Inventory planning with forecast updates: Approximate solutions and cost error bounds
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.
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Related Subject Headings
- Operations Research
- 3507 Strategy, management and organisational behaviour
- 1503 Business and Management
- 0802 Computation Theory and Mathematics
- 0102 Applied Mathematics
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Operations Research
- 3507 Strategy, management and organisational behaviour
- 1503 Business and Management
- 0802 Computation Theory and Mathematics
- 0102 Applied Mathematics