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Genetic Algorithm Based Approaches to Install Different Types of Facilities
Soumen Atta, Priya Ranjan Sinha Mahapatra, 2014, published scientific conference contribution

Abstract: Given a set P of n-points (customers) on the plane and a positive integer k (1 ≤ k ≤ n), the objective is to find a placement of k circles (facilities) such that the union of k circles contains all the points of P and the sum of the radii of the circles is minimized. We have proposed a Genetic Algorithm (GA) to solve this problem. In this context, we have also proposed two different algorithms for k=1 and 2. Finally, we have proposed a GA to solve another optimization problem to compute a placement of fixed number of facilities where the facilities are hazardous in nature and the range of each such facility is circular.
Keywords: Facility Location, Enclosing Problem, Optimization Problem, Genetic Algorithm
Published in RUNG: 05.06.2023; Views: 809; Downloads: 0
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3.
Solving a new variant of the capacitated maximal covering location problem with fuzzy coverage area using metaheuristic approaches
Soumen 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: 1202; Downloads: 0
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Meta-heuristic algorithms to improve fuzzy C-means and K-means clustering for location allocation of telecenters under e-governance in developing nations
Rajan Gupta, Sunil K. Muttoo, Saibal K. Pal, 2019, original scientific article

Abstract: The telecenter, popularly known as the rural kiosk or common service center, is an important building block for the improvement of e-governance in developing nations as they help in better citizen engagement. Setting up of these centers at appropriate locations is a challenging task; inappropriate locations can lead to a huge loss to the government and allied stakeholders. This study proposes the use of various meta-heuristic algorithms (particle swarm optimization, bat algorithm, and ant colony optimization) for the improvement of traditional clustering approaches (K-means and fuzzy C-means) used in the facility location allocation problem and maps them for the betterment of telecenter location allocation. A dataset from the Indian region was considered for the purpose of this experiment. The performance of the algorithms when applied to traditional facility location allocation problems such as set-cover, P-median, and the P-center problem was investigated, and it was found that their efficiency improved by 20%–25% over that of existing algorithms.
Keywords: ant colony optimization, bat algorithm, common service center, e-governance, fuzzy clustering, meta-heuristic algorithm, particle swarm optimization
Published in RUNG: 01.04.2021; Views: 1873; Downloads: 10
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