IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v28y2017i2d10.1007_s10845-014-0981-9.html
   My bibliography  Save this article

A multi-objective scheduling algorithm with self-evolutionary feature for job-shop-like knowledgeable manufacturing cell

Author

Listed:
  • Hong-Sen Yan

    (Southeast University)

  • Wen-Chao Li

    (Southeast University
    Jiangsu University)

Abstract

A multi-objective scheduling algorithm with self-evolutionary feature for job-shop-like knowledgeable manufacturing cell (JSKMC) is proposed in this paper, targeting such scheduling issues as make-span, mean complete time of tasks, total tardiness of tasks, number of tardy tasks and the maximum tardiness. Four matrixes are designed to represent the scheduling model of JSKMC. Properties of the key arcs of tasks are discussed and it is found helpless to seek a better solution by reversing the direction of the middle key arcs of tasks. A simplified neighborhood is then established whereby the number of feasible solutions to be searched for is greatly reduced. Based on the above a multi-objective scheduling algorithm with self-evolutionary feature for JSKMC is proposed. Adaptive heuristic critic method is adopted in the algorithm, whose associate search element (ASE) module is designed to select the appropriate action for acquisition of a better solution in the next step by using the knowledge obtained from learning; such an ability of this module can be improved progressively with the increasing training. A scheduling algorithm based on ASE is developed, in which a Pareto archive is embedded to obtain the Pareto optimal solutions. Numerical simulation results confirm the strong ability of the proposed algorithm to home in on the optimal solution by self-evolution via learning.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:2:d:10.1007_s10845-014-0981-9
    DOI: 10.1007/s10845-014-0981-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-014-0981-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-014-0981-9?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. Berkoune, Djamel & Mesghouni, Khaled, 2008. "Resolution approach for multi-objective problems with uncertain demands," European Journal of Operational Research, Elsevier, vol. 187(2), pages 403-414, June.
    2. Zhou, Hong & Cheung, Waiman & Leung, Lawrence C., 2009. "Minimizing weighted tardiness of job-shop scheduling using a hybrid genetic algorithm," European Journal of Operational Research, Elsevier, vol. 194(3), pages 637-649, May.
    3. Yan, Hong-Sen & Ma, Kai-Ping, 2011. "Competitive diffusion process of repurchased products in knowledgeable manufacturing," European Journal of Operational Research, Elsevier, vol. 208(3), pages 243-252, February.
    4. G. M.P. Swann, 2009. "The Economics of Innovation," Books, Edward Elgar Publishing, number 13211.
    5. Peter J. M. van Laarhoven & Emile H. L. Aarts & Jan Karel Lenstra, 1992. "Job Shop Scheduling by Simulated Annealing," Operations Research, INFORMS, vol. 40(1), pages 113-125, February.
    6. Song, Zhe & Kusiak, Andrew, 2010. "Mining Pareto-optimal modules for delayed product differentiation," European Journal of Operational Research, Elsevier, vol. 201(1), pages 123-128, February.
    7. Kusiak, Andrew, 2009. "Innovation: A data-driven approach," International Journal of Production Economics, Elsevier, vol. 122(1), pages 440-448, November.
    8. Michael Pinedo & Marcos Singer, 1999. "A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop," Naval Research Logistics (NRL), John Wiley & Sons, vol. 46(1), pages 1-17, February.
    9. Mattfeld, Dirk C. & Bierwirth, Christian, 2004. "An efficient genetic algorithm for job shop scheduling with tardiness objectives," European Journal of Operational Research, Elsevier, vol. 155(3), pages 616-630, June.
    10. Mati, Yazid & Dauzère-Pérès, Stèphane & Lahlou, Chams, 2011. "A general approach for optimizing regular criteria in the job-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 212(1), pages 33-42, July.
    11. 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.
    12. Loukil, Taicir & Teghem, Jacques & Fortemps, Philippe, 2007. "A multi-objective production scheduling case study solved by simulated annealing," European Journal of Operational Research, Elsevier, vol. 179(3), pages 709-722, June.
    13. M. R. Garey & D. S. Johnson & Ravi Sethi, 1976. "The Complexity of Flowshop and Jobshop Scheduling," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 117-129, May.
    14. 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.
    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.
    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. 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.
    2. Tamssaouet, Karim & Dauzère-Pérès, Stéphane & Knopp, Sebastian & Bitar, Abdoul & Yugma, Claude, 2022. "Multiobjective optimization for complex flexible job-shop scheduling problems," European Journal of Operational Research, Elsevier, vol. 296(1), pages 87-100.
    3. Hamed Piroozfard & Kuan Yew Wong & Adnan Hassan, 2016. "A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems," Journal of Optimization, Hindawi, vol. 2016, pages 1-13, April.
    4. Bierwirth, C. & Kuhpfahl, J., 2017. "Extended GRASP for the job shop scheduling problem with total weighted tardiness objective," European Journal of Operational Research, Elsevier, vol. 261(3), pages 835-848.
    5. Selcuk Goren & Ihsan Sabuncuoglu & Utku Koc, 2012. "Optimization of schedule stability and efficiency under processing time variability and random machine breakdowns in a job shop environment," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(1), pages 26-38, February.
    6. Bürgy, Reinhard & Bülbül, Kerem, 2018. "The job shop scheduling problem with convex costs," European Journal of Operational Research, Elsevier, vol. 268(1), pages 82-100.
    7. Chiang, Tsung-Che & Fu, Li-Chen, 2009. "Using a family of critical ratio-based approaches to minimize the number of tardy jobs in the job shop with sequence dependent setup times," European Journal of Operational Research, Elsevier, vol. 196(1), pages 78-92, July.
    8. 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.
    9. 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.
    10. Pai Zheng & Xun Xu & Chun-Hsien Chen, 2020. "A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 3-18, January.
    11. Zhang, Rui & Song, Shiji & Wu, Cheng, 2013. "A hybrid artificial bee colony algorithm for the job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 141(1), pages 167-178.
    12. Z C Zhu & K M Ng & H L Ong, 2010. "A modified tabu search algorithm for cost-based job shop problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(4), pages 611-619, April.
    13. 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.
    14. Tamssaouet, Karim & Dauzère-Pérès, Stéphane, 2023. "A general efficient neighborhood structure framework for the job-shop and flexible job-shop scheduling problems," European Journal of Operational Research, Elsevier, vol. 311(2), pages 455-471.
    15. Yannik Zeiträg & José Rui Figueira, 2023. "Automatically evolving preference-based dispatching rules for multi-objective job shop scheduling," Journal of Scheduling, Springer, vol. 26(3), pages 289-314, June.
    16. Ahmadian, Mohammad Mahdi & Salehipour, Amir & Cheng, T.C.E., 2021. "A meta-heuristic to solve the just-in-time job-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 288(1), pages 14-29.
    17. Da Col, Giacomo & Teppan, Erich C., 2022. "Industrial-size job shop scheduling with constraint programming," Operations Research Perspectives, Elsevier, vol. 9(C).
    18. 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.
    19. Liang Hou & Roger J. Jiao, 2020. "Data-informed inverse design by product usage information: a review, framework and outlook," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 529-552, March.
    20. Pannee Suanpang & Pitchaya Jamjuntr & Kittisak Jermsittiparsert & Phuripoj Kaewyong, 2022. "Tourism Service Scheduling in Smart City Based on Hybrid Genetic Algorithm Simulated Annealing Algorithm," Sustainability, MDPI, vol. 14(23), pages 1-21, 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:spr:joinma:v:28:y:2017:i:2:d:10.1007_s10845-014-0981-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.