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A GRASP-based approach for technicians and interventions scheduling for telecommunications

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  • Hideki Hashimoto
  • Sylvain Boussier
  • Michel Vasquez
  • Christophe Wilbaut

Abstract

The Technicians and Interventions Scheduling Problem for Telecommunications embeds the scheduling of interventions, the assignment of teams to interventions and the assignment of technicians to teams. Every intervention is characterized, among other attributes, by a priority. The objective of this problem is to schedule interventions such that the interventions with the highest priority are scheduled at the earliest time possible while satisfying a set of constraints like the precedence between some interventions and the minimum number of technicians needed with the required skill levels for the intervention. We present a Greedy Randomized Adaptive Search Procedure (GRASP) for solving this problem. In the proposed implementation, we integrate learning to the GRASP framework in order to generate good-quality solutions using information brought by previous ones. We also compute lower bounds and present experimental results that validate the effectiveness of this approach. Copyright Springer Science+Business Media, LLC 2011

Suggested Citation

  • Hideki Hashimoto & Sylvain Boussier & Michel Vasquez & Christophe Wilbaut, 2011. "A GRASP-based approach for technicians and interventions scheduling for telecommunications," Annals of Operations Research, Springer, vol. 183(1), pages 143-161, March.
    • Handle: RePEc:spr:annopr:v:183:y:2011:i:1:p:143-161:10.1007/s10479-009-0545-0
    DOI: 10.1007/s10479-009-0545-0
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    References listed on IDEAS

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    1. Taillard, Eric D. & Gambardella, Luca M. & Gendreau, Michel & Potvin, Jean-Yves, 2001. "Adaptive memory programming: A unified view of metaheuristics," European Journal of Operational Research, Elsevier, vol. 135(1), pages 1-16, November.
    2. Charles Fleurent & Fred Glover, 1999. "Improved Constructive Multistart Strategies for the Quadratic Assignment Problem Using Adaptive Memory," INFORMS Journal on Computing, INFORMS, vol. 11(2), pages 198-204, May.
    3. Andrea Lodi & Silvano Martello & Daniele Vigo, 2004. "Models and Bounds for Two-Dimensional Level Packing Problems," Journal of Combinatorial Optimization, Springer, vol. 8(3), pages 363-379, September.
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    Cited by:

    1. Ines Mathlouthi & Michel Gendreau & Jean-Yves Potvin, 2021. "Branch-and-Price for a Multi-attribute Technician Routing and Scheduling Problem," SN Operations Research Forum, Springer, vol. 2(1), pages 1-35, March.
    2. Chen, Xi & Thomas, Barrett W. & Hewitt, Mike, 2016. "The technician routing problem with experience-based service times," Omega, Elsevier, vol. 61(C), pages 49-61.
    3. Fangzhou Yan & Huaxin Qiu & Dongya Han, 2023. "Lagrangian Heuristic for Multi-Depot Technician Planning of Product Distribution and Installation with a Lunch Break," Mathematics, MDPI, vol. 11(3), pages 1-22, January.
    4. Michel Vasquez & Mirsad Buljubasic & Saïd Hanafi, 2023. "An efficient scenario penalization matheuristic for a stochastic scheduling problem," Journal of Heuristics, Springer, vol. 29(2), pages 383-408, June.
    5. Ehsan Pourjavad & Eman Almehdawe, 2022. "Optimization of the technician routing and scheduling problem for a telecommunication industry," Annals of Operations Research, Springer, vol. 315(1), pages 371-395, August.
    6. Paraskevopoulos, Dimitris C. & Laporte, Gilbert & Repoussis, Panagiotis P. & Tarantilis, Christos D., 2017. "Resource constrained routing and scheduling: Review and research prospects," European Journal of Operational Research, Elsevier, vol. 263(3), pages 737-754.

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