1. An improved harmony search algorithm using opposition-based learning and local search for solving the maximal covering location problemSoumen Atta, 2024, 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: 2010; Downloads: 11
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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: 1771; Downloads: 0
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4. 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: 1701; Downloads: 0
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5. 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: 1453; Downloads: 0
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6. Power-aware Traffic Grooming in WDM Optical Mesh Networks for Bandwidth Wastage Minimization: A Genetic Algorithm-based ApproachSoumen 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: 2009; Downloads: 0
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8. A New Variant of Dynamic Pickup and Delivery Problem with Time WindowsPetr Valenta, Hana Rudová, Soumen Atta, 2020, published scientific conference contribution Abstract: Motivated by the challenges faced by a logistics company, we present a new variant of the dynamic capacitated pickup and delivery problem with time windows (PDPTW) where excessive changes of unaffected routes are undesirable. In real-life scenarios, different dynamism sources such as canceled requests, change of demands, change of pickup, or delivery time windows often disrupt the existing planning of routes. The static PDPTW is solved with the current information about the problem well before executing the routes, such as the previous night. We present an algorithmic idea of a dynamic solver quickly addressing changes that occur due to the dynamism while avoiding excessive modifications to the previous solution. Since the company has not yet the dynamic data, new dynamic instances are generated from the existing static PDPTW instances in the literature. Preliminary results demonstrate that we can quickly incorporate the required changes. Future perspectives of this ongoing work are discussed in the end.
Keywords: Dynamic Pickup and Delivery Problem, Time Windows, Heuristic
Published in RUNG: 17.04.2023; Views: 3111; Downloads: 0
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10. 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: 2148; Downloads: 0
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