A self-adaptive differential evolution for the technician routing and scheduling problem

Abstract

This paper presents two approaches for solving the Technician Routing and Scheduling Problem (TRSP). The first approach is an integer programming method, and the second is a Self-Adaptive Differential Evolution (SADE) algorithm. The TRSP involves scheduling jobs for service teams who have different skill sets. These jobs have time constraints and can only be completed by technicians with the necessary skills. The goal of the TRSP is to minimize the operating cost, which includes travel, late service penalties, technician overtime, and subcontracting costs. To evaluate the effectiveness of the proposed SADE and integer programming approaches, we conducted small-scale numerical experiments. We also compared the performance of SADE to that of the conventional Differential Evolution (DE) algorithm on medium and large-scale problems. The results indicate that SADE produces significantly higher quality solutions compared to DE.

Keywords

• technician scheduling
• vehicle routing
• differential evolution