A sample-based method for perishable good inventory control with a service level constraint

Eligius M.T. Hendrix*, Karin G.J. Pauls-Worm, Roberto Rossi, Alejandro G. Alcoba, Rene Haijema

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper studies the computation of so-called order-upto levels for a stochastic programming inventory problem of a perishable product. Finding a solution is a challenge as the problem enhances a perishable product, fixed ordering cost and non-stationary stochastic demand with a service level constraint. An earlier study [7] derived order-up-to values via an MILP approximation. We consider a computational method based on the so-called Smoothed Monte Carlo method using sampled demand to optimize values. The resulting MINLP approach uses enumeration, bounding and iterative nonlinear optimization.

Original languageEnglish
Title of host publicationComputational Logistics
PublisherSpringer Verlag
Pages526-540
Volume9335
ISBN (Print)9783319242637
DOIs
Publication statusPublished - 2015
Event6th International Conference on Computational Logistics, ICCL 2015 - Delft, Netherlands
Duration: 23 Sep 201525 Sep 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9335
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Computational Logistics, ICCL 2015
CountryNetherlands
CityDelft
Period23/09/1525/09/15

Keywords

  • Chance constraint
  • Inventory control
  • MINLP
  • Monte carlo
  • Perishable products

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    Hendrix, E. M. T., Pauls-Worm, K. G. J., Rossi, R., Alcoba, A. G., & Haijema, R. (2015). A sample-based method for perishable good inventory control with a service level constraint. In Computational Logistics (Vol. 9335, pp. 526-540). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9335). Springer Verlag. https://doi.org/10.1007/978-3-319-24264-4_36