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1.
Genetic Algorithm Based Approach for Serving Maximum Number of Customers Using Limited Resources
Soumen 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: 340; Downloads: 0
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2.
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: 358; Downloads: 0
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3.
Power-aware Traffic Grooming in WDM Optical Mesh Networks for Bandwidth Wastage Minimization: A Genetic Algorithm-based Approach
Soumen Atta, Anirban Mukhopadhyay, 2012, published scientific conference contribution

Abstract: The cost of optical backbone network has increased nowadays. So we need to reduce this cost. One of the major contributory costs is the power consumed by the underlying network. Power may also be consumed by different network equipments viz. add-drop multiplexers (ADM), Network Interface Device (NID), Optical Network Terminal (ONT), electrical-to-optical-to-electrical (EOE) conversion etc. In this article we have only considered the power consumption by EOE conversion in a mesh network. We have proposed a genetic algorithm to minimize the EOE conversions needed for a mesh network to satisfy all the traffic requests for a given physical topology. We have also considered the amount of wavelength wastages for our solution and we have minimized these wastages below a user given value. The results have been demonstrated on two optical mesh networks.
Keywords: Optical Network, WDM, Traffic Grooming, Network Components, Green Optical Network, Genetic Algorithm
Published in RUNG: 05.06.2023; Views: 368; Downloads: 0
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4.
Solving maximal covering location problem using genetic algorithm with local refinement
Soumen 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: 325; Downloads: 0
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5.
Multi-objective uncapacitated facility location problem with customers’ preferences: Pareto-based and weighted sum GA-based approaches
Soumen 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: 383; Downloads: 0
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