A mathematical programming-based solution method for the nonstationary inventory problem under correlated demand

Xiang M., Rossi R., Martin-Barragan B., TARIM Ş. A.

European Journal of Operational Research, vol.304, no.2, pp.515-524, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 304 Issue: 2
  • Publication Date: 2023
  • Doi Number: 10.1016/j.ejor.2022.04.011
  • Journal Name: European Journal of Operational Research
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, International Bibliography of Social Sciences, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, EconLit, INSPEC, Public Affairs Index, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.515-524
  • Keywords: Correlated demand, Inventory, Martingale model of forecast evolution, Mixed integer linear programming, Stochastic programming
  • Hacettepe University Affiliated: Yes

Abstract

This paper extends the single-item single-stocking location nonstationary stochastic inventory problem to relax the assumption of independent demand. We present a mathematical programming-based solution method built upon an existing piecewise linear approximation strategy under the receding horizon control framework. Our method can be implemented by leveraging off-the-shelf mixed-integer linear programming solvers. It can tackle demand under various assumptions: the multivariate normal distribution, a collection of time-series processes, and the Martingale Model of Forecast Evolution. We compare against exact solutions obtained via stochastic dynamic programming to demonstrate that our method leads to near-optimal plans.