Enhanced iterated local search for the technician routing and scheduling problem

Abstract

Most public facilities in the European countries, including France, Germany, and the United Kingdom, were built during the reconstruction projects between 1950 and 1980. Owing to the deteriorating state of such vital infrastructure, its maintenance has become relatively expensive in the recent decades. A significant part of the maintenance operation costs is spent on the technical staff. Therefore, the optimal use of the available workforce is essential to minimize the overall costs. This includes planning technical interventions, workload balancing, productivity improvement, etc. In this paper, we focus on the routing of technicians and the scheduling of their tasks. We address for this purpose a variant of the workforce scheduling problem called the Technician Routing and Scheduling Problem. This problem has applications in different fields, such as maintenance of transportation infrastructure (rail and road networks), telecommunications, and sewage facilities. To solve the problem, we propose an enhanced iterated local search approach. The enhancement of the iterated local search firstly includes an intensification procedure that incorporates a set of local search operators and removal-repair heuristics adapted for the studied problem. Next, four different mechanisms are used in the perturbation phase. Finally, an elite set of solutions is used to extensively explore the neighborhood of local optima as well as to enhance diversification during search space exploration. To measure the performance of the proposed method, experiments were conducted based on benchmark instances from the literature, and the results obtained were compared with those of an existing method. Our method achieved very good results, since it reached the best overall gap, which is almost three times lower than that of the literature. Furthermore, eILS improved the best-known solution for 31 instances among a total of 56 while maintaining reasonable computational times.