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Simulation-based optimization methods for setting production planning parameters

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  • Gansterer, Margaretha
  • Almeder, Christian
  • Hartl, Richard F.

Abstract

This paper refers to a hierarchical production planning system in a make-to-order environment. A challenging task in this context is to determine good production parameter settings in order to benefit from established planning methods. We present a framework for hierarchical production planning which we use to identify good settings for three planning parameters, namely planned leadtimes, safety stock, and lotsizes. Within a discrete-event simulation which mimics the production system we use a mathematical optimization model for replicating the decision problem. This mathematical model is solved to optimality using a standard optimization engine. We use data referring to four different demand market situations in order to derive general statements concerning the quality and sensitivity of the three analyzed planning parameters.

Suggested Citation

  • Gansterer, Margaretha & Almeder, Christian & Hartl, Richard F., 2014. "Simulation-based optimization methods for setting production planning parameters," International Journal of Production Economics, Elsevier, vol. 151(C), pages 206-213.
    • Handle: RePEc:eee:proeco:v:151:y:2014:i:c:p:206-213
    DOI: 10.1016/j.ijpe.2013.10.016
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    5. Diaz, Juan Esteban & Handl, Julia & Xu, Dong-Ling, 2018. "Integrating meta-heuristics, simulation and exact techniques for production planning of a failure-prone manufacturing system," European Journal of Operational Research, Elsevier, vol. 266(3), pages 976-989.
    6. Fahad Kh. A.O.H. Alazemi & Mohd Khairol Anuar Bin Mohd Ariffin & Faizal Bin Mustapha & Eris Elianddy bin Supeni, 2021. "A Comprehensive Fuzzy Decision-Making Method for Minimizing Completion Time in Manufacturing Process in Supply Chains," Mathematics, MDPI, vol. 9(22), pages 1-39, November.
    7. Barros, Júlio & Cortez, Paulo & Carvalho, M. Sameiro, 2021. "A systematic literature review about dimensioning safety stock under uncertainties and risks in the procurement process," Operations Research Perspectives, Elsevier, vol. 8(C).
    8. Neto, Anis Assad & Ribeiro da Silva, Elias & Deschamps, Fernando & do Nascimento Junior, Laercio Alves & Pinheiro de Lima, Edson, 2023. "Modeling production disorder: Procedures for digital twins of flexibility-driven manufacturing systems," International Journal of Production Economics, Elsevier, vol. 260(C).
    9. Shishvan, Masoud Soleymani & Benndorf, Jörg, 2019. "Simulation-based optimization approach for material dispatching in continuous mining systems," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1108-1125.
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    11. Gonçalves, João N.C. & Sameiro Carvalho, M. & Cortez, Paulo, 2020. "Operations research models and methods for safety stock determination: A review," Operations Research Perspectives, Elsevier, vol. 7(C).

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