TY - JOUR
T1 - The traveling purchaser problem with stochastic prices
T2 - Exact and approximate algorithms
AU - Kang, Seungmo
AU - Ouyang, Yanfeng
N1 - 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.
PY - 2011/3/16
Y1 - 2011/3/16
N2 - 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.
AB - 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.
KW - Approximation
KW - Dynamic programming
KW - Heuristic
KW - Stochastic price
KW - Traveling purchaser problem
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U2 - 10.1016/j.ejor.2010.09.012
DO - 10.1016/j.ejor.2010.09.012
M3 - Article
AN - SCOPUS:78649630096
SN - 0377-2217
VL - 209
SP - 265
EP - 272
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 3
ER -