Solving a new variant of the capacitated maximal covering location problem with fuzzy coverage area using metaheuristic approaches. (August 2022) - Atta, Soumen; Mahapatra, Priya Ranjan Sinha; Mukhopadhyay, Anirban
Abstract: The Maximal Covering Location Problem (MCLP) is concerned with the optimal placement of a fixed number of facilities to cover the maximum number of customers. This article considers a new variant of MCLP where both the coverage radii of facilities and the distance between customer and facility are fuzzy. Moreover, the finite capacity of each facility is considered. We call this problem the capacitated MCLP with fuzzy coverage area (FCMCLP), and it is formulated as a 0–1 linear programming problem. In this article, two classical metaheuristics: particle swarm optimization, differential evolution, and two new-generation metaheuristics: artificial bee colony algorithm, firefly algorithm, are proposed for solving FCMCLP. Each of the customized metaheuristics utilizes a greedy deterministic heuristic to generate their initial populations. They also incorporate a local neighborhood search to improve their convergence rates. New instances of FCMCLP are generated from the traditional MCLP instances available in the literature, and IBM's CPLEX solver is used to generate benchmark solutions. An experimental comparative study among the four customized metaheuristics is described in this article. The performances of the proposed metaheuristics are also compared with the benchmark solutions obtained from CPLEX. Highlights: A new variant of the maximal covering location problem is considered Capacitated facilities with fuzzy coverage areas are considered. Benchmark solutions are generated using CPLEX solver for newly generated instances. A comparative study among four metaheuristic algorithms is presented.