1. An improved harmony search algorithm using opposition-based learning and local search for solving the maximal covering location problemSoumen Atta, 2023, original scientific article Abstract: In this article, an improved harmony search algorithm (IHSA) that utilizes opposition-based learning is presented for solving the maximal covering location problem (MCLP). The MCLP is a well-known facility location problem where a fixed number of facilities are opened at a given potential set of facility locations such that the sum of the demands of customers covered by the open facilities is maximized. Here, the performance of the harmony search algorithm (HSA) is improved by incorporating opposition-based learning that utilizes opposite, quasi-opposite and quasi-reflected numbers. Moreover, a local search heuristic is used to improve the performance of the HSA further. The proposed IHSA is employed to solve 83 real-world MCLP instances. The performance of the IHSA is compared with a Lagrangean/surrogate relaxation-based heuristic, a customized genetic algorithm with local refinement, and an improved chemical reaction optimization-based algorithm. The proposed IHSA is found to perform well in solving the MCLP instances.
Keywords: maximal covering location problem, harmony search algorithm, opposition-based learning, facility location problem, opposite number
Published in RUNG: 05.10.2023; Views: 733; Downloads: 7
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2. Genetic Algorithm Based Approach for Serving Maximum Number of Customers Using Limited ResourcesSoumen Atta, Priya Ranjan Sinha Mahapatra, 2013, original scientific article Abstract: It is often needed to install limited number of facilities to address the demand of customers due to resource constraints and thus the requirement to provide service to all customers is not possible to meet. In such situation, the facilities are installed (placed) so that the maximum demand can be met. The problem of installing (locating) such facilities are known as Maximal Covering Location Problem (MCLP) [2] in facility location [1]. We assume that (i) all facilities are in a plane, and (ii) all customers can be considered as a point set on the same plane. The type of covering area (or range) of a facility depends on the facility to be installed. We consider the MCLP where the covering area (or range) of each facility is the area of a square with fixed size. In other words here, each facility is installed at the center of the square. The problem considered in this article is defined as follows: given a set P of n input points (customers) on the plane and k squares (facilities) each of fixed size, the objective is to find a placement of k squares so that the union of k axis parallel squares covers (contains) the maximum numbers of input points where k (1≤k≤n) is a positive integer constant. This problem is known to be NP-hard [5]. We have proposed a genetic algorithm (GA) to solve this problem.
Keywords: Maximal Covering Location Problem, Facility Location, Genetic Algorithm
Published in RUNG: 05.06.2023; Views: 791; Downloads: 0
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3. Solving maximal covering location problem using genetic algorithm with local refinementSoumen Atta, Priya Ranjan Sinha Mahapatra, Anirban Mukhopadhyay, 2018, original scientific article Abstract: The maximal covering location problem (MCLP) deals with the problem of finding an optimal placement of a given number of facilities within a set of customers. Each customer has a specific demand and the facilities are to be placed in such a way that the total demand of the customers served by the facilities is maximized. In this article an improved genetic algorithm (GA)-based approach, which utilizes a local refinement strategy for faster convergence, is proposed to solve MCLP. The proposed algorithm is applied on several MCLP instances from literature and it is demonstrated that the proposed GA with local refinement gives better results in terms of percentage of coverage and computation time to find the solutions in almost all the cases. The proposed GA-based approach with local refinement is also found to outperform the other existing methods for most of the small as well as large instances of MCLP.
Keywords: Facility location problem, Covering location problem, Maximal covering location problem (MCLP), Genetic algorithm (GA), Local refinement
Published in RUNG: 17.04.2023; Views: 792; Downloads: 0
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4. A multi-objective formulation of maximal covering location problem with customers’ preferences: Exploring Pareto optimality-based solutionsSoumen Atta, Priya Ranjan Sinha Mahapatra, Anirban Mukhopadhyay, 2021, original scientific article 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
Published in RUNG: 17.04.2023; Views: 713; Downloads: 0
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5. Solving a new variant of the capacitated maximal covering location problem with fuzzy coverage area using metaheuristic approachesSoumen Atta, Priya Ranjan Sinha Mahapatra, Anirban Mukhopadhyay, 2022, original scientific article 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.
Keywords: Facility Location Problem (FLP), Fuzzy Capacitated Maximal Covering Location Problem (FCMCLP), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), Firefly Algorithm (FA)
Published in RUNG: 08.03.2023; Views: 1265; Downloads: 0
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