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Optimization Letters

, Volume 7, Issue 7, pp 1525–1535 | Cite as

A parallel matheuristic for the technician routing and scheduling problem

  • V. Pillac
  • C. Guéret
  • A. L. MedagliaEmail author
Original Paper

Abstract

The Technician Routing and Scheduling Problem (TRSP) consists in routing staff to serve requests for service, taking into account time windows, skills, tools, and spare parts. Typical applications include maintenance operations and staff routing in telecoms, public utilities, and in the health care industry. In this paper, we present a formal definition of the TRSP, discuss its relation with the Vehicle Routing Problem with Time Windows (VRPTW), and review related research. From a methodological perspective, we describe a matheuristic composed of a constructive heuristic, a parallel Adaptive Large Neighborhood Search, and a mathematical programming based post-optimization procedure that successfully tackles the TRSP. We validate the matheuristic on the Solomon VRPTW instances, where we achieve an average gap of \(0.23\,\%\), and matched 44 out of 55 optimal solutions. Finally, we illustrate how the matheuristic successfully solves a set of TRSP instances extended from the Solomon benchmark.

Keywords

Vehicle routing Technician routing and scheduling  Matheuristic ALNS  VRPTW 

Notes

Acknowledgments

Financial support for this work was provided by the CPER (Contrat de Projet Etat Region) Vallée du Libre (France); and the Centro de Estudios Interdisciplinarios Básicos y Aplicados en Complejidad (CEIBA, Colombia). This support is gratefully acknowledged. The authors would also like to thank Olivier Péton from the Ecole des Mines de Nantes and the anonymous reviewers for their insightful comments and suggestions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.École des Mines de NantesLUNAM UniversitéNantesFrance
  2. 2.Centro para la Optimización y Probabilidad Aplicada (COPA), Centro de Estudios Interdisciplinarios Básicos y Aplicados en Complejidad (CEIBA), Departamento de Ingeniería IndustrialUniversidad de los AndesBogotáColombia
  3. 3.LISA—IUT Angers-CholetLUNAM UniversitéAngersFrance
  4. 4.Centro para la Optimización y Probabilidad Aplicada (COPA), Departamento de Ingeniería IndustrialUniversidad de los AndesBogotáColombia

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