Comparing Single and Multiobjective Evolutionary Approaches to the Inventory and Transportation Problem

Details Comparing Single and Multiobjective Evolutionary Approaches to the Inventory and Transportation Problem Comparing Single and Multiobjective Evolutionary Approaches to the Inventory and Transportation Problem

2009-09-18

arxiv.org/abs/0909.3384v1

Computer Science

Neural and Evolutionary Computing

34 pages. Submitted to Evolutionary Computation

Scientific paper

EVITA, standing for Evolutionary Inventory and Transportation Algorithm, is a two-level methodology designed to address the Inventory and Transportation Problem (ITP) in retail chains. The top level uses an evolutionary algorithm to obtain delivery patterns for each shop on a weekly basis so as to minimise the inventory costs, while the bottom level solves the Vehicle Routing Problem (VRP) for every day in order to obtain the minimum transport costs associated to a particular set of patterns. The aim of this paper is to investigate whether a multiobjective approach to this problem can yield any advantage over the previously used single objective approach. The analysis performed allows us to conclude that this is not the case and that the single objective approach is in gene- ral preferable for the ITP in the case studied. A further conclusion is that it is useful to employ a classical algorithm such as Clarke & Wright's as the seed for other metaheuristics like local search or tabu search in order to provide good results for the Vehicle Routing Problem.

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