Constraint Programming Based Algorithm for Solving Large-Scale Vehicle Routing Problems

• Bochra Rabbouch ^{[1] [2]} ; Foued Saâdaoui ^{[2] [3]} ; Rafaa Mraihi ^[3]
  1. [1] Tunis University

     Tunis University

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  2. [2] University of Monastir

     University of Monastir

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  3. [3] Manouba University

     Manouba University

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• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 526-539
• Idioma: inglés
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  • Smart cities management has become currently an interesting topic where recent decision aid making algorithms are essential to solve and optimize their related problems. A popular transportation optimization problem is the Vehicle Routing Problem (VRP) which is high complicated in such a way that it is categorized as a NP-hard problem. VRPs are famous and appear as influential problems that are widely present in many real-world industrial applications. They have become an elemental part of economy, the enhancement of which arises in a significant reduction in costs.The basic version of VRPs, the Capacitated VRP (CVRP) occupies a central position for historical and practical considerations since there are important real-world systems can be satisfactorily modeled as a CVRP. A Constraint Programming (CP) paradigm is used to model and solve the CVRP by applying interval and sequence variables in addition to the use of a transition distance matrix to attain the objective. An empirical study over 52 CVRP classical instances, with a number of nodes that varies from 16 to 200, and 20 CVRP large-scale instances, with a number of nodes that varies from 106 to 459, shows the relative merits of our proposed approach. It shows also that the CP paradigm tackles successfully large-scale problems with a percentage deviation varying from 2% to 10% where several exact and heuristic algorithms fail to tackle them and only a few meta-heuristics can probably solve instances with a such big number of customers.