IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v209y2011i3p265-272.html
   My bibliography  Save this article

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

Author

Listed:
  • Kang, Seungmo
  • Ouyang, Yanfeng

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.

Suggested Citation

  • Kang, Seungmo & Ouyang, Yanfeng, 2011. "The traveling purchaser problem with stochastic prices: Exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 209(3), pages 265-272, March.
  • Handle: RePEc:eee:ejores:v:209:y:2011:i:3:p:265-272
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(10)00608-9
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Leipala, Timo, 1978. "On the solutions of stochastic traveling salesman problems," European Journal of Operational Research, Elsevier, vol. 2(4), pages 291-297, July.
    2. Bianchi, Leonora & Campbell, Ann Melissa, 2007. "Extension of the 2-p-opt and 1-shift algorithms to the heterogeneous probabilistic traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 176(1), pages 131-144, January.
    3. Liu, Yu-Hsin, 2008. "Diversified local search strategy under scatter search framework for the probabilistic traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 191(2), pages 332-346, December.
    4. Cyrus Derman & Gerald J. Lieberman & Sheldon M. Ross, 1972. "A Sequential Stochastic Assignment Problem," Management Science, INFORMS, vol. 18(7), pages 349-355, March.
    5. Singh, Kashi N. & van Oudheusden, Dirk L., 1997. "A branch and bound algorithm for the traveling purchaser problem," European Journal of Operational Research, Elsevier, vol. 97(3), pages 571-579, March.
    6. Renaud, Jacques & Boctor, Fayez F., 1998. "An efficient composite heuristic for the symmetric generalized traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 108(3), pages 571-584, August.
    7. Golden, Bruce & Levy, Larry & Dahl, Roy, 1981. "Two generalizations of the traveling salesman problem," Omega, Elsevier, vol. 9(4), pages 439-441.
    8. Bertsimas, Dimitris & Howell, Louis H., 1993. "Further results on the probabilistic traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 65(1), pages 68-95, February.
    9. Snyder, Lawrence V. & Daskin, Mark S., 2006. "A random-key genetic algorithm for the generalized traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 174(1), pages 38-53, October.
    10. Jason D. Papastavrou & Srikanth Rajagopalan & Anton J. Kleywegt, 1996. "The Dynamic and Stochastic Knapsack Problem with Deadlines," Management Science, INFORMS, vol. 42(12), pages 1706-1718, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Taesung Hwang & Yanfeng Ouyang, 2015. "Urban Freight Truck Routing under Stochastic Congestion and Emission Considerations," Sustainability, MDPI, Open Access Journal, vol. 7(6), pages 1-16, May.
    2. Manerba, Daniele & Mansini, Renata & Riera-Ledesma, Jorge, 2017. "The Traveling Purchaser Problem and its variants," European Journal of Operational Research, Elsevier, vol. 259(1), pages 1-18.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:209:y:2011:i:3:p:265-272. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/eor .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.