Adaptive large neighborhood search for service technician routing and scheduling problems

Motivated by the problem situation faced by infrastructure service and maintenance providers, we define the service technician routing and scheduling problem with and without team building: a given number of technicians have to complete a given number of service tasks. Each technician disposes of a number of skills at different levels and each task demands technicians that provide the appropriate skills of at least the demanded levels. Time windows at the different service sites have to be respected. In the case where a given task cannot be serviced by any of the technicians, outsourcing costs occur. In addition, in some companies technicians have to be grouped into teams at the beginning of the day since most of the tasks cannot be completed by a single technician. The objective is to minimize the sum of the total routing and outsourcing costs. We solve both problem versions by means of an adaptive large neighborhood search algorithm. It is tested on both artificial and real-world instances; high quality solutions are obtained within short computation times.