Solving a bi-objective Transportation Location Routing Problem by metaheuristic algorithms

• Published in: European journal of operational research 2014-04, Vol.234 (1), p.25-36
• Main Authors: Martínez-Salazar, Iris Abril, Molina, Julian, Ángel-Bello, Francisco, Gómez, Trinidad, Caballero, Rafael
• Format: Article
• Language: English
• Subjects: Algorithms; Computation; Computational efficiency; Computing time; Distribution costs; Genetic algorithms; Heuristic; Logistics; Mathematical models; Mathematical problems; Metaheuristics; Multiple objective programming; Optimization algorithms; Position (location); Representations; Route optimization; Studies; Transportation; Transportation Location Routing Problem; Transportation problem (Operations research)
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2013.09.008

•A mathematical formulation for the problem is proposed.•A new representation to reduce computational effort is presented.•Local search and evolutionary based solution algorithms are implemented.•Our implementation of Local Search based algorithms is outperformed by our implementation of Evolutionary based algorithms. In this work we consider a Transportation Location Routing Problem (TLRP) that can be seen as an extension of the two stage Location Routing Problem, in which the first stage corresponds to a transportation problem with truck capacity. Two objectives are considered in this research, reduction of distribution cost and balance of workloads for drivers in the routing stage. Here, we present a mathematical formulation for the bi-objective TLRP and propose a new representation for the TLRP based on priorities. This representation lets us manage the problem easily and reduces the computational effort, plus, it is suitable to be used with both local search based and evolutionary approaches. In order to demonstrate its efficiency, it was implemented in two metaheuristic solution algorithms based on the Scatter Tabu Search Procedure for Non-Linear Multiobjective Optimization (SSPMO) and on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) strategies. Computational experiments showed efficient results in solution quality and computing time.