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

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Date
2014
Author
Wang, Kai
Advisor
Sun, Peng
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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.

Type
Dissertation
Department
Business Administration
Subject
Business
Operations research
Approximate Dynamic Programming
Heuristics
Inventory System
L-Natural-Convex
Lost-Sales
Perishable
Permalink
https://hdl.handle.net/10161/8777
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.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

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