IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v316y2024i2p569-583.html
   My bibliography  Save this article

Enhancing stability and robustness in online machine shop scheduling: A multi-agent system and negotiation-based approach for handling machine downtime in industry 4.0

Author

Listed:
  • Didden, Jeroen B.H.C.
  • Dang, Quang-Vinh
  • Adan, Ivo J.B.F.

Abstract

Autonomous factories require high levels of adaptability, flexibility, and resilience to react to uncertainties on the shop floor, such as machine downtime. This paper proposes a negotiation-based, partial rescheduling method, combined with an existing multi-agent system, to swap jobs between machines. The negotiations are restricted to machines within the same work center, giving rise to a partial reschedule. A learning algorithm is also utilized, allowing machines to individually learn how to evaluate proposed bids from other machines and adapt the bids to their current environment. The main objective is to minimize the mean weighted tardiness of all jobs. Computational results indicate an improvement of 10–30 tardiness, compared to continuous rescheduling and complete rescheduling methods. In addition, a decrease of 70–80 sensitivity analysis and analysis of the partial reschedule.

Suggested Citation

  • Didden, Jeroen B.H.C. & Dang, Quang-Vinh & Adan, Ivo J.B.F., 2024. "Enhancing stability and robustness in online machine shop scheduling: A multi-agent system and negotiation-based approach for handling machine downtime in industry 4.0," European Journal of Operational Research, Elsevier, vol. 316(2), pages 569-583.
  • Handle: RePEc:eee:ejores:v:316:y:2024:i:2:p:569-583
    DOI: 10.1016/j.ejor.2024.02.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221724000973
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2024.02.006?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. Sicheng Zhang & Tak Nam Wong, 2017. "Flexible job-shop scheduling/rescheduling in dynamic environment: a hybrid MAS/ACO approach," International Journal of Production Research, Taylor & Francis Journals, vol. 55(11), pages 3173-3196, June.
    2. Aytug, Haldun & Lawley, Mark A. & McKay, Kenneth & Mohan, Shantha & Uzsoy, Reha, 2005. "Executing production schedules in the face of uncertainties: A review and some future directions," European Journal of Operational Research, Elsevier, vol. 161(1), pages 86-110, February.
    3. Young-In Kim & Hyun-Jung Kim, 2021. "Rescheduling of unrelated parallel machines with job-dependent setup times under forecasted machine breakdown," International Journal of Production Research, Taylor & Francis Journals, vol. 59(17), pages 5236-5258, September.
    4. Maroua Nouiri & Abdelghani Bekrar & Damien Trentesaux, 2020. "An energy-efficient scheduling and rescheduling method for production and logistics systems†," International Journal of Production Research, Taylor & Francis Journals, vol. 58(11), pages 3263-3283, June.
    5. Xiong, Jian & Xing, Li-ning & Chen, Ying-wu, 2013. "Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns," International Journal of Production Economics, Elsevier, vol. 141(1), pages 112-126.
    6. Lei Shi & Gang Guo & Xiaohui Song, 2021. "Multi-agent based dynamic scheduling optimisation of the sustainable hybrid flow shop in a ubiquitous environment," International Journal of Production Research, Taylor & Francis Journals, vol. 59(2), pages 576-597, January.
    7. 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.
    8. Abderraouf Maoudj & Brahim Bouzouia & Abdelfetah Hentout & Ahmed Kouider & Redouane Toumi, 2019. "Distributed multi-agent scheduling and control system for robotic flexible assembly cells," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1629-1644, April.
    9. Yin, Yunqiang & Cheng, T.C.E. & Wang, Du-Juan, 2016. "Rescheduling on identical parallel machines with machine disruptions to minimize total completion time," European Journal of Operational Research, Elsevier, vol. 252(3), pages 737-749.
    10. Manuel Parente & Gonçalo Figueira & Pedro Amorim & Alexandra Marques, 2020. "Production scheduling in the context of Industry 4.0: review and trends," International Journal of Production Research, Taylor & Francis Journals, vol. 58(17), pages 5401-5431, September.
    11. Zachariah Stevenson & Ricardo Fukasawa & Luis Ricardez-Sandoval, 2020. "Evaluating periodic rescheduling policies using a rolling horizon framework in an industrial-scale multipurpose plant," Journal of Scheduling, Springer, vol. 23(3), pages 397-410, June.
    12. Al-Hinai, Nasr & ElMekkawy, T.Y., 2011. "Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm," International Journal of Production Economics, Elsevier, vol. 132(2), pages 279-291, August.
    13. Pablo Valledor & Alberto Gomez & Paolo Priore & Javier Puente, 2018. "Solving multi-objective rescheduling problems in dynamic permutation flow shop environments with disruptions," International Journal of Production Research, Taylor & Francis Journals, vol. 56(19), pages 6363-6377, October.
    14. Matthias Thürer & Martin J. Land & Mark Stevenson & Lawrence D. Fredendall & Moacir Godinho Filho, 2015. "Concerning Workload Control and Order Release: The Pre-Shop Pool Sequencing Decision," Production and Operations Management, Production and Operations Management Society, vol. 24(7), pages 1179-1192, July.
    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. 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.
    2. Xuan Jing & Xifan Yao & Min Liu & Jiajun Zhou, 2024. "Multi-agent reinforcement learning based on graph convolutional network for flexible job shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 75-93, January.
    3. Shichang Xiao & Shudong Sun & Jionghua (Judy) Jin, 2017. "Surrogate Measures for the Robust Scheduling of Stochastic Job Shop Scheduling Problems," Energies, MDPI, vol. 10(4), pages 1-26, April.
    4. Shichang Xiao & Zigao Wu & Hongyan Dui, 2022. "Resilience-Based Surrogate Robustness Measure and Optimization Method for Robust Job-Shop Scheduling," Mathematics, MDPI, vol. 10(21), pages 1-22, October.
    5. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2022. "Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning," Omega, Elsevier, vol. 111(C).
    6. Zigao Wu & Shaohua Yu & Tiancheng Li, 2019. "A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling," Mathematics, MDPI, vol. 7(6), pages 1-19, June.
    7. Yin, Yunqiang & Luo, Zunhao & Wang, Dujuan & Cheng, T.C.E., 2023. "Wasserstein distance‐based distributionally robust parallel‐machine scheduling," Omega, Elsevier, vol. 120(C).
    8. Wang, Dujuan & Yin, Yunqiang & Cheng, T.C.E., 2018. "Parallel-machine rescheduling with job unavailability and rejection," Omega, Elsevier, vol. 81(C), pages 246-260.
    9. Xiong, Jian & Xing, Li-ning & Chen, Ying-wu, 2013. "Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns," International Journal of Production Economics, Elsevier, vol. 141(1), pages 112-126.
    10. Xingong Zhang & Win-Chin Lin & Chin-Chia Wu, 2022. "Rescheduling problems with allowing for the unexpected new jobs arrival," Journal of Combinatorial Optimization, Springer, vol. 43(3), pages 630-645, April.
    11. Che, Ada & Feng, Jianguang & Chen, Haoxun & Chu, Chengbin, 2015. "Robust optimization for the cyclic hoist scheduling problem," European Journal of Operational Research, Elsevier, vol. 240(3), pages 627-636.
    12. Jain, S. & Foley, W.J., 2016. "Dispatching strategies for managing uncertainties in automated manufacturing systems," European Journal of Operational Research, Elsevier, vol. 248(1), pages 328-341.
    13. Berti, Nicola & Finco, Serena & Battaïa, Olga & Delorme, Xavier, 2021. "Ageing workforce effects in Dual-Resource Constrained job-shop scheduling," International Journal of Production Economics, Elsevier, vol. 237(C).
    14. Wenchang Luo & Taibo Luo & Randy Goebel & Guohui Lin, 2018. "Rescheduling due to machine disruption to minimize the total weighted completion time," Journal of Scheduling, Springer, vol. 21(5), pages 565-578, October.
    15. Olumide Emmanuel Oluyisola & Swapnil Bhalla & Fabio Sgarbossa & Jan Ola Strandhagen, 2022. "Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 311-332, January.
    16. Xiong, Jian & Leus, Roel & Yang, Zhenyu & Abbass, Hussein A., 2016. "Evolutionary multi-objective resource allocation and scheduling in the Chinese navigation satellite system project," European Journal of Operational Research, Elsevier, vol. 251(2), pages 662-675.
    17. Alexey Matveev & Varvara Feoktistova & Ksenia Bolshakova, 2016. "On Global Near Optimality of Special Periodic Protocols for Fluid Polling Systems with Setups," Journal of Optimization Theory and Applications, Springer, vol. 171(3), pages 1055-1070, December.
    18. Faicel Hnaien & Taha Arbaoui, 2023. "Minimizing the makespan for the two-machine flow shop scheduling problem with random breakdown," Annals of Operations Research, Springer, vol. 328(2), pages 1437-1460, September.
    19. Taejong Joo & Hyunyoung Jun & Dongmin Shin, 2022. "Task Allocation in Human–Machine Manufacturing Systems Using Deep Reinforcement Learning," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    20. Lamas, Patricio & Goycoolea, Marcos & Pagnoncelli, Bernardo & Newman, Alexandra, 2024. "A target-time-windows technique for project scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 314(2), pages 792-806.

    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:ejores:v:316:y:2024:i:2:p:569-583. 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/eor .

    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.