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On Stochastic k-Facility Location

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Algorithmic Aspects in Information and Management (AAIM 2021)

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Abstract

In the stochastic facility location problem, there are two-stage processes for the decision. A set of facilities may be opened without information for the demand of clients at the first stage; an additional set of facilities may further be opened at the second stage where the scenario of the clients is realized according to some given distribution. One has to take the risk into consideration and make decision on the open facilities in each stage and each scenario such that the total expected cost of the opening and service is minimized. In this paper, we consider a global cardinality constraint in this model, i.e., there is an upper bound k for the number of open facilities at the second stage. This model generalizes both stochastic facility location and the k-median. Our main result is a provable efficient approximation algorithm with a performance ratio of 6 based on primal-dual schema.

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Acknowledgements

The research of the third author is supported by NSFC (No. 11971349). The research of the fourth author is supported by NSFC (No. 12071460).

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Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, People’s Republic of China

    Yicheng Xu & Yong Zhang

  2. Department of Operations Research and Information Engineering, Beijing University of Technology, Beijing, 100124, People’s Republic of China

    Chunlin Hao

  3. College of Science, Tianjin University of Technology, Tianjin, People’s Republic of China

    Chenchen Wu

Authors
  1. Yicheng Xu
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  2. Chunlin Hao
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  3. Chenchen Wu
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  4. Yong Zhang
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Editors and Affiliations

  1. The University of Texas at Dallas, Richardson, TX, USA

    Weili Wu

  2. Harbin Institute of Technology, Shenzhen, China

    Hongwei Du

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Xu, Y., Hao, C., Wu, C., Zhang, Y. (2021). On Stochastic k-Facility Location. In: Wu, W., Du, H. (eds) Algorithmic Aspects in Information and Management. AAIM 2021. Lecture Notes in Computer Science(), vol 13153. Springer, Cham. https://doi.org/10.1007/978-3-030-93176-6_5

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  • DOI: https://doi.org/10.1007/978-3-030-93176-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93175-9

  • Online ISBN: 978-3-030-93176-6

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