A greedy randomized adaptive search with probabilistic learning for solving the uncapacitated plant cycle location problem

  1. Israel López-Plata 1
  2. Christopher Expósito-Izquierdo 1
  3. Eduardo Lalla-Ruiz 2
  4. Belén Melián-Batista 1
  5. J. Marcos Moreno-Vega 1
  1. 1 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

  2. 2 University of Twente
    info

    University of Twente

    Enschede, Holanda

    ROR https://ror.org/006hf6230

Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2023

Volumen: 8

Número: 2

Páginas: 123-133

Tipo: Artículo

DOI: 10.9781/IJIMAI.2022.04.003 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Objetivos de desarrollo sostenible

Resumen

In this paper, we address the Uncapacitated Plant Cycle Location Problem. It is a location-routing problem aimed at determining a subset of locations to set up plants dedicated to serving customers. We propose a mathematical formulation to model the problem. The high computational burden required by the formulation when tackling large scenarios encourages us to develop a Greedy Randomized Adaptive Search Procedure with Probabilistic Learning Model. Its rationale is to divide the problem into two interconnected sub-problems. The computational results indicate the high performance of our proposal in terms of the quality of reported solutions and computational time. Specifically, we have overcome the best approach from the literature on a wide range of scenarios.

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Fuente de los datos: Dialnet