IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v7y2019i11p1120-d287719.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/7/11/1120/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/7/11/1120/
    Download Restriction: no
    ---><---

    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. Wang, Haibo & Alidaee, Bahram, 2019. "Effective heuristic for large-scale unrelated parallel machines scheduling problems," Omega, Elsevier, vol. 83(C), pages 261-274.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rujapa Nanthapodej & Cheng-Hsiang Liu & Krisanarach Nitisiri & Sirorat Pattanapairoj, 2021. "Hybrid Differential Evolution Algorithm and Adaptive Large Neighborhood Search to Solve Parallel Machine Scheduling to Minimize Energy Consumption in Consideration of Machine-Load Balance Problems," Sustainability, MDPI, vol. 13(10), pages 1-25, May.
    2. Zhang, Zhe & Gong, Xue & Song, Xiaoling & Yin, Yong & Lev, Benjamin & Chen, Jie, 2022. "A column generation-based exact solution method for seru scheduling problems," Omega, Elsevier, vol. 108(C).
    3. Yamashiro, Hirochika & Nonaka, Hirofumi, 2021. "Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem," Operations Research Perspectives, Elsevier, vol. 8(C).
    4. Zoltán Varga & Pál Simon, 2014. "Examination Of Scheduling Methods For Production Systems," Advanced Logistic systems, University of Miskolc, Department of Material Handling and Logistics, vol. 8(1), pages 111-120, December.
    5. Yung-Chia Chang & Kuei-Hu Chang & Ching-Ping Zheng, 2022. "Application of a Non-Dominated Sorting Genetic Algorithm to Solve a Bi-Objective Scheduling Problem Regarding Printed Circuit Boards," Mathematics, MDPI, vol. 10(13), pages 1-21, July.
    6. JinHyo Joseph Yun & Tan Yigitcanlar, 2017. "Open Innovation in Value Chain for Sustainability of Firms," Sustainability, MDPI, vol. 9(5), pages 1-8, May.
    7. Pan, Quan-Ke & Ruiz, Rubén, 2012. "Local search methods for the flowshop scheduling problem with flowtime minimization," European Journal of Operational Research, Elsevier, vol. 222(1), pages 31-43.
    8. García-Martínez, C. & Rodriguez, F.J. & Lozano, M., 2014. "Tabu-enhanced iterated greedy algorithm: A case study in the quadratic multiple knapsack problem," European Journal of Operational Research, Elsevier, vol. 232(3), pages 454-463.
    9. Caizhi Sun & Ling Liu & Yanting Tang, 2018. "Measuring the Inclusive Growth of China’s Coastal Regions," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
    10. Alejandro Santiago & Mirna Ponce-Flores & J. David Terán-Villanueva & Fausto Balderas & Salvador Ibarra Martínez & José Antonio Castan Rocha & Julio Laria Menchaca & Mayra Guadalupe Treviño Berrones, 2021. "Energy Idle Aware Stochastic Lexicographic Local Searches for Precedence-Constraint Task List Scheduling on Heterogeneous Systems," Energies, MDPI, vol. 14(12), pages 1-22, June.
    11. Pessoa, Luciana S. & Andrade, Carlos E., 2018. "Heuristics for a flowshop scheduling problem with stepwise job objective function," European Journal of Operational Research, Elsevier, vol. 266(3), pages 950-962.
    12. Yepes-Borrero, Juan C. & Perea, Federico & Ruiz, Rubén & Villa, Fulgencia, 2021. "Bi-objective parallel machine scheduling with additional resources during setups," European Journal of Operational Research, Elsevier, vol. 292(2), pages 443-455.
    13. Obradović, Tena & Vlačić, Božidar & Dabić, Marina, 2021. "Open innovation in the manufacturing industry: A review and research agenda," Technovation, Elsevier, vol. 102(C).
    14. Sang-Oh Shim & KyungBae Park & SungYong Choi, 2017. "Innovative Production Scheduling with Customer Satisfaction Based Measurement for the Sustainability of Manufacturing Firms," Sustainability, MDPI, vol. 9(12), pages 1-12, December.
    15. Huerta-Muñoz, Diana L. & Ríos-Mercado, Roger Z. & Ruiz, Rubén, 2017. "An iterated greedy heuristic for a market segmentation problem with multiple attributes," European Journal of Operational Research, Elsevier, vol. 261(1), pages 75-87.
    16. Ching-Jong Liao & Cheng-Hsiung Lee & Hsing-Tzu Tsai, 2016. "Scheduling with multi-attribute set-up times on unrelated parallel machines," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4839-4853, August.
    17. Bahram Alidaee & Haibo Wang & R. Bryan Kethley & Frank Landram, 2019. "A unified view of parallel machine scheduling with interdependent processing rates," Journal of Scheduling, Springer, vol. 22(5), pages 499-515, October.
    18. Bahman Naderi & Rubén Ruiz & Vahid Roshanaei, 2023. "Mixed-Integer Programming vs. Constraint Programming for Shop Scheduling Problems: New Results and Outlook," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 817-843, July.
    19. Wang, Haibo & Alidaee, Bahram, 2019. "Effective heuristic for large-scale unrelated parallel machines scheduling problems," Omega, Elsevier, vol. 83(C), pages 261-274.
    20. F. Rodriguez & C. Blum & C. García-Martínez & M. Lozano, 2012. "GRASP with path-relinking for the non-identical parallel machine scheduling problem with minimising total weighted completion times," Annals of Operations Research, Springer, vol. 201(1), pages 383-401, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:7:y:2019:i:11:p:1120-:d:287719. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.