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A RAMP Algorithm for Large-Scale Single Source Capacitated Facility Location Problems

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Learning and Intelligent Optimization (LION 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11968))

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Abstract

We propose a Relaxation Adaptive Memory Programming (RAMP) algorithm for the solution of the Single Source Capacitated Facility Location Problem (SSCFLP). This problem considers a set of possible locations for opening facilities and a set of clients whose demand must be satisfied. The objective is to minimize the cost of assigning the clients to the facilities, ensuring that all clients are served by only one facility without exceeding the capacity of the facilities. The RAMP framework efficiently explores the relation between the primal and the dual sides of combinatorial optimization problems. In our approach, the dual problem, obtained through a lagrangean relaxation, is solved by subgradient optimization. Computational experiments of the effectiveness of this approach are presented and discussed.

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Authors and Affiliations

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

    Óscar Oliveira, Telmo Matos & Dorabela Gamboa

Authors
  1. Óscar Oliveira
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  2. Telmo Matos
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  3. Dorabela Gamboa
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Corresponding author

Correspondence to Óscar Oliveira .

Editor information

Editors and Affiliations

  1. Technical University of Crete, Chania, Greece

    Nikolaos F. Matsatsinis

  2. Technical University of Crete, Chania, Greece

    Yannis Marinakis

  3. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos Pardalos

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Oliveira, Ó., Matos, T., Gamboa, D. (2020). A RAMP Algorithm for Large-Scale Single Source Capacitated Facility Location Problems. In: Matsatsinis, N., Marinakis, Y., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2019. Lecture Notes in Computer Science(), vol 11968. Springer, Cham. https://doi.org/10.1007/978-3-030-38629-0_14

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