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
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DOI: 10.1016/j.ejor.2024.02.006
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- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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Keywords
Multi-agent system; Negotiation-based method; Learning algorithm; Industry 4.0; Smart manufacturing;All these keywords.
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