11. Perturbation-minimising frequency assignment to address short term demand fluctuation in cellular networkSoumen Atta, Priya Ranjan Sinha Mahapatra, 2018, original scientific article Abstract: In cellular network short term demand fluctuation is a very common phenomenon. The demand of any cell may increase or decrease slightly or the system may expand by adding additional cells or the system may shrink if the demands of certain number of cells become zero. In this paper, the perturbation-minimising frequency assignment problem (PMFAP) is considered to address the short term fluctuation in demand vector. PMFAP is a frequency assignment problem in which newly generated demands are satisfied with minimum changes in the already existing frequency assignment keeping all the interference constraints. In this paper, an efficient heuristic algorithm for PMFAP is presented. The efficiency of this algorithm is compared with the existing results from literature. With a slight modification to the proposed algorithm, it can solve the well-known frequency assignment problem (FAP) and its performance is also compared with the existing results using the standard benchmark data sets for FAP.
Keywords: short term demand fluctuation, frequency assignment problem, FAP, PMFAP, cellular network, perturbation, heuristic algorithm
Published in RUNG: 17.04.2023; Views: 777; Downloads: 0
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12. Deterministic and randomized heuristic algorithms for uncapacitated facility location problemSoumen Atta, Priya Ranjan Sinha Mahapatra, Anirban Mukhopadhyay, 2018, published scientific conference contribution Abstract: A well-known combinatorial optimization problem, known as the Uncapacitated Facility Location Problem (UFLP) is considered in this paper. Given a set of customers and a set of potential facilities, the objective of UFLP is to open a subset of the potential facilities such that sum of the opening cost for opened facilities and the service cost of customers is minimized. In this paper, deterministic and randomized heuristic algorithms are presented to solve UFLP. The effectivenesses of the proposed algorithms are tested on UFLP instances taken from the OR-Library. Although the proposed deterministic algorithm gives optimal results for most of the instances, the randomized algorithm achieves optimal results for all the instances of UFLP considered in this paper including those for which the deterministic algorithm fails to achieve the optimal solutions.
Keywords: Uncapacitated facility location problem (UFLP), Simple plant location problem (SPLP), Warehouse location problem (WLP), Heuristics Randomization
Published in RUNG: 17.04.2023; Views: 850; Downloads: 0
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13. Solving uncapacitated facility location problem using monkey algorithmSoumen Atta, Priya Ranjan Sinha Mahapatra, Anirban Mukhopadhyay, 2018, published scientific conference contribution Abstract: The Uncapacitated Facility Location Problem (UFLP) is considered in this paper. Given a set of customers and a set of potential facility locations, the objective of UFLP is to open a subset of facilities to satisfy the demands of all the customers such that the sum of the opening cost for the opened facilities and the service cost is minimized. UFLP is a well-known combinatorial optimization problem which is also NP-hard. So, a metaheuristic algorithm for solving this problem is natural choice. In this paper, a relatively new swarm intelligence-based algorithm known as the Monkey Algorithm (MA) is applied to solve UFLP. To validate the efficiency of the proposed binary MA-based algorithm, experiments are carried out with various data instances of UFLP taken from the OR-Library and the results are compared with those of the Firefly Algorithm (FA) and the Artificial Bee Colony (ABC) algorithm.
Keywords: Uncapacitated Facility Location Problem (UFLP), Simple Plant Location Problem (SPLP), Warehouse Location Problem (WLP), Monkey Algorithm
Published in RUNG: 17.04.2023; Views: 852; Downloads: 0
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14. L (D, 2, 1)-labeling of Square GridSoumen Atta, Priya Ranjan Sinha Mahapatra, 2019, original scientific article Abstract: For a fixed integer $D (\geq 3)$ and $\lambda$ $\in$ $\mathbb{Z}^+$, a $\lambda$-$L(D, 2, 1)$-$labeling$ of a graph $G = (V, E)$ is the problem of assigning non-negative integers (known as labels) from the set $\{0, \ldots, \lambda\}$ to the vertices of $G$ such that if any two vertices in $V$ are one, two and three distance apart from each other then the assigned labels to these vertices must have a difference of at least $D$, 2 and 1 respectively. The vertices which are at least $4$ distance apart can receive the same label. The minimum value among all the possible values of $\lambda$ for which there exists a $\lambda$-$L(D, 2, 1)$-$labeling$ is known as the labeling number. In this paper $\lambda$-$L(D, 2 ,1)$-$labeling$ of square grid is considered. The lower bound on the labeling number for square grid is presented and a formula for $\lambda$-$L(D, 2 ,1)$-$labeling$ of square grid is proposed. The correctness proof of the proposed formula is given here. The upper bound of the labeling number obtained from the proposed labeling formula for square grid matches exactly with the lower bound of the labeling number.
Keywords: Graph labeling, Square grid, Labeling number, Frequency assignment problem (FAP)
Published in RUNG: 17.04.2023; Views: 762; Downloads: 0
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15. No-hole λ-L (k, k – 1, …, 2,1)-labeling for square gridSoumen Atta, Stanisław Goldstein, Priya Ranjan Sinha Mahapatra, 2017, original scientific article Abstract: Motivated by a frequency assignment problem, we demonstrate, for a fixed positive integer k, how to label an infinite square grid with a possibly small number of integer labels, ranging from 0 to λ −1, in such a way that labels of adjacent vertices differ by at least k, vertices connected by a path of length two receive values which differ by at least k − 1, and so on. The vertices which are at least k + 1 distance apart may receive the same label. By finding a lower bound for λ, we prove that the solution is close to optimal, with approximation ratio at most 9/8. The labeling presented is a no-hole one, i.e., it uses each of the allowed labels at least once.
Keywords: graph labeling, labeling number, no-hole labeling, square grid, frequency assignment problem, approximation ratio
Published in RUNG: 17.04.2023; Views: 754; Downloads: 0
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16. 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: 752; Downloads: 0
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17. 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: 1341; Downloads: 0
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