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

• Published in: Annals of Operations Research Vol. 183; no. 1; pp. 143 - 161
• Main Authors: Hashimoto, Hideki, Boussier, Sylvain, Vasquez, Michel, Wilbaut, Christophe
• Format: Journal Article
• Language: English
• Published: Boston Springer Science and Business Media LLC 01.03.2011; Springer US; Springer Science + Business Media; Springer; Springer Nature B.V; Springer Verlag
• Subjects: [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO]; [INFO.INFO-RO]Computer Science [cs]/Operations Research [math.OC]; Business and Management; Combinatorics; Computer Science; GRASP; Heuristic; Intervention; Learning; Lower bounds; Metaheuristics; Operations Research; Operations Research/Decision Theory; Priorities; Queuing; Schedules; Scheduling; Scheduling algorithms; Searching; Studies; Supervisors; Teams; Technicians and Intervention Scheduling; Telecommunications; Telecommunications systems; Theory of Computation; France; Metaheuristics; GRASP; Technicians and Intervention Scheduling
• ISSN: 0254-5330; 1572-9338; 1572-9338
• DOI: 10.1007/s10479-009-0545-0

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.