1. Solving a new variant of the capacitated maximal covering location problem with fuzzy coverage area using metaheuristic approachesAnirban Mukhopadhyay, Priya Ranjan Sinha Mahapatra, Soumen Atta, 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.
Found in: osebi
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: 08.03.2023; Views: 300; Downloads: 0
 Fulltext (2,21 MB) |
2. A multi-objective formulation of maximal covering location problem with customers’ preferences: Exploring Pareto optimality-based solutionsAnirban Mukhopadhyay, Priya Ranjan Sinha Mahapatra, Soumen Atta, 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.
Found in: osebi
Keywords: Maximal covering location problem (MCLP), Multi-objective MCLP, Customers’ preferences, Multi-objective harmony search algorithm (MOHSA), NSGA II, CPLEX
Published: 17.04.2023; Views: 106; Downloads: 0
Fulltext (1,43 MB) |
3. A new variant of the p-hub location problem with a ring backbone network for content placement in VoD servicesSoumen Atta, Goutam Sen, 2021, original scientific article Abstract: In this article, the single allocation p-hub location problem (SApHLP) with a ring backbone network for content placement in VoD services is proposed. In VoD services, a large volume of digital data is kept as data segments in spatially distributed hubs. In SApHLP, each user is restricted to be allocated only to a single hub, and here hubs form a ring backbone network. SApHLP jointly addresses (i) the locations of hubs, (ii) the placement of segments to hubs, (iii) the allocation of users to hubs as per their demands, and (iv) the optimal paths to route the demands from users to hubs. We have introduced network flow-based 3-subscripted and path-based 4-subscripted MILP formulations of SApHLP. This article presents a novel discrete particle swarm optimization (PSO)-based approach where factoradic numbers are used to encode solution. It also incorporates three problem-specific solution refinement methods for faster convergence. In this article, SApHLP instances are generated from a real-world database of video files obtained from a movie recommender system. The benchmark solutions are generated using IBM’s CPLEX optimizer with default settings and Benders decomposition strategy. The performance of the proposed PSO is compared with the benchmark results produced by CPLEX.
Found in: osebi
Keywords: Single allocation p-hub location problem, Ring backbone network, VoD services, Particle Swarm Optimization (PSO), Factoradics, CPLEX
Published: 17.04.2023; Views: 116; Downloads: 0
Fulltext (1,61 MB) |
4. A New Variant of Dynamic Pickup and Delivery Problem with Time WindowsSoumen Atta, Hana Rudová, Petr Valenta, 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.
Found in: osebi
Keywords: Dynamic Pickup and Delivery Problem, Time Windows, Heuristic
Published: 17.04.2023; Views: 145; Downloads: 0
Fulltext (419,40 KB) |
5. Multiple allocation p-hub location problem for content placement in VoD services: a differential evolution based approachGoutam Sen, Soumen Atta, 2020, original scientific article Abstract: In video-on-demand (VoD) services, large volumes of digital data are kept at hubs which are spatially distributed over large geographic areas and users are connected to these hubs based on their demands. In this article, we consider a large database of video files, that are pre-partitioned to multiple segments based on the demand patterns of users. These segments are restricted to be located only in hubs. Here, users are allowed to be allocated to multiple hubs and all hubs are assumed to be connected with each other. We jointly decide the location of hubs, the placement of segments to these hubs and then the assignment of users to these hubs as per their demand patterns and finally, we find the optimal paths to route the demands of users for different segments having the objective of minimizing the total routing cost. In this article, a differential evolution (DE) based method is proposed to solve the problem. The proposed DE-based method utilizes an efficient function to evaluate the objective value of a candidate solution to the proposed problem. It also incorporates two problem-specific solution refinement techniques for faster convergence. Instances of the problem are generated from the real world movie database and the proposed method is applied to these instances and the performance is evaluated against the benchmark results obtained from CPLEX.
Found in: osebi
Keywords: Video-on-demand (VoD) services, Content distribution networks, Database segment location, Hub location, Multiple hub allocation, Differential evolution (DE), IBM ILOG CPLEX
Published: 17.04.2023; Views: 129; Downloads: 0
Fulltext (2,15 MB) |
6. L (D, 2, 1)-labeling of Square GridPriya Ranjan Sinha Mahapatra, Soumen Atta, 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.
Found in: osebi
Keywords: Graph labeling, Square grid, Labeling number, Frequency assignment problem (FAP)
Published: 17.04.2023; Views: 96; Downloads: 0
Fulltext (304,64 KB) |
7. Multi-objective uncapacitated facility location problem with customers’ preferences: Pareto-based and weighted sum GA-based approachesAnirban Mukhopadhyay, Priya Ranjan Sinha Mahapatra, Soumen Atta, 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.
Found in: osebi
Keywords: Uncapacitated facility location problem (UFLP), Multi-objective UFLP with customers’ preferences (MOUFLPCP), NSGA-II, Weighted sum genetic algorithm (WSGA)
Published: 17.04.2023; Views: 140; Downloads: 0
Fulltext (534,37 KB) |
8. Population-based improvement heuristic with local search for single-row facility layout problemPriya Ranjan Sinha Mahapatra, Soumen Atta, 2019, original scientific article Abstract: The Single-Row Facility Layout Problem (SRFLP) is a well-known combinatorial optimization problem. The objective of SRFLP is to find out the arrangement of facilities with given lengths on a line so that the weighted sum of the distances between all pairs of facilities is minimized. This problem is known to be NP-hard. Hence, a population-based improvement heuristic algorithm with local search is presented in this article to solve SRFLP. The proposed algorithm works well also for the Single-Row Equidistant Facility Layout Problem (SREFLP), where the length of each facility is equal. The computational efficiency of the proposed algorithm is checked with the instances of sizes ranging from 5 to 300 available in the literature for SRFLP and SREFLP. The obtained results are compared to those from different state-of-the-art algorithms. The proposed algorithm achieves best known solutions to date for every instance considered in this article in reasonable computational time.
Found in: osebi
Keywords: Single-row facility layout problem (SRFLP), single-row equidistant facility layout problem (SREFLP), population-based heuristic, improvement heuristic, local search
Published: 17.04.2023; Views: 147; Downloads: 0
Fulltext (1,43 MB) |
9. Solving tool indexing problem using harmony search algorithm with harmony refinementAnirban Mukhopadhyay, Priya Ranjan Sinha Mahapatra, Soumen Atta, 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.
Found in: osebi
Keywords: Tool indexing problem (TIP), Computer Numerically Controlled (CNC) machine, Harmony search (HS) algorithm, Automatic tool changer (ATC), CPLEX
Published: 17.04.2023; Views: 100; Downloads: 0
Fulltext (974,57 KB) |
10. Solving uncapacitated facility location problem using heuristic algorithmsAnirban Mukhopadhyay, Priya Ranjan Sinha Mahapatra, Soumen Atta, 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.
Found in: osebi
Keywords: Uncapacitated Facility Location Problem (UFLP), Simple Plant Location Problem (SPLP), Warehouse Location Problem (WLP), Heuristics, Randomization
Published: 17.04.2023; Views: 117; Downloads: 0
Fulltext (2,29 MB) |
