Heuristics for Inventory Systems Based on Quadratic Approximation of L-Natural-Convex Value Functions

Loading...
Thumbnail Image

Date

2014

Advisors

Sun, Peng

Journal Title

Journal ISSN

Volume Title

Repository Usage Stats

458
views
919
downloads

Abstract

We propose an approximation scheme for single-product periodic-review inventory systems with L-natural-convex structure. We lay out three well-studied inventory models, namely the lost-sales system, the perishable inventory system, and the joint inventory-pricing problem. We approximate the value functions for these models by the class of L-natural-convex quadratic functions, through the technique of linear programming approach to approximate dynamic programming. A series of heuristics are derived based on the quadratic approximation, and their performances are evaluated by comparison with existing heuristics. We present the numerical results and show that our heuristics outperform the benchmarks for majority of cases and scale well with long lead times. In this dissertation we also discuss the alternative strategies we have tried but with unsatisfactory result.

Description

Provenance

Citation

Citation

Wang, Kai (2014). Heuristics for Inventory Systems Based on Quadratic Approximation of L-Natural-Convex Value Functions. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/8777.

Collections


Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license.