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Branch and bound algorithm for a facility location problem with concave site dependent costs

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  • Dupont, Lionel

Abstract

The purpose of facility location models is to select a set of facilities (warehouses, plants, public facilities, antennas, etc.) to be implanted over a given area in order to satisfy the needs of all or part of the customers within this area. In this paper, the objective is to minimize the sum of the costs of investment, production and distribution while satisfying all customers' demands. We introduce a new type of facility location model, in which the global cost incurred for each established facility is a concave function of the quantity q delivered by this facility. We introduce some properties of an optimal solution and derive heuristic algorithms and a branch and bound method from these properties. Numerical examples illustrate this approach.

Suggested Citation

  • Dupont, Lionel, 2008. "Branch and bound algorithm for a facility location problem with concave site dependent costs," International Journal of Production Economics, Elsevier, vol. 112(1), pages 245-254, March.
    • Handle: RePEc:eee:proeco:v:112:y:2008:i:1:p:245-254
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    Cited by:

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    3. Péter Egri & Balázs Dávid & Tamás Kis & Miklós Krész, 2023. "Robust facility location in reverse logistics," Annals of Operations Research, Springer, vol. 324(1), pages 163-188, May.
    4. Ge, Houtian & Goetz, Stephan & Canning, Patrick & Perez, Agnes, 2018. "Optimal locations of fresh produce aggregation facilities in the United States with scale economies," International Journal of Production Economics, Elsevier, vol. 197(C), pages 143-157.
    5. Rentizelas, Athanasios A. & Tatsiopoulos, Ilias P., 2010. "Locating a bioenergy facility using a hybrid optimization method," International Journal of Production Economics, Elsevier, vol. 123(1), pages 196-209, January.
    6. Alejandro Montoya & Mario C. Vélez–Gallego & Juan G. Villegas, 2016. "Multi-product capacitated facility location problem with general production and building costs," Netnomics, Springer, vol. 17(1), pages 47-70, July.
    7. Emelogu, Adindu & Chowdhury, Sudipta & Marufuzzaman, Mohammad & Bian, Linkan & Eksioglu, Burak, 2016. "An enhanced sample average approximation method for stochastic optimization," International Journal of Production Economics, Elsevier, vol. 182(C), pages 230-252.
    8. Ge, Houtian & Goetz, Stephan J. & Cleary, Rebecca & Yi, Jing & Gómez, Miguel I., 2022. "Facility locations in the fresh produce supply chain: An integration of optimization and empirical methods," International Journal of Production Economics, Elsevier, vol. 249(C).
    9. Lin, C.K.Y., 2009. "Stochastic single-source capacitated facility location model with service level requirements," International Journal of Production Economics, Elsevier, vol. 117(2), pages 439-451, February.
    10. Miranda, Pablo A. & Garrido, Rodrigo A., 2009. "Inventory service-level optimization within distribution network design problem," International Journal of Production Economics, Elsevier, vol. 122(1), pages 276-285, November.
    11. Ge, Houtian & Gomez, Miguel I. & Peters, Christian, 2022. "Assessment of the economic viability of grass-fed beef production in the Northeastern U.S," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322487, Agricultural and Applied Economics Association.

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