Publication Type

Journal Article

Version

Postprint

Publication Date

10-2016

Abstract

We propose a target-oriented robust optimization approach to solve a multi-product, multi-period inventory management problem subject to ordering capacity constraints. We assume the demand for each product in each period is characterized by an uncertainty set, which depends only on a reference value and the bounds of the demand. Our goal is to find an ordering policy that maximizes the sizes of all the uncertainty sets such that all demand realizations from the sets will result in a total cost lower than a pre-specified cost target. We prove that a static decision rule is optimal for an approximate formulation of the problem, which significantly reduces the computation burden. By tuning the cost target, the resultant policy can achieve a balance between the expected cost and the associated cost variance. Numerical experiments suggest that, although only limited demand information is used, the proposed approach performs comparably to traditional methods based on dynamic programming and stochastic programming. More importantly, our approach significantly outperforms the traditional methods if the latter assume inaccurate demand distributions. We demonstrate the applicability of our approach through two case studies from different industries.

Keywords

Inventory, Cost, Variability, Lead Time, Robust Optimization, Target

Discipline

Operations and Supply Chain Management

Research Areas

Operations Management

Publication

Management Science

First Page

1

Last Page

20

ISSN

0025-1909

Identifier

10.1287/mnsc.2016.2565

Publisher

INFORMS

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1287/mnsc.2016.2565

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