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Parallel bat algorithm for optimizing makespan in job shop scheduling problems

Author

Listed:
  • Thi-Kien Dao

    (National Kaohsiung University of Applied Sciences)

  • Tien-Szu Pan

    (National Kaohsiung University of Applied Sciences)

  • Trong-The Nguyen

    (National Kaohsiung University of Applied Sciences)

  • Jeng-Shyang Pan

    (College of Information Science and Engineering, Fujian University of Technology)

Abstract

Parallel processing plays an important role in efficient and effective computations of function optimization. In this paper, an optimization algorithm based on parallel versions of the bat algorithm (BA), random-key encoding scheme, communication strategy scheme and makespan scheme is proposed to solve the NP-hard job shop scheduling problem. The aim of the parallel BA with communication strategies is to correlate individuals in swarms and to share the computation load over few processors. Based on the original structure of the BA, the bat populations are split into several independent groups. In addition, the communication strategy provides the diversity-enhanced bats to speed up solutions. In the experiment, forty three instances of the benchmark in job shop scheduling data set with various sizes are used to test the behavior of the convergence, and accuracy of the proposed method. The results compared with the other methods in the literature show that the proposed scheme increases more the convergence and the accuracy than BA and particle swarm optimization.

Suggested Citation

  • Thi-Kien Dao & Tien-Szu Pan & Trong-The Nguyen & Jeng-Shyang Pan, 2018. "Parallel bat algorithm for optimizing makespan in job shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 451-462, February.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:2:d:10.1007_s10845-015-1121-x
    DOI: 10.1007/s10845-015-1121-x
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    1. Blazewicz, Jacek & Domschke, Wolfgang & Pesch, Erwin, 1996. "The job shop scheduling problem: Conventional and new solution techniques," European Journal of Operational Research, Elsevier, vol. 93(1), pages 1-33, August.
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    4. Goncalves, Jose Fernando & de Magalhaes Mendes, Jorge Jose & Resende, Mauricio G. C., 2005. "A hybrid genetic algorithm for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 167(1), pages 77-95, November.
    5. James C. Bean, 1994. "Genetic Algorithms and Random Keys for Sequencing and Optimization," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 154-160, May.
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    Cited by:

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    2. Yuanfei Wei & Zalinda Othman & Kauthar Mohd Daud & Shihong Yin & Qifang Luo & Yongquan Zhou, 2022. "Equilibrium Optimizer and Slime Mould Algorithm with Variable Neighborhood Search for Job Shop Scheduling Problem," Mathematics, MDPI, vol. 10(21), pages 1-20, November.
    3. Ying Sun & Jeng-Shyang Pan & Pei Hu & Shu-Chuan Chu, 2023. "Enhanced Equilibrium Optimizer algorithm applied in job shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1639-1665, April.
    4. Isaac Kofi Nti & Adebayo Felix Adekoya & Benjamin Asubam Weyori & Owusu Nyarko-Boateng, 2022. "Applications of artificial intelligence in engineering and manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1581-1601, August.
    5. Ai-Qing Tian & Shu-Chuan Chu & Jeng-Shyang Pan & Huanqing Cui & Wei-Min Zheng, 2020. "A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station," Sustainability, MDPI, vol. 12(3), pages 1-19, January.
    6. Tao Ren & Yan Zhang & Shuenn-Ren Cheng & Chin-Chia Wu & Meng Zhang & Bo-yu Chang & Xin-yue Wang & Peng Zhao, 2020. "Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates," Mathematics, MDPI, vol. 8(8), pages 1-25, July.
    7. Hongfeng Wang & Min Huang & Junwei Wang, 2019. "An effective metaheuristic algorithm for flowshop scheduling with deteriorating jobs," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2733-2742, October.
    8. Shahed Mahmud & Ripon K. Chakrabortty & Alireza Abbasi & Michael J. Ryan, 2022. "Switching strategy-based hybrid evolutionary algorithms for job shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1939-1966, October.
    9. Hongli Yu & Yuelin Gao & Le Wang & Jiangtao Meng, 2020. "A Hybrid Particle Swarm Optimization Algorithm Enhanced with Nonlinear Inertial Weight and Gaussian Mutation for Job Shop Scheduling Problems," Mathematics, MDPI, vol. 8(8), pages 1-17, August.

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