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Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles

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  • Lacomme, Philippe
  • Larabi, Mohand
  • Tchernev, Nikolay

Abstract

This paper deals with the problem of simultaneous scheduling of machines and identical automated guided vehicles (AGVs) which are well known difficult to solve problems. The studied problem can be modelled as a job shop where the jobs have to be transported between machines by AGVs. This article introduces a framework based on a disjunctive graph to modelize the joint scheduling problem and on a memetic algorithm for machines and AGVs scheduling. The objective is to minimize the makespan. Computational results are presented for a benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

Suggested Citation

  • Lacomme, Philippe & Larabi, Mohand & Tchernev, Nikolay, 2013. "Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles," International Journal of Production Economics, Elsevier, vol. 143(1), pages 24-34.
    • Handle: RePEc:eee:proeco:v:143:y:2013:i:1:p:24-34
    DOI: 10.1016/j.ijpe.2010.07.012
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    References listed on IDEAS

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    1. Caumond, A. & Lacomme, P. & Moukrim, A. & Tchernev, N., 2009. "An MILP for scheduling problems in an FMS with one vehicle," European Journal of Operational Research, Elsevier, vol. 199(3), pages 706-722, December.
    2. Ümit Bilge & Gündüz Ulusoy, 1995. "A Time Window Approach to Simultaneous Scheduling of Machines and Material Handling System in an FMS," Operations Research, INFORMS, vol. 43(6), pages 1058-1070, December.
    3. Hurink, Johann & Knust, Sigrid, 2005. "Tabu search algorithms for job-shop problems with a single transport robot," European Journal of Operational Research, Elsevier, vol. 162(1), pages 99-111, April.
    4. ElMekkawy, T.Y. & Liu, S., 2009. "A new memetic algorithm for optimizing the partitioning problem of tandem AGV systems," International Journal of Production Economics, Elsevier, vol. 118(2), pages 508-520, April.
    5. Soylu, Mustafa & Ozdemirel, Nur E. & Kayaligil, Sinan, 2000. "A self-organizing neural network approach for the single AGV routing problem," European Journal of Operational Research, Elsevier, vol. 121(1), pages 124-137, February.
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    Citations

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    Cited by:

    1. Li, Guo & Li, Na & Sambandam, Narayanasamy & Sethi, Suresh P. & Zhang, Faping, 2018. "Flow shop scheduling with jobs arriving at different times," International Journal of Production Economics, Elsevier, vol. 206(C), pages 250-260.
    2. Jianxun Li & Wenjie Cheng & Kin Keung Lai & Bhagwat Ram, 2022. "Multi-AGV Flexible Manufacturing Cell Scheduling Considering Charging," Mathematics, MDPI, vol. 10(19), pages 1-15, September.
    3. James T. Lin & Chun-Chih Chiu & Edward Huang & Hung-Ming Chen, 2018. "A Multi-Fidelity Model Approach for Simultaneous Scheduling of Machines and Vehicles in Flexible Manufacturing Systems," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(01), pages 1-20, February.
    4. Min Dai & Ziwei Zhang & Adriana Giret & Miguel A. Salido, 2019. "An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints," Sustainability, MDPI, vol. 11(11), pages 1-23, May.
    5. Torres, Isidro Ramos & Romero Dessens, Luis Felipe & Martínez Flores, José Luis & Olivares Benítez, Elías, 2015. "Review of Comprehensive Approaches in Optimizing AGV Systems," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Blecker, Thorsten & Kersten, Wolfgang & Ringle, Christian M. (ed.), Operational Excellence in Logistics and Supply Chains: Optimization Methods, Data-driven Approaches and Security Insights. Proceedings of the Hamburg , volume 22, pages 203-232, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    6. 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.
    7. Marie-Laure Espinouse & Grzegorz Pawlak & Malgorzata Sterna, 2017. "Complexity of Scheduling Problem in Single-Machine Flexible Manufacturing System with Cyclic Transportation and Unlimited Buffers," Journal of Optimization Theory and Applications, Springer, vol. 173(3), pages 1042-1054, June.
    8. Quang-Vinh Dang & Cong Thanh Nguyen & Hana Rudová, 2019. "Scheduling of mobile robots for transportation and manufacturing tasks," Journal of Heuristics, Springer, vol. 25(2), pages 175-213, April.
    9. Dalila B. M. M. Fontes & Seyed Mahdi Homayouni, 2019. "Joint production and transportation scheduling in flexible manufacturing systems," Journal of Global Optimization, Springer, vol. 74(4), pages 879-908, August.
    10. Hosseini, Amir & Otto, Alena & Pesch, Erwin, 2024. "Scheduling in manufacturing with transportation: Classification and solution techniques," European Journal of Operational Research, Elsevier, vol. 315(3), pages 821-843.
    11. Olatunde T. Baruwa & Miquel A. Piera, 2016. "A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4773-4792, August.
    12. James T. Lin & Chun-Chih Chiu & Yu-Hsiang Chang, 2019. "Simulation-based optimization approach for simultaneous scheduling of vehicles and machines with processing time uncertainty in FMS," Flexible Services and Manufacturing Journal, Springer, vol. 31(1), pages 104-141, March.
    13. Fragapane, Giuseppe & de Koster, René & Sgarbossa, Fabio & Strandhagen, Jan Ola, 2021. "Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda," European Journal of Operational Research, Elsevier, vol. 294(2), pages 405-426.
    14. Philippe Lacomme & Aziz Moukrim & Alain Quilliot & Marina Vinot, 2019. "Integration of routing into a resource-constrained project scheduling problem," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 7(4), pages 421-464, December.
    15. Moussa Abderrahim & Abdelghani Bekrar & Damien Trentesaux & Nassima Aissani & Karim Bouamrane, 2020. "Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints," Energies, MDPI, vol. 13(18), pages 1-19, September.
    16. Cai, Wei & Wang, Lianguo & Li, Li & Xie, Jun & Jia, Shun & Zhang, Xugang & Jiang, Zhigang & Lai, Kee-hung, 2022. "A review on methods of energy performance improvement towards sustainable manufacturing from perspectives of energy monitoring, evaluation, optimization and benchmarking," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    17. Fontes, Dalila B.M.M. & Homayouni, S. Mahdi & Gonçalves, José F., 2023. "A hybrid particle swarm optimization and simulated annealing algorithm for the job shop scheduling problem with transport resources," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1140-1157.
    18. Wenxiang Xu & Shunsheng Guo, 2019. "A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode," Sustainability, MDPI, vol. 11(5), pages 1-24, March.
    19. Arash Amirteimoori & Reza Kia, 2023. "Concurrent scheduling of jobs and AGVs in a flexible job shop system: a parallel hybrid PSO-GA meta-heuristic," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 727-753, September.
    20. Sunil Prayagi & Padma Lalitha Mareddy & Lakshmi Narasimhamu Katta & Sivarami Reddy Narapureddy, 2023. "Optimum Scheduling of a Multi-Machine Flexible Manufacturing System Considering Job and Tool Transfer Times without Tool Delay," Mathematics, MDPI, vol. 11(19), pages 1-37, October.

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