IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v243y2022ics0925527321003182.html
   My bibliography  Save this article

A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems

Author

Listed:
  • Braune, Roland
  • Benda, Frank
  • Doerner, Karl F.
  • Hartl, Richard F.

Abstract

This paper deals with a Genetic Programming (GP) approach for solving flexible shop scheduling problems. The adopted approach aims to generate priority rules in the form of an expression tree for dispatching jobs. Therefore, in a list-scheduling algorithm, the available jobs can be ranked using the tree-based priority rules generated using GP.

Suggested Citation

  • Braune, Roland & Benda, Frank & Doerner, Karl F. & Hartl, Richard F., 2022. "A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems," International Journal of Production Economics, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:proeco:v:243:y:2022:i:c:s0925527321003182
    DOI: 10.1016/j.ijpe.2021.108342
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527321003182
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2021.108342?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. Pickardt, Christoph W. & Hildebrandt, Torsten & Branke, Jürgen & Heger, Jens & Scholz-Reiter, Bernd, 2013. "Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems," International Journal of Production Economics, Elsevier, vol. 145(1), pages 67-77.
    2. Yong Zhou & Jian-jun Yang & Zhuang Huang, 2020. "Automatic design of scheduling policies for dynamic flexible job shop scheduling via surrogate-assisted cooperative co-evolution genetic programming," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2561-2580, May.
    3. Stéphane Dauzère-Pérès & Jan Paulli, 1997. "An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search," Annals of Operations Research, Springer, vol. 70(0), pages 281-306, April.
    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. Frank Benda & Roland Braune & Karl F. Doerner & Richard F. Hartl, 2019. "A machine learning approach for flow shop scheduling problems with alternative resources, sequence-dependent setup times, and blocking," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(4), pages 871-893, December.
    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. Balwin Bokor & Klaus Altendorfer & Andrea Matta, 2025. "Optimizing Energy Consumption in Stochastic Production Systems: Using a Simulation-Based Approach for Stopping Policy," Papers 2505.11536, arXiv.org.

    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. Fei Luan & Zongyan Cai & Shuqiang Wu & Shi Qiang Liu & Yixin He, 2019. "Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm," Mathematics, MDPI, vol. 7(8), pages 1-17, August.
    2. Dauzère-Pérès, Stéphane & Ding, Junwen & Shen, Liji & Tamssaouet, Karim, 2024. "The flexible job shop scheduling problem: A review," European Journal of Operational Research, Elsevier, vol. 314(2), pages 409-432.
    3. 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.
    4. Lunardi, Willian T. & Birgin, Ernesto G. & Ronconi, Débora P. & Voos, Holger, 2021. "Metaheuristics for the online printing shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 293(2), pages 419-441.
    5. Jose L. Andrade-Pineda & David Canca & Pedro L. Gonzalez-R & M. Calle, 2020. "Scheduling a dual-resource flexible job shop with makespan and due date-related criteria," Annals of Operations Research, Springer, vol. 291(1), pages 5-35, August.
    6. 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.
    7. Marko Ɖurasević & Domagoj Jakobović, 2019. "Creating dispatching rules by simple ensemble combination," Journal of Heuristics, Springer, vol. 25(6), pages 959-1013, December.
    8. Dung-Ying Lin & Tzu-Yun Huang, 2021. "A Hybrid Metaheuristic for the Unrelated Parallel Machine Scheduling Problem," Mathematics, MDPI, vol. 9(7), pages 1-20, April.
    9. Shen, Liji & Buscher, Udo, 2012. "Solving the serial batching problem in job shop manufacturing systems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 14-26.
    10. Lingxuan Liu & Leyuan Shi, 2019. "Simulation Optimization on Complex Job Shop Scheduling with Non-Identical Job Sizes," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(05), pages 1-26, October.
    11. Lu Sun & Lin Lin & Haojie Li & Mitsuo Gen, 2019. "Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling," Mathematics, MDPI, vol. 7(4), pages 1-20, March.
    12. An, Youjun & Chen, Xiaohui & Hu, Jiawen & Zhang, Lin & Li, Yinghe & Jiang, Junwei, 2022. "Joint optimization of preventive maintenance and production rescheduling with new machine insertion and processing speed selection," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    13. 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.
    14. 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.
    15. 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.
    16. James C. Chen & Tzu-Li Chen & Ping-Hsuan Wu, 2024. "Truck scheduling with fixed outbound departures in a closed-loop conveyor system with shortcuts," Flexible Services and Manufacturing Journal, Springer, vol. 36(3), pages 1107-1156, September.
    17. Alvarez-Valdes, R. & Fuertes, A. & Tamarit, J. M. & Gimenez, G. & Ramos, R., 2005. "A heuristic to schedule flexible job-shop in a glass factory," European Journal of Operational Research, Elsevier, vol. 165(2), pages 525-534, September.
    18. Xiaoqiu Shi & Wei Long & Yanyan Li & Dingshan Deng, 2020. "Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-23, May.
    19. Pham, Dinh-Nguyen & Klinkert, Andreas, 2008. "Surgical case scheduling as a generalized job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1011-1025, March.
    20. 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.

    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:243:y:2022:i:c:s0925527321003182. 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.