Exploring feasible and infeasible regions in the vehicle routing problem with time windows using a multi-objective particle swarm optimization approach

Sie sind hier: Homepage > Suche

Exploring feasible and infeasible regions in the vehicle routing problem with time windows using a multi-objective particle swarm optimization approach

Castro, Juan P. / Landa-Silva, Dario / Moreno Perez, Jose A.

This paper investigates the ability of a discrete particle swarm optimization algorithm (DPSO) to evolve solutions from infeasibility to feasibility for the vehicle routing problem with time windows (VRPTW). The proposed algorithm incorporates some principles from multi-objective optimization to allow particles to conduct a dynamic trade-off between objectives in order to reach feasibility. The main contribution of this paper is to demonstrate that without incorporating tailored heuristics or operators to tackle infeasibility, it is possible to evolve very poor infeasible route-plans to very good feasible ones using swarm intelligence. Section 2 describes the particle swarm optimization (PSO) paradigm which is used in this paper to tackle the VRPTW. Section 3 describes the multi-objective discrete PSO proposed in this paper to evolve infeasible solutions to feasible ones using a dynamic trade-off of the multiple objectives. Section 4 describes and discusses experiments and results while Section 5 concludes this paper.

Zugriff

Zugriff über TIB

Verfügbarkeit in meiner Bibliothek prüfen

Bestellung bei Subito €

Exportieren, teilen und zitieren

Dokumentinformationen

Titel :

Exploring feasible and infeasible regions in the vehicle routing problem with time windows using a multi-objective particle swarm optimization approach

Weitere Titelangaben:

Untersuchung zulässiger und nichtzulässiger Bereiche beim Tourenplanungsproblem mit Zeitfenstern mit Hilfe eines multikriteriellen Partikelschwarm-Optimierungsansatzes

Beteiligte:

Castro, Juan P. (Autor:in) / Landa-Silva, Dario (Autor:in) / Moreno Perez, Jose A. (Autor:in)

Erschienen in:

NICSO, Workshop on Nature-Inspired Cooperative Strategies for Optimization, 3 ; 103-114

Studies in Computational Intelligence ; 236

Erscheinungsdatum :

2009

Format / Umfang :

12 Seiten, 2 Bilder, 1 Tabelle, 25 Quellen

ISBN :

978-3-642-03210-3

ISSN :