Many companies that deliver units to customer premises need to provide periodical maintenance and services on request by their field service technicians. A common challenge is to evaluate different design choices, related to staffing decisions, technician scheduling strategies, and technological improvements in order to make the system more efficient.
This work provides a simulation-based field service scheduling solution for supporting decision makers in tackling this challenging problem. The proposed framework relies on an optimization engine for the generation of the daily plans. The study uses AnyLogic as field service scheduling software to evaluate the applicability of such plans by taking into account the stochastic factors. Furthermore, an interface manages the communication between these two components and allows a feedback loop between the simulator and the optimizer to achieve more robust plans.
In this paper, the researchers develop an integrated simulation framework that is capable of analyzing field service operations with advanced dispatching strategies, adapting to stochastic events. Field service tasks include both routine preventive maintenance tasks as well as unexpected repair requests.
The main purpose of the work is to evaluate alternative staffing decisions as well as dispatching strategies before their actual implementation in the field. Compared with analytical approaches, simulation-based field service scheduling software provides a more accurate modeling for the dynamic environment of field service operations. On the other hand, compared with more commonly used simulation models with simplified dispatching decisions, this work describes a more intelligent field service scheduling with optimized dispatching decisions that are capable of adapting during the simulation runs to unexpected stochastic events.