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A hybrid evolutionary algorithm to solve the job shop scheduling problem

Author

Listed:
  • T. C. E. Cheng

    (The Hong Kong Polytechnic University)

  • Bo Peng

    (Huazhong University of Science and Technology)

  • Zhipeng Lü

    (Huazhong University of Science and Technology)

Abstract

This paper presents a Hybrid Evolutionary Algorithm (HEA) to solve the Job Shop Scheduling Problem (JSP). Incorporating a tabu search procedure into the framework of an evolutionary algorithm, the HEA embraces several distinguishing features such as a longest common sequence based recombination operator and a similarity-and-quality based replacement criterion for population updating. The HEA is able to easily generate the best-known solutions for 90 % of the tested difficult instances widely used in the literature, demonstrating its efficacy in terms of both solution quality and computational efficiency. In particular, the HEA identifies a better upper bound for two of these difficult instances.

Suggested Citation

  • T. C. E. Cheng & Bo Peng & Zhipeng Lü, 2016. "A hybrid evolutionary algorithm to solve the job shop scheduling problem," Annals of Operations Research, Springer, vol. 242(2), pages 223-237, July.
    • Handle: RePEc:spr:annopr:v:242:y:2016:i:2:d:10.1007_s10479-013-1332-5
    DOI: 10.1007/s10479-013-1332-5
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    References listed on IDEAS

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    Cited by:

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    2. Lin, Geng & Guan, Jian & Feng, Huibin, 2018. "An ILP based memetic algorithm for finding minimum positive influence dominating sets in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 199-209.
    3. 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.
    4. Bo Peng & Yuan Zhang & Yuvraj Gajpal & Xiding Chen, 2019. "A Memetic Algorithm for the Green Vehicle Routing Problem," Sustainability, MDPI, vol. 11(21), pages 1-20, October.
    5. 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.
    6. Mohamed Habib Zahmani & Baghdad Atmani, 2021. "Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation," Journal of Scheduling, Springer, vol. 24(2), pages 175-196, April.
    7. Kannan Govindan, 2016. "Evolutionary algorithms for supply chain management," Annals of Operations Research, Springer, vol. 242(2), pages 195-206, July.

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