1. 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: 349; Downloads: 0 This document has many files! More... |
2. 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: 383; Downloads: 0 This document has many files! More... |
3. 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: 367; Downloads: 0 This document has many files! More... |
4. 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: 389; Downloads: 0 This document has many files! More... |