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A multi-agent based approach to dynamic scheduling with flexible processing capabilities

Author

Listed:
  • Cenk Sahin

    (Cukurova University)

  • Melek Demirtas

    (Cukurova University)

  • Rizvan Erol

    (Cukurova University)

  • Adil Baykasoğlu

    (Dokuz Eylul University)

  • Vahit Kaplanoğlu

    (Gaziantep University)

Abstract

A multi-agent based system is proposed to simultaneous scheduling of flexible machine groups and material handling system working under a manufacturing dynamic environment. The proposed model is designed by means of $$\hbox {Prometheus}^{\mathrm{TM}}$$ Prometheus TM methodology and programmed in $$\hbox {JACK}^{\mathrm{TM}}$$ JACK TM agent based systems development environment. Each agent in the model is autonomous and has an ability to cooperate and negotiate with the other agents in the system. Due to these abilities of agents, the structure of the system is more suitable to handle dynamic events. The proposed dynamic scheduling system is tested on several test problems the literature and the results are quite satisfactory because it generates effective schedules for both dynamic cases in the real time and static problem sets. Although the model is designed as an online method and has a dynamic structure, obtained schedule performance parameters are very close to those obtained from offline optimization based algorithms.

Suggested Citation

  • Cenk Sahin & Melek Demirtas & Rizvan Erol & Adil Baykasoğlu & Vahit Kaplanoğlu, 2017. "A multi-agent based approach to dynamic scheduling with flexible processing capabilities," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1827-1845, December.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:8:d:10.1007_s10845-015-1069-x
    DOI: 10.1007/s10845-015-1069-x
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    References listed on IDEAS

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    1. 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.
    2. Wahab, M.I.M. & Wu, Desheng & Lee, Chi-Guhn, 2008. "A generic approach to measuring the machine flexibility of manufacturing systems," European Journal of Operational Research, Elsevier, vol. 186(1), pages 137-149, April.
    3. Vinod, V. & Sridharan, R., 2011. "Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system," International Journal of Production Economics, Elsevier, vol. 129(1), pages 127-146, January.
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    Cited by:

    1. Satheeshkumar Veeramani & Sreekumar Muthuswamy & Keerthi Sagar & Matteo Zoppi, 2020. "Artificial intelligence planners for multi-head path planning of SwarmItFIX agents," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 815-832, April.
    2. Ping Lou & Yutong Zhong & Jiwei Hu & Chuannian Fan & Xiao Chen, 2023. "Digital-Twin-Driven AGV Scheduling and Routing in Automated Container Terminals," Mathematics, MDPI, vol. 11(12), pages 1-25, June.
    3. Anran Zhao & Peng Liu & Xiyu Gao & Guotai Huang & Xiuguang Yang & Yuan Ma & Zheyu Xie & Yunfeng Li, 2022. "Data-Mining-Based Real-Time Optimization of the Job Shop Scheduling Problem," Mathematics, MDPI, vol. 10(23), pages 1-30, December.
    4. Alper Türkyılmaz & Özlem Şenvar & İrem Ünal & Serol Bulkan, 2020. "A research survey: heuristic approaches for solving multi objective flexible job shop problems," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1949-1983, December.

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