A reactive hybrid product-driven system for rescheduling in a manufacturing planning
DOI:
https://doi.org/10.14488/BJOPM.2570.2025Keywords:
Simulation, Scheduling, Flexible manufacturing, Planning, Agent based modelling and simulationAbstract
Goal: This research aims to develop a novel scheduling model integrating the intelligent product paradigm of a product-driven system (PDS) with the shifting bottleneck heuristic (SBH) to enhance rescheduling efficiency and adaptability in dynamic job shop scheduling problems with disruptions (JSSP-D).
Design / Methodology / Approach: The model employs agent-based modeling, where products act as autonomous agents in rescheduling decisions. Simulations covered 151 scenarios across 14 benchmark instances of machine failures, with production time increases of 100%, 200%, and 300%. The model’s performance was evaluated on its ability to minimize makespan deterioration and maintain efficiency under different disturbance levels.
Results: The PDS-SBH model effectively reduced production efficiency gaps, achieving an average makespan reduction of 7.81%, with peaks of 36.06%. Higher disturbance levels allowed for better rescheduling outcomes, albeit with increased variability. The model’s adaptability provided solutions comparable or superior to stable scheduling benchmarks.
Limitations of the investigation: The study used 14 benchmark instances and focused solely on machine failures, limiting generalizability to other disruptions like resource shortages or order changes. Despite these constraints, the 151 scenarios and rigorous analysis strengthen result reliability.
Practical implications: The PDS-SBH model offers a robust approach for real-time schedule adjustments, maintaining operational continuity, and optimizing resource use. It provides practical insights for decision support systems and policy development in dynamic manufacturing.
Originality / Value: This study pioneers a hybrid approach combining intelligent product paradigms with SBH. It advances JSSP-D research by presenting a resilient, adaptive framework for dynamic scheduling, significantly contributing to manufacturing efficiency and robustness.
Downloads
References
Bhongade, A.S., Khodke, P.M., Rehman, A.U., Nikam, M.D., Patil, P.D. and Suryavanshi, P. (2023), "Managing Disruptions in a Flow-Shop Manufacturing System", Mathematics, Vol. 11, No. 7. Doi: https://doi.org/10.3390/math11071731
Bożek, A. and Werner, F. (2018), "Flexible job shop scheduling with lot streaming and sublot size optimisation", International Journal of Production Research, Vol. 56, No. 19, pp. 6391–6411. Doi: https://doi.org/10.1080/00207543.2017.1346322
Campos, J.T. de G.A.e.A., Blumelova, J., Lepikson, H.A. and Freires, F.G.M. (2020), "Agent-based dynamic scheduling model for product-driven production", Brazilian Journal of Operations & Production Management, Vol. 17, No. 4, pp. 1–10. Doi: https://doi.org/10.14488/bjopm.2020.044
Cui, W.W. and Lu, Z. (2017), "Minimizing the makespan on a single machine with flexible maintenances and jobs’ release dates", Computers and Operations Research, Vol. 80, pp. 11–22. Doi: https://doi.org/10.1016/j.cor.2016.11.008
Fowler, J.W. and Mönch, L. (2022), "A survey of scheduling with parallel batch (p-batch) processing", European Journal of Operational Research, Vol. 298, No. 1, pp. 1–24. Doi: https://doi.org/10.1016/j.ejor.2021.06.012
Gao, K., Yang, F., Li, J., Sang, H. and Luo, J. (2020), "Improved Jaya Algorithm for Flexible Job Shop Rescheduling Problem", IEEE Access, Vol. 8, pp. 86915–86922. Doi: https://doi.org/10.1109/ACCESS.2020.2992478
Kim, Y.I. and Kim, H.J. (2021), "Rescheduling of unrelated parallel machines with job-dependent setup times under forecasted machine breakdown", International Journal of Production Research, Vol. 59, No. 17, pp. 5236–5258. Doi: https://doi.org/10.1080/00207543.2020.1775910
Ku, W.Y. and Beck, J.C. (2016), "Mixed Integer Programming models for job shop scheduling: A computational analysis", Computers and Operations Research, Vol. 73, pp. 165–173. Doi: https://doi.org/10.1016/j.cor.2016.04.006
Li, X., Peng, Z., Du, B., Guo, J., Xu, W. and Zhuang, K. (2017), "Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems", Computers and Industrial Engineering, Vol. 113, pp. 10–26. Doi: https://doi.org/10.1016/j.cie.2017.09.005
Lin, P.C. and Uzsoy, R. (2016), "Chance-constrained formulations in rolling horizon production planning: an experimental study", International Journal of Production Research, Vol. 54, No. 13, pp. 3927–3942. Doi: https://doi.org/10.1080/00207543.2016.1165356
Liu, R., Piplani, R. and Toro, C. (2022), "Deep reinforcement learning for dynamic scheduling of a flexible job shop", International Journal of Production Research, Vol. 60, No. 13, pp. 4049–4069. Doi: https://doi.org/10.1080/00207543.2022.2058432
Mahmoodjanloo, M., Tavakkoli-Moghaddama, R., Baboli, A. and Bozorgi-Amiri, A. (2022), "Distributed job-shop rescheduling problem considering reconfigurability of machines: a self-adaptive hybrid equilibrium optimiser", International Journal of Production Research, Vol. 60, No. 16, pp. 4973–4994. Doi: https://doi.org/10.1080/00207543.2021.1946193
Mehrdad, P., Delgoshaei, A. and Ali, A. (2021), "A multi-objective scheduling algorithm for multi-mode resource constrained projects in the presence of uncertain resource availability", Brazilian Journal of Operations & Production Management, Vol. 18, No. 1, e2021942. Doi: https://doi.org/10.14488/BJOPM.2021.007
Meyer, G.G., Wortmann, J.C.H. and Szirbik, N.B. (2011), "Production monitoring and control with intelligent products", International Journal of Production Research, Vol. 49, No. 5, pp. 1303–1317. Doi: https://doi.org/10.1080/00207543.2010.518742
Mönch, L., Schabacker, R., Pabst, D. and Fowler, J.W. (2007), "Genetic algorithm-based subproblem solution procedures for a modified shifting bottleneck heuristic for complex job shops", European Journal of Operational Research, Vol. 177, No. 3, pp. 2100–2118. Doi: https://doi.org/10.1016/j.ejor.2005.12.020
Muhuri, P.K. and Biswas, S.K. (2020), "Bayesian optimization algorithm for multi-objective scheduling of time and precedence constrained tasks in heterogeneous multiprocessor systems", Applied Soft Computing Journal, Vol. 92, p. 106274. Doi: https://doi.org/10.1016/j.asoc.2020.106274
Pu, Y., Li, F. and Rahimifard, S. (2024), "Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments", Sustainability, Vol. 16, No. 8, p. 3234. Doi: https://doi.org/10.3390/su16083234
Rasheed, M.B., Javaid, N., Malik, M.S.A., Asif, M., Hanif, M.K. and Chaudary, M.H. (2019), "Intelligent Multi-Agent Based Multilayered Control System for Opportunistic Load Scheduling in Smart Buildings", IEEE Access, Vol. 7, pp. 23990–24006. Doi: https://doi.org/10.1109/ACCESS.2019.2900049
Sáez, P., Herrera, C., Booth, C., Belmokhtar-Berraf, S. and Parada, V. (2023), "A product-driven system with an evolutionary algorithm to increase flexibility in planning a job shop", PLoS ONE, Vol. 18, No. 2, pp. 1–12. Doi: https://doi.org/10.1371/journal.pone.0281807
Sahin, F., Narayanan, A. and Robinson, E.P. (2013), "Rolling horizon planning in supply chains: Review, implications and directions for future research", International Journal of Production Research, Vol. 51, No. 18, pp. 5413–5436. Doi: https://doi.org/10.1080/00207543.2013.775523
Salido, M.A., Escamilla, J., Barber, F. and Giret, A. (2017), "Rescheduling in job-shop problems for sustainable manufacturing systems", Journal of Cleaner Production, Vol. 162, pp. S121–S132. Doi: https://doi.org/10.1016/j.jclepro.2016.11.002
Shukla, O.J., Soni, G., Kumar, R., Sujil, A. and Prakash, S. (2019), "Harmony Search and Nature Inspired Optimization Algorithms", in Lecture Notes in Electrical Engineering, Vol. 741, pp. 751–760. Doi: https://doi.org/10.1007/978-981-13-0761-4
Wu, C.-C., Chen, J.-Y., Lin, W.-C., Lai, K., Bai, D. and Lai, S.-Y. (2019), "A two-stage three-machine assembly scheduling flowshop problem with both two-agent and learning phenomenon", Computers and Industrial Engineering, Vol. 130, pp. 485–499. Doi: https://doi.org/10.1016/j.cie.2019.02.047
Yadav, A. and Jayswal, S.C. (2018), "Evaluation of batching and layout on the performance of flexible manufacturing system", International Journal of Advanced Manufacturing Technology. Doi: https://doi.org/10.1007/s00170-018-2999-1
Zhang, S., Xiang, L., Bowen, Z. and Shouyang, W. (2020), "Multi-objective optimisation in flexible assembly job shop scheduling using a distributed ant colony system", European Journal of Operational Research, Vol. 283, No. 2, pp. 441–460. Doi: https://doi.org/10.1016/j.ejor.2019.11.016
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Patricio Sáez Bustos, Victor Parada
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors must have a written permission from any third-party materials used in the article, such as figures and graphics. The permission must explicitly allow authors to use the materials. The permission should be submitted with the article, as a supplementary file.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after BJO&PM publishes it (See The Effect of Open Access).
