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Integration of production planning and scheduling using an expert system and a genetic algorithm

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  • A Ławrynowicz

    (University of Technology and Life Sciences)

Abstract

In the traditional approaches, processes of planning and scheduling are done sequentially, where the process plan is determined before the actual scheduling is performed. This simple approach ignores the relationship between the scheduling and planning. Practical scheduling systems need to be able to react to significant real-time events within an acceptable response time and revise schedules appropriately. Therefore, the author proposes a new methodology with artificial intelligence to support production planning and scheduling in supply net. In this approach, the production planning problem is first solved, and then the scheduling problem is considered with the constraint of the solution. The approach is implemented as a combination of expert system and genetic algorithm. The research indicates that the new system yields better results in real-life supply net than using a traditional method. The results of experiments provide that the proposed genetic algorithm produces schedules with makespan that is average 21% better than the methods based on dispatching rules.

Suggested Citation

  • A Ławrynowicz, 2008. "Integration of production planning and scheduling using an expert system and a genetic algorithm," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 455-463, April.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:4:d:10.1057_palgrave.jors.2602423
    DOI: 10.1057/palgrave.jors.2602423
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    References listed on IDEAS

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    1. Cowling, Peter & Johansson, Marcus, 2002. "Using real time information for effective dynamic scheduling," European Journal of Operational Research, Elsevier, vol. 139(2), pages 230-244, June.
    2. Akkan, Can & Karabati, Selcuk, 2004. "The two-machine flowshop total completion time problem: Improved lower bounds and a branch-and-bound algorithm," European Journal of Operational Research, Elsevier, vol. 159(2), pages 420-429, December.
    3. Della Croce, F. & Ghirardi, M. & Tadei, R., 2002. "An improved branch-and-bound algorithm for the two machine total completion time flow shop problem," European Journal of Operational Research, Elsevier, vol. 139(2), pages 293-301, June.
    4. J M Framinan & J N D Gupta & R Leisten, 2004. "A review and classification of heuristics for permutation flow-shop scheduling with makespan objective," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1243-1255, December.
    5. Blazewicz, Jacek & Domschke, Wolfgang & Pesch, Erwin, 1996. "The job shop scheduling problem: Conventional and new solution techniques," European Journal of Operational Research, Elsevier, vol. 93(1), pages 1-33, August.
    6. Percy, David F. & Kobbacy, Khairy A. H., 2000. "Determining economical maintenance intervals," International Journal of Production Economics, Elsevier, vol. 67(1), pages 87-94, August.
    7. Koh, S.C. Lenny & Saad, Sameh M., 2006. "Managing uncertainty in ERP-controlled manufacturing environments in SMEs," International Journal of Production Economics, Elsevier, vol. 101(1), pages 109-127, May.
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    Cited by:

    1. Toorajipour, Reza & Sohrabpour, Vahid & Nazarpour, Ali & Oghazi, Pejvak & Fischl, Maria, 2021. "Artificial intelligence in supply chain management: A systematic literature review," Journal of Business Research, Elsevier, vol. 122(C), pages 502-517.
    2. Sicheng Zhang & T.N. Wong, 2016. "Studying the impact of sequence-dependent set-up times in integrated process planning and scheduling with E-ACO heuristic," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4815-4838, August.
    3. S. Zhang & T. N. Wong, 2018. "Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 585-601, March.
    4. Ławrynowicz Anna, 2011. "Genetic Algorithms for Solving Scheduling Problems in Manufacturing Systems," Foundations of Management, Sciendo, vol. 3(2), pages 7-26, January.

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