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Chance-constrained formulations in rolling horizon production planning: an experimental study

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  • Po-Chen Lin
  • Reha Uzsoy

Abstract

Rolling horizon procedures, where an infinite horizon problem is approximated by the solution to a sequence of finite horizon problems, are common in production planning practice and research. However, these procedures also lead to frequent changes in planned release and production quantities, a phenomenon referred to as nervousness. We examine the performance of two chance-constrained production planning models developed for systems with stochastic demand in a rolling horizon environment, and find that these formulations significantly reduce planned release changes (nervousness) while also improving cost and service-level performance.

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  • Po-Chen Lin & Reha Uzsoy, 2016. "Chance-constrained formulations in rolling horizon production planning: an experimental study," International Journal of Production Research, Taylor & Francis Journals, vol. 54(13), pages 3927-3942, July.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:13:p:3927-3942
    DOI: 10.1080/00207543.2016.1165356
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    1. Srinivasan, A. & Carey, M. & Morton, T.E., 1988. "Resource Pricing And Aggregate Scheduling In Manufacturing Systems," GSIA Working Papers 88-89-58, Carnegie Mellon University, Tepper School of Business.
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    Cited by:

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    2. Nasreddine Saadouli, 2021. "Stochastic programming model for production planning with stochastic aggregate demand and spreadsheet-based solution heuristics," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(4), pages 117-127.
    3. Simon Thevenin & Yossiri Adulyasak & Jean‐François Cordeau, 2021. "Material Requirements Planning Under Demand Uncertainty Using Stochastic Optimization," Production and Operations Management, Production and Operations Management Society, vol. 30(2), pages 475-493, February.
    4. Xu, Haoxuan & Gong, Yeming (Yale) & Chu, Chengbin & Zhang, Jinlong, 2017. "Dynamic lot-sizing models for retailers with online channels," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 171-184.
    5. Yazdanparast, R. & Jolai, F. & Pishvaee, M.S. & Keramati, A., 2022. "A resilient drop-in biofuel supply chain integrated with existing petroleum infrastructure: Toward more sustainable transport fuel solutions," Renewable Energy, Elsevier, vol. 184(C), pages 799-819.

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