The traveling purchaser problem with stochastic prices: Exact and approximate algorithms

Seungmo Kang, Yanfeng Ouyang

Research output: Contribution to journalArticlepeer-review


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 languageEnglish (US)
Pages (from-to)265-272
Number of pages8
JournalEuropean Journal of Operational Research
Issue number3
StatePublished - Mar 16 2011


  • Approximation
  • Dynamic programming
  • Heuristic
  • Stochastic price
  • Traveling purchaser problem

ASJC Scopus subject areas

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


Dive into the research topics of 'The traveling purchaser problem with stochastic prices: Exact and approximate algorithms'. Together they form a unique fingerprint.

Cite this