Authors:
Eneko Osaba
and
Roberto Carballedo
Affiliation:
University of Deusto, Spain
Keyword(s):
Vehicle Routing Problem, Combinatorial Optimization, Evolutionary Computing, Problem Bechmarks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Memetic Algorithms
;
Soft Computing
Abstract:
In the field of vehicle routing problems it is very common to use benchmarks (sets of problem instances) to evaluate new solving techniques or algorithms. The purpose of these benchmarks is to compare the techniques based on the results or solutions obtained. Typically, the benchmarks include the values of optimal solutions (if they have been obtained) or values of the best known solutions. In many cases, details of how these results were obtained are not described. This may generate controversy and difficults the comparisons of techniques. This paper shows an example of ambiguity in the results of an instance of the most used VRPTW (Vehicle Routing Problem with Time Windows) bechmark. We show that when analyzing the optimal solution and the best approximate solution of a specific problem, the two results are equivalent. Finally, we will propose a set of guidelines to consider when publishing the results obtained by a new algorithm.