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Approximation algorithms for Median Hub Location Problems

Author

Listed:
  • Marcelo P. L. Benedito

    (University of Campinas)

  • Lehilton L. C. Pedrosa

    (University of Campinas)

Abstract

In Hub Location Problems, an input is composed of sets of clients, locations and a pairs of clients; a solution is a subset of locations to open hubs and an assignment for each pair of clients to a route starting in the first client, passing through one or more hubs and ending in the second client. The objective is to find a solution that minimizes the length of all routes plus the cost of opening hubs. The currently known approximation algorithms consider only the case in which the set of hubs is given as part of the input and the problem is assigning clients to hubs. For a metric space setting, this work presents the first constant-factor approximation algorithms for the problem of, simultaneously, selecting hubs and allocating clients. A few variants are considered, depending on whether the number of open hubs is given in the input or a client must be assigned to a single hub. In particular, we give an LP-rounding $$2.48$$ 2.48 -approximation algorithm for the Single-Allocation Median Hub Location Problem, using a new formulation and exploiting its symmetries.

Suggested Citation

  • Marcelo P. L. Benedito & Lehilton L. C. Pedrosa, 2019. "Approximation algorithms for Median Hub Location Problems," Journal of Combinatorial Optimization, Springer, vol. 38(2), pages 375-401, August.
    • Handle: RePEc:spr:jcomop:v:38:y:2019:i:2:d:10.1007_s10878-019-00386-1
    DOI: 10.1007/s10878-019-00386-1
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    Cited by:

    1. Gunadi, Christian, 2020. "Does immigrant legalization affect crime? Evidence from deferred action for childhood arrivals in the United States," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 327-353.

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