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 Gutierrez-Alcoba, Rene Haijema

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

This paper studies the computation of order up to levels for
a stochastic programming inventory problem of a perishable product.
Finding a solution is a challenge as the problem enhances a perishable
product, xed ordering cost and non-stationary stochastic demand with
a service level constraint. An earlier study [6] 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 publicationProceedings of the 6th International Conference on Computational Logistics (ICCL’15)
PublisherSpringer-Verlag
Pages526-540
DOIs
Publication statusPublished - 2015
Event6th International Conference on Computational Logistics (ICCL’15) - Delft, Netherlands
Duration: 23 Sep 201525 Sep 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag

Conference

Conference6th International Conference on Computational Logistics (ICCL’15)
CountryNetherlands
CityDelft
Period23/09/1525/09/15

Keywords

  • inventory control
  • perishable products
  • MINLP
  • chance constraint
  • Monte Carlo

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