Authors:
Philippe Lacomme
1
;
Caroline Prodhon
2
;
Christian Prins
2
;
Xavier Gandibleux
3
;
Boris Beillevaire
3
and
Libo Ren
1
Affiliations:
1
Université Blaise Pascal (LIMOS UMR 6158), France
;
2
Université de Technologie de Troyes, France
;
3
Université de Nantes (LINA UMR 6241), France
Keyword(s):
Vehicle Routing, Multi-objective Optimization, Split Algorithm, Meta-heuristic.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
e-Business
;
Enterprise Information Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Logistics
;
Methodologies and Technologies
;
Operational Research
;
Optimization
;
Routing
;
Symbolic Systems
Abstract:
The vehicle routing problem with route balancing is a bi-objective routing problem, in which the total route length and the balance of routes (i.e. the difference between the maximal and minimal route length) have to be minimized. In this paper, we propose an approach based on two solution representations: a giant tour representing a sequence of customers (indirect representation) and a complete solution with a decomposition of the giant tour, combined with a split algorithm to alternate between them. This approach offers a mainly efficient way to explore the solution space. Our motivation is based on the possibility to generate efficiently several solutions a time using an indirect representation which has been already proved to be efficient in numerous routing problems resolution. The originality here is to tune the split algorithm considering two objectives. An evolutionary path relinking algorithm is embedded to improve the obtained solutions. The proposed approach is evaluated o
n classical vehicle routing problem instances and the results push us into accepting that the method is competitive with the best published mono-objective methods (on criteria one : the total route length). On a bi-objective point of view, our method is competitive with the lexicographic solutions reported in the literature in the sense that it provides similar or better results in comparable computational time.
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