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An improved brain storm optimization algorithm for fuzzy distributed hybrid flowshop scheduling with setup time

Authors:

Jun-qing Li,

Hui Yu,

Xiaolong Chen,

Wenhan Li,

Yu Du,

Yu-yan HanAuthors Info & Claims

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Pages 275 - 276

https://doi.org/10.1145/3377929.3389986

Published: 08 July 2020 Publication History

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Abstract

The distributed hybrid flowshop scheduling (DHFS) problem, as one of the typical scheduling problems, has been researched in both academic and industrial fields during recent years. Consiering the DHFS with fuzzy processing time and setup time constraints, we develop an improved version of BSO, named IBSO. The objective is to minimize the maximize fuzzy completion time among all the factories. First, each solution is represented by a two-dimensional vectors. The two realistic constraints, i.e., the fuzzy processing time under uncertain enviornment and the setup time, make the problem close to the reality. Then, a novel constructive heuristic based on the Nawaz-Enscore-Ham (NEH) method, called distributed NEH, is proposed. Several local search heuristics considering the problem features and the objective are developed to enhance the local search abilities. Moreover a SA-based acceptance criterion is embeded to enhance the exploration abilities. Experimental results verify that the proposed algorithm is efficient and effective for solving the considered DHFS problems in comparison with other recently published efficient algorithms.

References

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B. Naderi and R. Ruiz, "The distributed permutation flowshop scheduling problem," Computers & Operations Research, vol. 37, no. 4, pp. 754--768, 2010.

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S. Hatami, R. Ruiz, and C. Andrés-Romano, "The Distributed Assembly Permutation Flowshop Scheduling Problem," International Journal Of Production Research, vol. 51, no. 17, pp. 5292--5308, 2013.

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T. K. Liu, Y. P. Chen, and J. H. Chou, "Solving Distributed and Flexible Job-Shop Scheduling Problems for a Real-World Fastener Manufacturer," IEEE Access., vol. 2, pp. 1598--1606, 2014.

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S. W. Lin and K. C. Ying, "Minimizing makespan for solving the distributed no-wait flowshop scheduling problem," Computers & Industrial Engineering, vol. 99, pp. 202--209, 2016.

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K. C. Ying and S. W. Lin, "Minimizing makespan for the distributed hybrid flowshop scheduling problem with multiprocessor tasks," Expert Systems with Applications, vol. 92, pp. 132--141, 2018.

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Zhang JCai J(2023)A Dual-Population Genetic Algorithm with Q-Learning for Multi-Objective Distributed Hybrid Flow Shop Scheduling ProblemSymmetry10.3390/sym1504083615:4(836)Online publication date: 30-Mar-2023
https://doi.org/10.3390/sym15040836
Zhao FHu XWang LXu TZhu NJonrinaldi (2022)A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problemInternational Journal of Production Research10.1080/00207543.2022.207078661:9(2854-2872)Online publication date: 12-May-2022
https://doi.org/10.1080/00207543.2022.2070786
Zhao FHu XWang LXu TZhu NJonrinaldi (2022)A brain storm optimization algorithm with feature information knowledge and learning mechanismApplied Intelligence10.1007/s10489-022-03762-3Online publication date: 20-Jun-2022
https://doi.org/10.1007/s10489-022-03762-3

Index Terms

An improved brain storm optimization algorithm for fuzzy distributed hybrid flowshop scheduling with setup time
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
      1. Scheduling algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning
        Sequential decision making

Index terms have been assigned to the content through auto-classification.

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Published In

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

July 2020

1982 pages

ISBN:9781450371278

DOI:10.1145/3377929

General Chair:
Carlos Artemio Coello Coello
CINVESTAV-IPN

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 08 July 2020

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GECCO '20: Genetic and Evolutionary Computation Conference

July 8 - 12, 2020

Cancún, Mexico

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Zhang JCai J(2023)A Dual-Population Genetic Algorithm with Q-Learning for Multi-Objective Distributed Hybrid Flow Shop Scheduling ProblemSymmetry10.3390/sym1504083615:4(836)Online publication date: 30-Mar-2023
https://doi.org/10.3390/sym15040836
Zhao FHu XWang LXu TZhu NJonrinaldi (2022)A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problemInternational Journal of Production Research10.1080/00207543.2022.207078661:9(2854-2872)Online publication date: 12-May-2022
https://doi.org/10.1080/00207543.2022.2070786
Zhao FHu XWang LXu TZhu NJonrinaldi (2022)A brain storm optimization algorithm with feature information knowledge and learning mechanismApplied Intelligence10.1007/s10489-022-03762-3Online publication date: 20-Jun-2022
https://doi.org/10.1007/s10489-022-03762-3

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Minimizing makespan for the distributed hybrid flowshop scheduling problem with multiprocessor tasks

An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time

Deteriorating job scheduling to minimize the number of late jobs with setup times

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Minimizing makespan for the distributed hybrid flowshop scheduling problem with multiprocessor tasks

An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time

Deteriorating job scheduling to minimize the number of late jobs with setup times

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