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A rolling horizon simulation approach for managing demand with lead time variability

Author

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  • Francisco Campuzano-Bolarín
  • Josefa Mula
  • Manuel Díaz-Madroñero
  • Álvar-Ginés Legaz-Aparicio

Abstract

This paper proposes a rolling horizon (RH) approach to deal with management problems under dynamic demand in planning horizons with variable lead times using system dynamics (SD) simulation. Thus, the nature of dynamic RH solutions entails no inconveniences to contemplate planning horizons with unpredictable demands. This is mainly because information is periodically updated and replanning is done in time. Therefore, inventory and logistic costs may be lower. For the first time, an RH is applied for demand management with variable lead times along with SD simulation models, which allowed the use of lot-sizing techniques to be evaluated (Wagner-Whitin and Silver-Meal). The basic scenario is based on a real-world example from an automotive single-level SC composed of a first-tier supplier and a car assembler that contemplates uncertain demands while planning the RH and 216 subscenarios by modifying constant and variable lead times, holding costs and order costs, combined with lot-sizing techniques. Twenty-eight more replications comprising 504 new subscenarios with variable lead times are generated to represent a relative variation coefficient of the initial demand. We conclude that our RH simulation approach, along with lot-sizing techniques, can generate more sustainable planning results in total costs, fill rates and bullwhip effect terms.

Suggested Citation

  • Francisco Campuzano-Bolarín & Josefa Mula & Manuel Díaz-Madroñero & Álvar-Ginés Legaz-Aparicio, 2020. "A rolling horizon simulation approach for managing demand with lead time variability," International Journal of Production Research, Taylor & Francis Journals, vol. 58(12), pages 3800-3820, June.
  • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:12:p:3800-3820
    DOI: 10.1080/00207543.2019.1634849
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    Cited by:

    1. Manuel Schlenkrich & Wolfgang Seiringer & Klaus Altendorfer & Sophie N. Parragh, 2024. "Enhancing Rolling Horizon Production Planning Through Stochastic Optimization Evaluated by Means of Simulation," Papers 2402.14506, arXiv.org.

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