The Capacitated Vehicle Routing Problem with Planned Downtime

Abstract The Capacitated Vehicle Routing Problem with Planned Downtime introduced in this work is a generalization of the classical vehicle routing problem in which two sets of routes have to be jointly designed: one set considering that all the vehicles of the fleet are available, and another set that considers the scheduled unavailability of a vehicle. Besides routing cost minimization, it is also intended that the routes for the two cases (when all vehicles are available and when one is missing) are as similar as possible in order to reduce the impact on the customers. In this paper, we formally define the problem, propose two mixed integer programming models for it, one of them based on bilevel programming, and devise exact branch-and-cut algorithms based on those models. The algorithms are first compared, and then the most efficient one is tested on different benchmark instances with up to 40 nodes and 5 vehicles.

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Publication Details

Journal
Computational Optimization and Applications
Published
2025-07-19
DOI
https://doi.org/10.1007/s10589-025-00713-9
Citations
1
Primary Topic
Vehicle Routing Optimization Methods
Type
article
Field-Weighted Citation Impact
1.24

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article

The Capacitated Vehicle Routing Problem with Planned Downtime

Inmaculada Rodrı́guez-Martı́n, Juan F. Monge, Mercedes Landete, Marina Leal
1 citations
Computational Optimization and Applications
Vehicle Routing Optimization Methods
1.24
article

The Capacitated Vehicle Routing Problem with Planned Downtime

Inmaculada Rodrı́guez-Martı́n, Juan F. Monge, Mercedes Landete, Marina Leal
article en
1 citations

Abstract

Abstract The Capacitated Vehicle Routing Problem with Planned Downtime introduced in this work is a generalization of the classical vehicle routing problem in which two sets of routes have to be jointly designed: one set considering that all the vehicles of the fleet are available, and another set that considers the scheduled unavailability of a vehicle. Besides routing cost minimization, it is also intended that the routes for the two cases (when all vehicles are available and when one is missing) are as similar as possible in order to reduce the impact on the customers. In this paper, we formally define the problem, propose two mixed integer programming models for it, one of them based on bilevel programming, and devise exact branch-and-cut algorithms based on those models. The algorithms are first compared, and then the most efficient one is tested on different benchmark instances with up to 40 nodes and 5 vehicles.

Computational Optimization and ApplicationsVol. 92(2)
Universitat de Miguel Hernández d'Elx (ES), Universidad de La Laguna (ES)
Universidad de La Laguna, Generalitat Valenciana, Ministerio de Ciencia e Innovación, European Regional Development Fund
Openalex Percentile: Top 17%
Vehicle Routing Optimization Methods
1.24
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