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Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system

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  • He, N.
  • Zhang, D.Z.
  • Li, Q.

Abstract

Nowadays, manufacturing organisations face increasing pressures from the frequent change in product type, continuous demand fluctuation and unexpected change in customer requirements. In order to survive in the turbulent environment, manufacturing organisations must become flexible and responsive to these dynamic changes in the business environment. This paper presents a hierarchical agent bidding mechanism that is particularly designed for Make-to-Order manufacturing system and attempts to enhance the operational flexibility of manufacturing system in dealing with dynamic changes in the business environment. The novelty of this mechanism is that it enables manufacturing resources to be self-organised cost-efficiently within structural constraints of manufacturing system for fulfilling customer orders. However, when orders cannot be fulfilled within the structural constraints of manufacturing systems, the mechanism can enable manufacturing resources to be regrouped flexibly across system boundaries but with minimum disturbances to existing system structure. Based on an example application to a manufacturing company, this paper demonstrates that the operational flexibility provided by this mechanism is able to help manufacturing system to respond demand fluctuation through balancing the capacity across the entire system. Meanwhile, this mechanism potentially enables manufacturing systems to deal with unexpected changes in product type. As long as the manufacturing system has the technicality required by a new product, this mechanism enables resources across the manufacturing system to be cost-efficiently and flexibly self-organised to fulfil the new product.

Suggested Citation

  • He, N. & Zhang, D.Z. & Li, Q., 2014. "Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system," International Journal of Production Economics, Elsevier, vol. 149(C), pages 117-130.
  • Handle: RePEc:eee:proeco:v:149:y:2014:i:c:p:117-130
    DOI: 10.1016/j.ijpe.2013.08.022
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    References listed on IDEAS

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    1. Kathryn E. Stecke, 1983. "Formulation and Solution of Nonlinear Integer Production Planning Problems for Flexible Manufacturing Systems," Management Science, INFORMS, vol. 29(3), pages 273-288, March.
    2. Radu F. Babiceanu & F. Frank Chen, 2007. "Manufacturing scheduling in decentralised holonic systems using artificial intelligence techniques," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 11(3/4), pages 389-410.
    3. Mes, Martijn & van der Heijden, Matthieu & van Harten, Aart, 2007. "Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems," European Journal of Operational Research, Elsevier, vol. 181(1), pages 59-75, August.
    4. Balakrishnan, Jaydeep & Jacobs, F. Robert & Venkataramanan, Munirpallam A., 1992. "Solutions for the constrained dynamic facility layout problem," European Journal of Operational Research, Elsevier, vol. 57(2), pages 280-286, March.
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    Cited by:

    1. Li, Xingyu & Epureanu, Bogdan I., 2020. "An agent-based approach to optimizing modular vehicle fleet operation," International Journal of Production Economics, Elsevier, vol. 228(C).
    2. Son Duy Dao & Kazem Abhary & Romeo Marian, 2018. "An innovative model for resource scheduling in VCIM systems," Operational Research, Springer, vol. 18(1), pages 33-54, April.
    3. Kucukkoc, Ibrahim & Zhang, David Z., 2014. "Mathematical model and agent based solution approach for the simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines," International Journal of Production Economics, Elsevier, vol. 158(C), pages 314-333.
    4. Becker, Till & Illigen, Christoph & McKelvey, Bill & Hülsmann, Michael & Windt, Katja, 2016. "Using an agent-based neural-network computational model to improve product routing in a logistics facility," International Journal of Production Economics, Elsevier, vol. 174(C), pages 156-167.
    5. Guo, Daqiang & Li, Mingxing & Lyu, Zhongyuan & Kang, Kai & Wu, Wei & Zhong, Ray Y. & Huang, George Q., 2021. "Synchroperation in industry 4.0 manufacturing," International Journal of Production Economics, Elsevier, vol. 238(C).
    6. Mohd. Shaaban Hussain & Mohammed Ali, 2019. "A Multi-agent Based Dynamic Scheduling of Flexible Manufacturing Systems," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(3), pages 267-290, September.
    7. Amirhosein Gholami & Nasim Nezamoddini & Mohammad T. Khasawneh, 2023. "Customized orders management in connected make-to-order supply chains," Operations Management Research, Springer, vol. 16(3), pages 1428-1443, September.
    8. Gansterer, Margaretha, 2015. "Aggregate planning and forecasting in make-to-order production systems," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 521-528.
    9. Surbhi Upadhyay & Suresh Kumar Garg & Rishu Sharma, 2023. "Analyzing the Factors for Implementing Make-to-Order Manufacturing System," Sustainability, MDPI, vol. 15(13), pages 1-22, June.

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    Keywords

    Hierarchical planning; Multi-agent system;

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