1. 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: 820; Downloads: 0
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3. Genetic Algorithm Based Approaches to Install Different Types of FacilitiesSoumen 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: 862; Downloads: 0
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4. L(4, 3, 2, 1)-Labeling for Simple GraphsSoumen Atta, Priya Ranjan Sinha Mahapatra, 2015, published scientific conference contribution Abstract: An L(4, 3, 2, 1)-labeling of a graph is a function which assigns label to each vertex of the graph such that if two vertices are one, two, three and four distance apart then assigned labels must have a difference of at least 4, 3, 2 and 1 respectively between them. This paper presents L(4, 3, 2, 1)-labeling number for simple graphs such as complete graphs, complete bipartite graphs, stars, paths and cycles. This paper also presents an L(4, 3, 2, 1)-labeling algorithm for paths which is optimal for paths on n≥7 vertices.
Keywords: L(4, 3, 2, 1)-labeling, Labeling number, Graph labeling, Channel assignment problem
Published in RUNG: 05.06.2023; Views: 685; Downloads: 0
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7. Solving tool indexing problem using harmony search algorithm with harmony refinementSoumen Atta, Priya Ranjan Sinha Mahapatra, Anirban Mukhopadhyay, 2019, original scientific article Abstract: The tool indexing problem (TIP) is the problem of allocating cutting tools to different slots in a tool magazine of Computer Numerically Controlled machine to reduce the processing time of jobs on the machine. This is one of the mostly encountered optimization problems in manufacturing systems. In TIP, the number of tools used by the machine is at most the number of slots available in the tool magazine. In this article, a customized harmony search (HS) algorithm, which utilizes a harmony refinement strategy for faster convergence, is presented to solve TIP. The harmony refinement method also helps to avoid getting stuck into local optima. The performance of the proposed method is tested on 27 instances taken from the literature and out of these it is found to improve the best known solutions for 16 instances. For the remaining instances, it gives the same results as found in the literature. Moreover, the performance of the proposed algorithm is tested on newly adapted 41 instances and for some of these instances the results obtained using the proposed algorithm are compared with that obtained using CPLEX.
Keywords: Tool indexing problem (TIP), Computer Numerically Controlled (CNC) machine, Harmony search (HS) algorithm, Automatic tool changer (ATC), CPLEX
Published in RUNG: 17.04.2023; Views: 776; Downloads: 0
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8. Solving uncapacitated facility location problem using heuristic algorithmsSoumen Atta, Priya Ranjan Sinha Mahapatra, Anirban Mukhopadhyay, 2019, original scientific article Abstract: A well-known combinatorial optimization problem, known as the uncapacitated facility location problem (UFLP) is considered in this article. A deterministic heuristic algorithm and a randomized heuristic algorithm are presented to solve UFLP. Though the proposed deterministic heuristic algorithm is very simple, it produces good solution for each instance of UFLP considered in this article. The main purpose of this article is to process all the data sets of UFLP available in the literature using a single algorithm. The proposed two algorithms are applied on these test instances of UFLP to determine their effectiveness. Here, the solution obtained from the proposed randomized algorithm is at least as good as the solution produced by the proposed deterministic algorithm. Hence, the proposed deterministic algorithm gives upper bound on the solution produced by the randomized algorithm. Although the proposed deterministic algorithm gives optimal results for most of the instances of UFLP, the randomized algorithm achieves optimal results for all the instances of UFLP considered in this article 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: 769; Downloads: 0
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9. 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: 813; Downloads: 0
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10. Multi-objective uncapacitated facility location problem with customers’ preferences: Pareto-based and weighted sum GA-based approachesSoumen Atta, Priya Ranjan Sinha Mahapatra, Anirban Mukhopadhyay, 2019, original scientific article Abstract: The uncapacitated facility location problem (UFLP) is a well-known combinatorial optimization problem having single-objective function. The objective of UFLP is to find a subset of facilities from a given set of potential facility locations such that the sum of the opening costs of the opened facilities and the service cost to serve all the customers is minimized. In traditional UFLP, customers are served by their nearest facilities. In this article, we have proposed a multi-objective UFLP where each customer has a preference for each facility. Hence, the objective of the multi-objective UFLP with customers’ preferences (MOUFLPCP) is to open a subset of facilities to serve all the customers such that the sum of the opening cost and service cost is minimized and the sum of the preferences is maximized. In this article, the elitist non-dominated sorting genetic algorithm II (NSGA-II), a popular Pareto-based GA, is employed to solve this problem. Moreover, a weighted sum genetic algorithm (WSGA)-based approach is proposed to solve MOUFLPCP where conflicting two objectives of the problem are aggregated to a single quality measure. For experimental purposes, new test instances of MOUFLPCP are created from the existing UFLP benchmark instances and the experimental results obtained using NSGA-II and WSGA-based approaches are demonstrated and compared for these newly created test instances.
Keywords: Uncapacitated facility location problem (UFLP), Multi-objective UFLP with customers’ preferences (MOUFLPCP), NSGA-II, Weighted sum genetic algorithm (WSGA)
Published in RUNG: 17.04.2023; Views: 863; Downloads: 0
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