On the design of hybrid bio‐inspired meta‐heuristics for complex multiattribute vehicle routing problems

Nogareda, Ana-Maria (Ecole hôtelière de Lausanne, HES-SO // University of Applied Sciences Western Switzerland) ; Del Ser, Javier (ICT Division, TECNALIA. P. Tecnologico Bizkaia, Derio, Spain ; University of the Basque Country UPV/EHU, Bilbao, Spain) ; Osaba, Eneko (ICT Division, TECNALIA. P. Tecnologico Bizkaia, Derio, Spain) ; Camacho, David (Technical University of Madrid, Madrid, Spain)

This paper addresses a multiattribute vehicle routing problem, the rich vehicle routing problem, with time constraints, heterogeneous fleet, multiple depots, multiple routes, and incompatibilities of goods. Four different approaches are presented and applied to 15 real datasets. They are based on two meta‐heuristics, ant colony optimization (ACO) and genetic algorithm (GA), that are applied in their standard formulation and combined as hybrid meta‐heuristics to solve the problem. As such ACO‐GA is a hybrid meta‐heuristic using ACO as main approach and GA as local search. GA‐ACO is a memetic algorithm using GA as main approach and ACO as local search. The results regarding quality and computation time are compared with two commercial tools currently used to solve the problem. Considering the number of customers served, one of the tools and the ACO‐GA approach outperforms the others. Considering the cost, ACO, GA, and GA‐ACO provide better results. Regarding computation time, GA and GA‐ACO have been found the most competitive among the benchmark.


Keywords:
Article Type:
scientifique
Faculty:
Economie et Services
School:
EHL
Institute:
Aucun institut
Subject(s):
Economie/gestion
Date:
2020-12
Pagination:
20 p.
Published in:
Expert Systems
Numeration (vol. no.):
2020, vol. 37, no. 6, article e12528, pp. 1-20
DOI:
ISSN:
0266-4720
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2021-03-29, last modified 2021-06-21

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