Combining constraint programming, lagrangean relaxation and probabilistic algorithms to solve the vehicle routing problem
View/ Open
Annals_Math_Art_Int_Juan_2011.pdf (299.3Kb) (Restricted access)
Document typeArticle
Date issued2011
Rights accessRestricted access - publisher's policy
Abstract
This paper presents an original hybrid approach to solve the Capacitated
Vehicle Routing Problem (CVRP). The approach combines a Probabilistic Algorithm
with Constraint Programming (CP) and Lagrangian Relaxation (LR). After
introducing the CVRP and reviewing the existing literature on the topic, the paper
proposes an approach based on a probabilistic Variable Neighbourhood Search
(VNS) algorithm. Given a CVRP instance, this algorithm uses a randomized version
of the classical Clarke and Wright Savings constructive heuristic to generate a starting
solution. This starting solution is then improved through a local search process which
combines: (a) LR to optimise each individual route, and (b) CP to quickly verify the
feasibility of new proposed solutions. The efficiency of our approach is analysed after
testing some well-known CVRP benchmarks. Benefits of our hybrid approach over
already existing approaches are also discussed. In particular, the potential flexibility
of our methodology is highlighted.
CitationGuimarans, D. [et al.]. Combining constraint programming, lagrangean relaxation and probabilistic algorithms to solve the vehicle routing problem. "Annals of mathematics and artificial intelligence", 2011, vol. 62, núm. 3-4, p. 299-315.
ISSN1012-2443
Collections
Files | Description | Size | Format | View |
---|---|---|---|---|
Annals_Math_Art_Int_Juan_2011.pdf![]() | 299.3Kb | Restricted access |
Except where otherwise noted, content on this work is licensed under a Creative Commons license:
Attribution-NonCommercial-NoDerivs 3.0 Spain