IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v141y2013i1p87-98.html
   My bibliography  Save this article

A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling

Author

Listed:
  • Chiang, Tsung-Che
  • Lin, Hsiao-Jou

Abstract

This paper addresses the multiobjective flexible job shop scheduling problem (MOFJSP) regarding minimizing the makespan, total workload, and maximum workload. The problem is solved in a Pareto manner, whose goal is to seek for the set of Pareto optimal solutions. We propose a multiobjective evolutionary algorithm, which utilizes effective genetic operators and maintains population diversity carefully. A main feature of the proposed algorithm is its simplicity—it needs only two parameters. Performance of our algorithm is compared with seven state-of-the-art algorithms on fifteen popular benchmark instances. Only our algorithm can find 70% or more non-dominated solutions for every instance.

Suggested Citation

  • Chiang, Tsung-Che & Lin, Hsiao-Jou, 2013. "A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling," International Journal of Production Economics, Elsevier, vol. 141(1), pages 87-98.
    • Handle: RePEc:eee:proeco:v:141:y:2013:i:1:p:87-98
    DOI: 10.1016/j.ijpe.2012.03.034
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527312001429
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2012.03.034?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mati, Yazid & Xie, Xiaolan, 2004. "The complexity of two-job shop problems with multi-purpose unrelated machines," European Journal of Operational Research, Elsevier, vol. 152(1), pages 159-169, January.
    2. Baykasoglu, Adil & ÖzbakIr, Lale, 2010. "Analyzing the effect of dispatching rules on the scheduling performance through grammar based flexible scheduling system," International Journal of Production Economics, Elsevier, vol. 124(2), pages 369-381, April.
    3. Chang, Pei-Chann & Hsieh, Jih-Chang & Lin, Shui-Geng, 2002. "The development of gradual-priority weighting approach for the multi-objective flowshop scheduling problem," International Journal of Production Economics, Elsevier, vol. 79(3), pages 171-183, October.
    4. Kacem, Imed & Hammadi, Slim & Borne, Pierre, 2002. "Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 245-276.
    5. Eugeniusz Nowicki & Czeslaw Smutnicki, 1996. "A Fast Taboo Search Algorithm for the Job Shop Problem," Management Science, INFORMS, vol. 42(6), pages 797-813, June.
    6. Zribi, N. & El Kamel, A. & Borne, P., 2008. "Minimizing the makespan for the MPM job-shop with availability constraints," International Journal of Production Economics, Elsevier, vol. 112(1), pages 151-160, March.
    7. 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.
    8. Moslehi, Ghasem & Mahnam, Mehdi, 2011. "A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search," International Journal of Production Economics, Elsevier, vol. 129(1), pages 14-22, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kumar, V.N.S.A. & Kumar, V. & Brady, M. & Garza-Reyes, Jose Arturo & Simpson, M., 2017. "Resolving forward-reverse logistics multi-period model using evolutionary algorithms," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 458-469.
    2. Xiuli Wu & Shaomin Wu, 2017. "An elitist quantum-inspired evolutionary algorithm for the flexible job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1441-1457, August.
    3. Gerstl, Enrique & Mosheiov, Gur, 2013. "A two-stage flow shop batch-scheduling problem with the option of using Not-All-Machines," International Journal of Production Economics, Elsevier, vol. 146(1), pages 161-166.
    4. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
    5. Choo Jun Tan & Siew Chin Neoh & Chee Peng Lim & Samer Hanoun & Wai Peng Wong & Chu Kong Loo & Li Zhang & Saeid Nahavandi, 2019. "Application of an evolutionary algorithm-based ensemble model to job-shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 879-890, February.
    6. M. Saqlain & S. Ali & J. Y. Lee, 2023. "A Monte-Carlo tree search algorithm for the flexible job-shop scheduling in manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 35(2), pages 548-571, June.
    7. Deming Lei & Youlian Zheng & Xiuping Guo, 2017. "A shuffled frog-leaping algorithm for flexible job shop scheduling with the consideration of energy consumption," International Journal of Production Research, Taylor & Francis Journals, vol. 55(11), pages 3126-3140, June.
    8. Yuri N. Sotskov & Omid Gholami, 2017. "Mixed graph model and algorithms for parallel-machine job-shop scheduling problems," International Journal of Production Research, Taylor & Francis Journals, vol. 55(6), pages 1549-1564, March.
    9. Alejandro Vital-Soto & Mohammed Fazle Baki & Ahmed Azab, 2023. "A multi-objective mathematical model and evolutionary algorithm for the dual-resource flexible job-shop scheduling problem with sequencing flexibility," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 626-668, September.
    10. Alper Türkyılmaz & Özlem Şenvar & İrem Ünal & Serol Bulkan, 2020. "A research survey: heuristic approaches for solving multi objective flexible job shop problems," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1949-1983, December.

    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. Alper Türkyılmaz & Özlem Şenvar & İrem Ünal & Serol Bulkan, 2020. "A research survey: heuristic approaches for solving multi objective flexible job shop problems," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1949-1983, December.
    2. Rego, César & Duarte, Renato, 2009. "A filter-and-fan approach to the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 194(3), pages 650-662, May.
    3. Wei Xiong & Dongmei Fu, 2018. "A new immune multi-agent system for the flexible job shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 857-873, April.
    4. G I Zobolas & C D Tarantilis & G Ioannou, 2009. "A hybrid evolutionary algorithm for the job shop scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(2), pages 221-235, February.
    5. Hong-Sen Yan & Wen-Chao Li, 2017. "A multi-objective scheduling algorithm with self-evolutionary feature for job-shop-like knowledgeable manufacturing cell," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 337-351, February.
    6. Nicolás Álvarez-Gil & Rafael Rosillo & David de la Fuente & Raúl Pino, 2021. "A discrete firefly algorithm for solving the flexible job-shop scheduling problem in a make-to-order manufacturing system," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(4), pages 1353-1374, December.
    7. Julien Autuori & Faicel Hnaien & Farouk Yalaoui, 2016. "A mapping technique for better solution exploration: NSGA-II adaptation," Journal of Heuristics, Springer, vol. 22(1), pages 89-123, February.
    8. Zhang, Rui & Chang, Pei-Chann & Wu, Cheng, 2013. "A hybrid genetic algorithm for the job shop scheduling problem with practical considerations for manufacturing costs: Investigations motivated by vehicle production," International Journal of Production Economics, Elsevier, vol. 145(1), pages 38-52.
    9. Yiyi Xu & M’hammed Sahnoun & Fouad Ben Abdelaziz & David Baudry, 2022. "A simulated multi-objective model for flexible job shop transportation scheduling," Annals of Operations Research, Springer, vol. 311(2), pages 899-920, April.
    10. Ho, Nhu Binh & Tay, Joc Cing & Lai, Edmund M.-K., 2007. "An effective architecture for learning and evolving flexible job-shop schedules," European Journal of Operational Research, Elsevier, vol. 179(2), pages 316-333, June.
    11. Miguel A. Fernández Pérez & Fernanda M. P. Raupp, 2016. "A Newton-based heuristic algorithm for multi-objective flexible job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 409-416, April.
    12. Raja Awais Liaqait & Shermeen Hamid & Salman Sagheer Warsi & Azfar Khalid, 2021. "A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    13. Zhang, Sicheng & Li, Xiang & Zhang, Bowen & Wang, Shouyang, 2020. "Multi-objective optimisation in flexible assembly job shop scheduling using a distributed ant colony system," European Journal of Operational Research, Elsevier, vol. 283(2), pages 441-460.
    14. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
    15. Moslehi, Ghasem & Mahnam, Mehdi, 2011. "A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search," International Journal of Production Economics, Elsevier, vol. 129(1), pages 14-22, January.
    16. Fei Luan & Zongyan Cai & Shuqiang Wu & Tianhua Jiang & Fukang Li & Jia Yang, 2019. "Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem," Mathematics, MDPI, vol. 7(5), pages 1-14, April.
    17. Groflin, Heinz & Klinkert, Andreas, 2007. "Feasible insertions in job shop scheduling, short cycles and stable sets," European Journal of Operational Research, Elsevier, vol. 177(2), pages 763-785, March.
    18. Kumar, V.N.S.A. & Kumar, V. & Brady, M. & Garza-Reyes, Jose Arturo & Simpson, M., 2017. "Resolving forward-reverse logistics multi-period model using evolutionary algorithms," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 458-469.
    19. Bingke Yan & Bo Wang & Lin Zhu & Hesen Liu & Yilu Liu & Xingpei Ji & Dichen Liu, 2015. "A Novel, Stable, and Economic Power Sharing Scheme for an Autonomous Microgrid in the Energy Internet," Energies, MDPI, vol. 8(11), pages 1-24, November.
    20. Seyed Habib A. Rahmati & Abbas Ahmadi & Kannan Govindan, 2018. "A novel integrated condition-based maintenance and stochastic flexible job shop scheduling problem: simulation-based optimization approach," Annals of Operations Research, Springer, vol. 269(1), pages 583-621, October.

    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:eee:proeco:v:141:y:2013:i:1:p:87-98. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.