Robust multi-echelon multi-period inventory control

Aharon Ben-Tal, Boaz Golany, Shimrit Shtern

Research output: Contribution to journalArticlepeer-review

Abstract

We consider the problem of minimizing the overall cost of a supply chain, over a possible long horizon, under demand uncertainly which is known only crudely. Under such circumstances, the method of choice is Robust Optimization, in particular the Affinely Adjustable Robust Counterpart (AARC) method which leads to tractable deterministic optimization problems. The latter is due to a recent re-parametrization technique for discrete time linear control systems. In this paper we model, analyze and test an extension of the AARC method known as the Globalized Robust Counterpart (GRC) in order to control inventories in serial supply chains. A simulation study demonstrates the merit of the methods employed here, in particular, it shows that a good control law that minimizes cost achieves simultaneously good control of the bullwhip effect.

Original languageEnglish
Pages (from-to)922-935
Number of pages14
JournalEuropean Journal of Operational Research
Volume199
Issue number3
DOIs
StatePublished - 16 Dec 2009

Keywords

  • Affinely adjustable robust optimization
  • Bullwhip effect
  • Globalized Robust Counterpart
  • Multi-echelon supply chains
  • Multi-period inventory control
  • Robust Optimization

ASJC Scopus subject areas

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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