Matheuristics and Column Generation for a Basic Technician Routing Problem

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...

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Autores principales:	Nicolas Dupin, Rémi Parize, El-Ghazali Talbi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	optimization operations research mathematical programming mixed integer linear programming Dantzig–Wolfe decomposition column generation Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/f7d96d1eb633482d9cab6ceef952a97b
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spelling	oai:doaj.org-article:f7d96d1eb633482d9cab6ceef952a97b2021-11-25T16:13:05ZMatheuristics and Column Generation for a Basic Technician Routing Problem10.3390/a141103131999-4893https://doaj.org/article/f7d96d1eb633482d9cab6ceef952a97b2021-10-01T00:00:00Zhttps://www.mdpi.com/1999-4893/14/11/313https://doaj.org/toc/1999-4893This 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.Nicolas DupinRémi ParizeEl-Ghazali TalbiMDPI AGarticleoptimizationoperations researchmathematical programmingmixed integer linear programmingDantzig–Wolfe decompositioncolumn generationIndustrial engineering. Management engineeringT55.4-60.8Electronic computers. Computer scienceQA75.5-76.95ENAlgorithms, Vol 14, Iss 313, p 313 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	optimization operations research mathematical programming mixed integer linear programming Dantzig–Wolfe decomposition column generation Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
spellingShingle	optimization operations research mathematical programming mixed integer linear programming Dantzig–Wolfe decomposition column generation Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95 Nicolas Dupin Rémi Parize El-Ghazali Talbi Matheuristics and Column Generation for a Basic Technician Routing Problem
description	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.
format	article
author	Nicolas Dupin Rémi Parize El-Ghazali Talbi
author_facet	Nicolas Dupin Rémi Parize El-Ghazali Talbi
author_sort	Nicolas Dupin
title	Matheuristics and Column Generation for a Basic Technician Routing Problem
title_short	Matheuristics and Column Generation for a Basic Technician Routing Problem
title_full	Matheuristics and Column Generation for a Basic Technician Routing Problem
title_fullStr	Matheuristics and Column Generation for a Basic Technician Routing Problem
title_full_unstemmed	Matheuristics and Column Generation for a Basic Technician Routing Problem
title_sort	matheuristics and column generation for a basic technician routing problem
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f7d96d1eb633482d9cab6ceef952a97b
work_keys_str_mv	AT nicolasdupin matheuristicsandcolumngenerationforabasictechnicianroutingproblem AT remiparize matheuristicsandcolumngenerationforabasictechnicianroutingproblem AT elghazalitalbi matheuristicsandcolumngenerationforabasictechnicianroutingproblem
_version_	1718413294877802496

Matheuristics and Column Generation for a Basic Technician Routing Problem

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