Abstract
The paper formulates an extension of the traveling purchaser problem where multiple types of commodities are sold at spatially distributed locations with stochastic prices (each following a known probability distribution). A purchaser's goal is to find the optimal routing and purchasing strategies that minimize the expected total travel and purchasing costs needed to purchase one unit of each commodity. The purchaser reveals the actual commodity price at a seller upon arrival, and then either purchases the commodity at the offered price, or rejects the price and visits a next seller. In this paper, we propose an exact solution algorithm based on dynamic programming, an iterative approximate algorithm that yields bounds for the minimum total expected cost, and a greedy heuristic for fast solutions to large-scale applications. We analyze the characteristics of the problem and test the computational performance of the proposed algorithms. The numerical results show that the approximate and heuristic algorithms yield near-optimum strategies and very good estimates of the minimum total cost.
Original language | English |
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Pages (from-to) | 265-272 |
Number of pages | 8 |
Journal | European Journal of Operational Research |
Volume | 209 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2011 Mar 16 |
Bibliographical note
Funding Information:This work is partially supported by the US National Science Foundation through Award CMMI-0748067 . Dr. Alexander Nikolaev (UIUC) provided helpful information including the literature on SSAP and DKP.
Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
Keywords
- Approximation
- Dynamic programming
- Heuristic
- Stochastic price
- Traveling purchaser problem
ASJC Scopus subject areas
- General Computer Science
- Modelling and Simulation
- Management Science and Operations Research
- Information Systems and Management