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Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships

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  • Rossi, Andrea

Abstract

This paper proposes a swarm intelligence approach based on a disjunctive graph model in order to schedule a manufacturing system with resource flexibility and separable setup times. Resource flexibility assigns each operation to one of the alternative resources (assigning sub-problem) and, consequently, arranges the operation in the right sequence of the assigned resource (sequencing sub-problem) in order to minimize the makespan. Resource flexibility is mandatory for rescheduling a manufacturing system after unforeseen events which modify resource availability. The proposed method considers parallel (related) machines and enforces in a single step both the assigning and sequencing sub-problems. A neighboring function on the disjunctive graph is enhanced by means of a reinforced relation-learning model of pheromone involving more effective machine-sequence constraints and a dynamic visibility function. It also considers the overlap between the jobs feeding and the machine (anticipatory) setup times. It involves separable sequence-independent and dependent setup phases. The algorithm performance is evaluated by modifying the well-known benchmark problems for job shop scheduling. Comparison with other systems and lower bounds of benchmark problems has been performed. Statistical tests highlight how the approach is very promising. The performance achieved when the system addresses the complete problem is quite close to that obtained in the case of the classical job-shop problem. This fact makes the system effective in coping with the exponential complexity especially for sequence dependent setup times.

Suggested Citation

  • Rossi, Andrea, 2014. "Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships," International Journal of Production Economics, Elsevier, vol. 153(C), pages 253-267.
    • Handle: RePEc:eee:proeco:v:153:y:2014:i:c:p:253-267
    DOI: 10.1016/j.ijpe.2014.03.006
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    2. Liang Tang & Zhihong Jin & Xuwei Qin & Ke Jing, 2019. "Supply chain scheduling in a collaborative manufacturing mode: model construction and algorithm design," Annals of Operations Research, Springer, vol. 275(2), pages 685-714, April.
    3. Wieslaw Kubiak & Yanling Feng & Guo Li & Suresh P. Sethi & Chelliah Sriskandarajah, 2020. "Efficient algorithms for flexible job shop scheduling with parallel machines," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(4), pages 272-288, June.
    4. Dominik Kress & David Müller & Jenny Nossack, 2019. "A worker constrained flexible job shop scheduling problem with sequence-dependent setup times," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(1), pages 179-217, March.
    5. Xiaoyan Li & Xuedong Liang & Zhi Li, 2023. "The Strategy of Strengthening Efficiency and Environmental Performance of Product Changeover in the Multiproduct Production System," SAGE Open, , vol. 13(3), pages 21582440231, September.
    6. Shen, Liji & Dauzère-Pérès, Stéphane & Neufeld, Janis S., 2018. "Solving the flexible job shop scheduling problem with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 265(2), pages 503-516.
    7. Feng, Yanling & Li, Guo & Sethi, Suresh P., 2018. "A three-layer chromosome genetic algorithm for multi-cell scheduling with flexible routes and machine sharing," International Journal of Production Economics, Elsevier, vol. 196(C), pages 269-283.
    8. 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.
    9. Luscombe, Ruth & Kozan, Erhan, 2016. "Dynamic resource allocation to improve emergency department efficiency in real time," European Journal of Operational Research, Elsevier, vol. 255(2), pages 593-603.
    10. Berterottière, Lucas & Dauzère-Pérès, Stéphane & Yugma, Claude, 2024. "Flexible job-shop scheduling with transportation resources," European Journal of Operational Research, Elsevier, vol. 312(3), pages 890-909.
    11. 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).
    12. Xiong, Hegen & Fan, Huali & Jiang, Guozhang & Li, Gongfa, 2017. "A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints," European Journal of Operational Research, Elsevier, vol. 257(1), pages 13-24.
    13. 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.

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