A new GA based solution for capacitated multi source Weber problem

Abstract

The main purpose of any facility location is to select the optimal places that satisfy project's goals. In location problems, the object is usually to optimize a function -objective function -that defines the problem conditions and efficient decision parameters. Numerous methods are proposed to challenge the location facility issues. In this paper, we consider a class of location/allocation problem that can assume more realistic conditions in real-life applications. This problem is an extension to the well-known capacitated multi source Weber problem. A new method that uses two genetic algorithms is used to solve the problem efficiently. The first, External GA, solves the location problem while the second, Internal GA, solves the allocation problem. A case study was designed to assess the feasibility of the proposed solution. The results indicate that the new approach is optimal, efficient and successful.

Application of Heuristic and Metaheuristic Algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs

“…Also, some variants of the Weber problem have been successfully solved by different metaheuristic approaches [47][48][49][50][51][52]. In [52], the authors studied a capacitated multisource Weber problem as an extended facility location problem that involves both facility locations and service allocations simultaneously. The method proposed in [52] is based on the integration of two genetic algorithms.…”

“…In [52], the authors studied a capacitated multisource Weber problem as an extended facility location problem that involves both facility locations and service allocations simultaneously. The method proposed in [52] is based on the integration of two genetic algorithms. The problem of locating one new facility with respect to a given set of existing facilities in the plane and in the presence of convex polyhedral barriers was considered in [47].…”

An approach for solving maximal covering location problems with fuzzy constraints

“…In this sense, heuristic and metaheuristic methods have been used in recent years to solve different types of location problems 13,11,5 . Here, using a specific metaheuristic we will show how the problem can be solved, and the kind of information we can obtain when using this parametric approach, which in turn, will allow the decision maker to take a more informed decision.…”

Risk-based optimization of emergency rescue facilities locations for large-scale environmental accidents to improve urban public safety

“…In order to overcome these limitations of exact methods, some scholars have resorted to a number of heuristic approaches to solve geographical optimization problems effectively and efficiently (Xiao 2008;Tong and Murray 2012). Among various modern heuristic approaches, evolutionary algorithms (e.g., GA) have shown great promise for generating solutions to large-scale ERFLs problem (Jia et al 2007b;Indriasari et al 2010;Mohammadi et al 2010). When GA is applied to solve multi-objective optimization problems, however, the scalar fitness information should be provided to combine multiple objectives into a single objective by using aggregating approaches (e.g., weighted sum approach, goal programming, etc.)…”

“…Moreover, there is a serious drawback of the weighted sum approach as it may miss concave portions of the trade-off curve, in other words, it may miss important solutions, especially when the Pareto front contains concave and/or discontinuous sections (Coello 1999;Xiao et al 2002). Furthermore, it is usually difficult for decision makers to determine the appropriate weights when some features of the problem are not fully understood during the early stages of decision making (Miettinen 1999;Xiao et al 2007). In this context, appropriate approach is needed to solve the multi-objective ERFLs problem in a large-scale emergency situation.…”