A biased random-key genetic algorithm for the two-stage capacitated facility location problem. (January 2019)

• Record Type: Journal Article
• Authors: Biajoli, Fabrício Lacerda; Chaves, Antônio Augusto; Lorena, Luiz Antonio Nogueira
• Is Part Of: Expert systems with applications. Volume 115(2019)
• Page Start: 418
• Page End: 426
• Publication Date: 2019-01
• Subjects: Two-stage capacitated facility location -- Biased random-key genetic algorithm -- Local search -- Transportation systems; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.08.024
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Two-stage capacitated facility location problem (TSCFLP) is a NP-Hard problem. BRKGA+LS is a hybrid approach with BRKGA and a specific local search for TSCFLP. A literature review showed that BRKGA had never been applied to the TSCFLP. CALIBRA procedure was used to tuning the parameters of BRKGA. The solutions average and the computational time are improved in most instances. Abstract: This paper presents a new metaheuristic approach for the two-stage capacitated facility location problem (TSCFLP), which the objective is to minimize the operation costs of the underlying two-stage transportation system, satisfying demand and capacity constraints. In this problem, a single product must be transported from a set of plants to meet customers demands passing out by intermediate depots. Since this problem is known to be NP-hard, approximated methods become an efficient alternative to solve real-industry problems. As far as we know, the TSCFLP is being solved in most cases by hybrid approaches supported by an exact method, and sometimes a commercial solver is used for this purpose. Bearing this in mind, a BRKGA metaheuristic and a new local search for TSCFLP are proposed. It is the first time that BRKGA had been applied to this problem and the computational results show the competitiveness of the approach developed in terms of quality of the solutions and required computational time when compared with those obtained by state-of-the-art heuristics. The approach proposed can be easily coupled in intelligent systems to help organizations enhance competitiveness by optimally placing facilities in order to minimize operational costs.