exploring Pareto optimality-based solutions
Soumen Atta (Author), Priya Ranjan Sinha Mahapatra (Author), Anirban Mukhopadhyay (Author)

Abstract

The maximal covering location problem (MCLP) is a well-known combinatorial optimization problem with several applications in emergency and military services as well as in public services. Traditionally, MCLP is a single objective problem where the objective is to maximize the sum of the demands of customers which are served by a fixed number of open facilities. In this article, a multi-objective MCLP is proposed where each customer has a preference for each facility. The multi-objective MCLP with customers’ preferences (MOMCLPCP) deals with the opening of a fixed number of facilities from a given set of potential facility locations and then customers are assigned to these opened facilities such that both (i) the sum of the demands of customers and (ii) the sum of the preferences of the customers covered by these opened facilities are maximized. A Pareto-based multi-objective harmony search algorithm (MOHSA), which utilizes a harmony refinement strategy for faster convergence, is proposed to solve MOMCLPCP. The proposed MOHSA is terminated based on the stabilization of the density of non-dominated solutions. For experimental purposes, 82 new test instances of MOMCLPCP are generated from the existing single objective MCLP benchmark data sets. The performance of the proposed MOHSA is compared with the well-known non-dominated sorting genetic algorithm II (NSGA-II), and it has been observed that the proposed MOHSA always outperforms NSGA-II in terms of computation time. Moreover, statistical tests show that the objective values obtained from both algorithms are comparable.

Keywords

maximal covering location problem;MCLP;multi-objective MCLP;customers' preferences;multi-objective harmony search algorithm;MOHSA;NSGA II;CPLEX;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UNG - University of Nova Gorica
UDC: 004
COBISS: 149312003 Link will open in a new window
ISSN: 1873-6793
Views: 119
Downloads: 0
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Other data

URN: URN:SI:UNG
Type (COBISS): Not categorized
Pages: str. 1-15
Volume: ǂVol. ǂ186
Issue: ǂ[article no.] ǂ115830
Chronology: Dec. 2021
DOI: 10.1016/j.eswa.2021.115830
ID: 18625113