Inventory planning with forecast updates: Approximate solutions and cost error bounds

Published

Journal Article

We consider a finite-horizon, periodic-review inventory model with demand forecasting updates following the martingale model of forecast evolution (MMFE). The optimal policy is a state-dependent base-stock policy, which, however, is computationally intractable to obtain. We develop tractable bounds on the optimal base-stock levels and use them to devise a general class of heuristic solutions. Through this analysis, we identify a necessary and sufficient condition for the myopic policy to be optimal. Finally, to assess the effectiveness of the heuristic policies, we develop upper bounds on their value loss relative to optimal cost. These solution bounds and cost error bounds also work for general dynamic inventory models with nonstationary and autocorrelated demands. Numerical results are presented to illustrate the results. © 2006 INFORMS.

Full Text

Duke Authors

Cited Authors

  • Lu, X; Song, JS; Regan, A

Published Date

  • November 1, 2006

Published In

Volume / Issue

  • 54 / 6

Start / End Page

  • 1079 - 1097

Electronic International Standard Serial Number (EISSN)

  • 1526-5463

International Standard Serial Number (ISSN)

  • 0030-364X

Digital Object Identifier (DOI)

  • 10.1287/opre.1060.0338

Citation Source

  • Scopus