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Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times

Author

Listed:
  • Hamza Jouhari

    (School of automation, Wuhan University of Technology, Wuhan 430070, China)

  • Deming Lei

    (School of automation, Wuhan University of Technology, Wuhan 430070, China)

  • Mohammed A. A. Al-qaness

    (School of Computer Science, Wuhan University, Wuhan 430072, China)

  • Mohamed Abd Elaziz

    (Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt)

  • Ahmed A. Ewees

    (Department of e-Systems, University of Bisha, Bisha 61922, Saudi Arabia
    Department of Computer, Damietta University, Damietta 34511, Egypt)

  • Osama Farouk

    (Mathematics Department, Faculty of Science, Damanhour University, Beheira 22516, Egypt)

Abstract

This paper presents a hybrid method of Simulated Annealing (SA) algorithm and Sine Cosine Algorithm (SCA) to solve unrelated parallel machine scheduling problems (UPMSPs) with sequence-dependent and machine-dependent setup times. The proposed method, called SASCA, aims to improve the SA algorithm using the SCA as a local search method. The SCA provides a good tool for the SA to avoid getting stuck in a focal point and improving the convergence to an efficient solution. SASCA algorithm is used to solve UPMSPs by minimizing makespan. To evaluate the performance of SASCA, a set of experiments were performed using 30 tests for 4 problems. Moreover, the performance of the proposed method was compared with other meta-heuristic algorithms. The comparison results showed the superiority of SASCA over other methods in terms of performance dimensions.

Suggested Citation

  • Hamza Jouhari & Deming Lei & Mohammed A. A. Al-qaness & Mohamed Abd Elaziz & Ahmed A. Ewees & Osama Farouk, 2019. "Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times," Mathematics, MDPI, vol. 7(11), pages 1-18, November.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:11:p:1120-:d:287719
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    References listed on IDEAS

    as
    1. Fanjul-Peyro, Luis & Ruiz, Rubén, 2010. "Iterated greedy local search methods for unrelated parallel machine scheduling," European Journal of Operational Research, Elsevier, vol. 207(1), pages 55-69, November.
    2. Sang-Oh Shim & KyungBae Park, 2016. "Technology for Production Scheduling of Jobs for Open Innovation and Sustainability with Fixed Processing Property on Parallel Machines," Sustainability, MDPI, vol. 8(9), pages 1-10, September.
    3. Wang, Haibo & Alidaee, Bahram, 2019. "Effective heuristic for large-scale unrelated parallel machines scheduling problems," Omega, Elsevier, vol. 83(C), pages 261-274.
    4. Wu, Xueqi & Che, Ada, 2019. "A memetic differential evolution algorithm for energy-efficient parallel machine scheduling," Omega, Elsevier, vol. 82(C), pages 155-165.
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