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Accumulation Point Location on Tree Networks for Guaranteed Time Distribution

Author

Listed:
  • Ananth V. Iyer

    (Graduate School of Business, University of Chicago, 1101 East 58th Street, Chicago, Illinois 60637)

  • H. Donald Ratliff

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

Abstract

A guaranteed time distribution system is organized so that movement of items between any source-destination pair (serviced by the system) is completed within a guaranteed period of time (T). Various express mail services are examples of such distribution systems. The movement of items between source and destination consists of a combination of different modes of travel with varying speeds (i.e. trucks, airplanes etc.). Decisions have to be made regarding location of accumulation points (called local centers) at which changes in the mode of travel occur. All customers have to be allocated to these local centers so as to provide the best (minimum) time guarantee for travel between every source-destination pair. Accumulated flows between local centers pass through a global center in a centralized sorting system (CSS) and move directly between local centers in a decentralized sorting system (DSS). For tree structured distribution systems we provide polynomial time algorithms that locate a given number of local centers, allocate customers to local centers and provide the best possible time guarantee for both centralized and decentralized guaranteed time distribution systems.

Suggested Citation

  • Ananth V. Iyer & H. Donald Ratliff, 1990. "Accumulation Point Location on Tree Networks for Guaranteed Time Distribution," Management Science, INFORMS, vol. 36(8), pages 958-969, August.
    • Handle: RePEc:inm:ormnsc:v:36:y:1990:i:8:p:958-969
    DOI: 10.1287/mnsc.36.8.958
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    Citations

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    Cited by:

    1. Omar Ben-Ayed, 2012. "Redesigning the inter-facility network of a parcel distribution company in a developing country," International Journal of Services, Economics and Management, Inderscience Enterprises Ltd, vol. 4(1), pages 27-42.
    2. James F. Campbell & Morton E. O'Kelly, 2012. "Twenty-Five Years of Hub Location Research," Transportation Science, INFORMS, vol. 46(2), pages 153-169, May.
    3. Chen, Dongxu & Yang, Zhongzhen, 2018. "Systematic optimization of port clusters along the Maritime Silk Road in the context of industry transfer and production capacity constraints," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 174-189.
    4. Bahar Y. Kara & Barbaros Ç. Tansel, 2001. "The Latest Arrival Hub Location Problem," Management Science, INFORMS, vol. 47(10), pages 1408-1420, October.
    5. Campbell, Ann Melissa & Lowe, Timothy J. & Zhang, Li, 2007. "The p-hub center allocation problem," European Journal of Operational Research, Elsevier, vol. 176(2), pages 819-835, January.
    6. R. L. Francis & T. J. Lowe & Arie Tamir, 2000. "Aggregation Error Bounds for a Class of Location Models," Operations Research, INFORMS, vol. 48(2), pages 294-307, April.
    7. Alumur, Sibel & Kara, Bahar Y., 2008. "Network hub location problems: The state of the art," European Journal of Operational Research, Elsevier, vol. 190(1), pages 1-21, October.
    8. Gelareh, Shahin & Nickel, Stefan, 2011. "Hub location problems in transportation networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1092-1111.

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