Matheuristics and Column Generation for a ...
Type de document :
Compte-rendu et recension critique d'ouvrage
DOI :
Titre :
Matheuristics and Column Generation for a Basic Technician Routing Problem
Auteur(s) :
Dupin, Nicolas [Auteur]
Laboratoire Interdisciplinaire des Sciences du Numérique [LISN]
Université Paris-Saclay
Parize, Rémi [Auteur]
Talbi, El-Ghazali [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Université de Lille
Inria Lille - Nord Europe
Laboratoire Interdisciplinaire des Sciences du Numérique [LISN]
Université Paris-Saclay
Parize, Rémi [Auteur]
Talbi, El-Ghazali [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Université de Lille
Inria Lille - Nord Europe
Titre de la revue :
Algorithms
Pagination :
313
Éditeur :
MDPI
Date de publication :
2021-11
ISSN :
1999-4893
Discipline(s) HAL :
Computer Science [cs]/Operations Research [math.OC]
Informatique [cs]/Intelligence artificielle [cs.AI]
Mathématiques [math]/Optimisation et contrôle [math.OC]
Informatique [cs]/Intelligence artificielle [cs.AI]
Mathématiques [math]/Optimisation et contrôle [math.OC]
Résumé en anglais : [en]
This paper considers a variant of the Vehicle Routing Problem with Time Windows, with site dependencies, multiple depots and outsourcing costs. This problem is the basis for many technician routing problems. Having both ...
Lire la suite >This paper considers a variant of the Vehicle Routing Problem with Time Windows, with site dependencies, multiple depots and outsourcing costs. This problem is the basis for many technician routing problems. Having both site-dependency and time window constraints lresults in difficulties in finding feasible solutions and induces highly constrained instances. Matheuristics based on Mixed Integer Linear Programming compact formulations are firstly designed. Column Generation matheuristics are then described by using previous matheuristics and machine learning techniques to stabilize and speed up the convergence of the Column Generation algorithm. The computational experiments are analyzed on public instances with graduated difficulties in order to analyze the accuracy of algorithms for ensuring feasibility and the quality of solutions for weakly to highly constrained instances. The results emphasize the interest of the multiple types of hybridization between mathematical programming, machine learning and heuristics inside the Column Generation framework. This work offers perspectives for many extensions of technician routing problems.Lire moins >
Lire la suite >This paper considers a variant of the Vehicle Routing Problem with Time Windows, with site dependencies, multiple depots and outsourcing costs. This problem is the basis for many technician routing problems. Having both site-dependency and time window constraints lresults in difficulties in finding feasible solutions and induces highly constrained instances. Matheuristics based on Mixed Integer Linear Programming compact formulations are firstly designed. Column Generation matheuristics are then described by using previous matheuristics and machine learning techniques to stabilize and speed up the convergence of the Column Generation algorithm. The computational experiments are analyzed on public instances with graduated difficulties in order to analyze the accuracy of algorithms for ensuring feasibility and the quality of solutions for weakly to highly constrained instances. The results emphasize the interest of the multiple types of hybridization between mathematical programming, machine learning and heuristics inside the Column Generation framework. This work offers perspectives for many extensions of technician routing problems.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
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