, existing metaheuristics for this problem and conventional differential evolution algorithms through comprehensive experiments. The experimental results demonstrate that our proposed self-adaptive algorithm is more effective than other algorithms for this scheduling problem.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2019.2939219