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Improving Traditional Dual Ascent Algorithm for the Uncapacitated Multiple Allocation Hub Location Problem: A RAMP Approach

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Machine Learning, Optimization, and Data Science (LOD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11331))

Abstract

Hub Location Problems are complex combinatorial optimization problems that raised a lot of interest in the literature and have a huge number of practical applications, going from the telecommunications, airline transportation among others. In this paper we propose a primal-dual algorithm to solve the Uncapacitated Multiple Allocation Hub Location Problem (UMAHLP). RAMP algorithm combines information of traditional Dual Ascent procedure on the dual side with an improvement method on the primal side, together with adaptive memory structures. The overall performance of the proposed algorithm was tested on standard Australian Post (AP) and Civil Aeronautics Boarding (CAB) instances, comprising 192 test instances. The effectiveness of our approach has been proven by comparing with other state-of-the-art algorithms.

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Authors and Affiliations

  1. School of Technology and Management, Polytechnic of Porto, CIICESI – Center for Research and Innovation in Business Sciences and Information Systems, Porto, Portugal

    Telmo Matos, Fábio Maia & Dorabela Gamboa

Authors
  1. Telmo Matos
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  2. Fábio Maia
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  3. Dorabela Gamboa
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Corresponding author

Correspondence to Telmo Matos .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy and University of Reading, Reading, UK

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

  5. IBM, Tivoli Research Lab, Rome, Italy

    Vincenzo Sciacca

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Matos, T., Maia, F., Gamboa, D. (2019). Improving Traditional Dual Ascent Algorithm for the Uncapacitated Multiple Allocation Hub Location Problem: A RAMP Approach. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_20

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  • DOI: https://doi.org/10.1007/978-3-030-13709-0_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13708-3

  • Online ISBN: 978-3-030-13709-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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