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Solving Type-2 Fuzzy Distributed Hybrid Flowshop Scheduling Using an Improved Brain Storm Optimization Algorithm

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Abstract

The distributed hybrid flowshop scheduling (DHFS) problem is a common scheduling problem that has been researched in both academic and industrial fields during recent years. The uncertainty levels in realistic applications are generally too high to be represented by a deterministic value or a triangular fuzzy number (TFN) value. Considering the DHFS problem with type-2 fuzzy processing time and setup time constraints, an improved version of brain storm optimization was developed, where the objective is to minimize the maximum type-2 fuzzy completion time among all factories. The main contributions of this study are as follows: (1) each solution is represented by a two vectors, i.e., a scheduling vector and a factory assignment vector; (2) two realistic constraints, i.e., the type-2 fuzzy processing time in an uncertain environment and the setup time, make the problem more realistic; (3) a novel constructive heuristic based on the Nawaz-Enscore-Ham (NEH) method, called distributed NEH, is proposed; (4) several local search heuristics considering the problem features and the objective are developed to enhance the local search abilities; and (5) a simulated-annealing-based acceptance criterion is embedded to enhance the exploration abilities. The experimental results demonstrate that the proposed algorithm is more efficient and effective for solving the considered type-2 fuzzy DHFS problems in comparison with other recently published efficient algorithms.

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Acknowledgements

This research is partially supported by National Science Foundation of China under Grant 61773192, 61803192, 61773246.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, People’s Republic of China

    Junqing Li

  2. School of Computer, Liaocheng University, Liaocheng, People’s Republic of China

    Junqing Li, Hongyan Sang, Yuyan Han & Qingda Chen

  3. School of Tourism, Hainan University, Haikou, People’s Republic of China

    Jiake Li

  4. Liaocheng Jingxin Steel Pipe Plant, Liaocheng, People’s Republic of China

    Lijing Zhang

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  1. Junqing Li
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  2. Jiake Li
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  3. Lijing Zhang
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Correspondence to Junqing Li.

Appendix

Appendix

The list of acronyms used in this study is given as follows:

  • FSP: flowshop scheduling problem

  • HFS: hybrid flowshop scheduling

  • FJSP: flexible job shop scheduling problem

  • DFSP: distributed flow shop scheduling problems

  • DAPFSP: distributed assembly permutation flowshop scheduling problems

  • DFJSP: distributed flexible job shop scheduling problems

  • TFN: triangular fuzzy number

  • T1FLS: type-1 fuzzy logic system

  • T2FS: type-2 fuzzy logic system

  • BSO: brain storm optimization

  • VNS: variable neighborhood search

  • ILS: iterated local search

  • SA: simulated annealing

  • IG: iterated greedy

  • FOA: fruit fly optimization algorithm

  • TS: tabu search

  • PSO: particle swarm optimization

  • ABC: artificial bee colony

  • GA: genetic algorithm

  • TLBO: teaching-learning-based optimization

  • SS: scatter search

  • CRO: chemical reaction optimization

  • EDA: estimation of distribution algorithm

  • ANOVA: analysis of variance

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Li, J., Li, J., Zhang, L. et al. Solving Type-2 Fuzzy Distributed Hybrid Flowshop Scheduling Using an Improved Brain Storm Optimization Algorithm. Int. J. Fuzzy Syst. 23, 1194–1212 (2021). https://doi.org/10.1007/s40815-021-01050-9

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