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Stochastic Gradual Covering Location Models

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Uncertainty in Facility Location Problems

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 347))

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Abstract

Most location models assume that the parameters are given and fixed. Demand for services is known, and the distance to the facility is given. Real-world parameters are not fixed but follow a probability distribution such as a normal distribution. Therefore, stochastic models estimate the results (cost, profit, cover) more accurately.

In cover models, facilities need to be located in an area to provide service to a set of demand points. Demand points that are within a given distance are covered. Two main objectives are investigated in the literature: provide as much cover as possible with a given number of facilities and minimize the number of facilities required to provide full cover. In gradual cover models, up to a certain distance, the demand point is fully covered, and beyond a greater distance, it is not covered at all. Between these two extreme distances, the demand point is partially covered.

In this chapter, we summarize gradual cover models emphasizing on models that have stochastic parameters. We also propose a new model analyzing a stochastic version of the directional graduate cover.

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Authors and Affiliations

  1. College of Business and Economics, California State University-Fullerton, Fullerton, CA, USA

    Zvi Drezner

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  1. Zvi Drezner
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Correspondence to Zvi Drezner .

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Editors and Affiliations

  1. Faculty of Management, University of New Brunswick, Fredericton, NB, Canada

    H. A. Eiselt

  2. Department of Electrical Engineering, Pontificia Universidad Católica de Chile and Instituto Sistemas Complejos de Ingeniería (ISCI), Santiago, Chile

    Vladimir Marianov

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Drezner, Z. (2023). Stochastic Gradual Covering Location Models. In: Eiselt, H.A., Marianov, V. (eds) Uncertainty in Facility Location Problems. International Series in Operations Research & Management Science, vol 347. Springer, Cham. https://doi.org/10.1007/978-3-031-32338-6_11

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