Hybrid Differential Evolution and Particle Swarm Optimization for Multi-Visit and Multi-Period Workforce Scheduling and Routing Problems

This research proposed an optimization method using a solution technique based on two well-known techniques, differential evolution and particle swarm optimization, to tackle a multi-visit and multi-period workforce scheduling and routing problem in field service operation of a sugarcane mill company in Thailand. The method can be used for planning of routes and maintenance work for each sugarcane harvesters to be provided by service teams of mechanical, hydraulic, and electrical technicians. The method will determine members of the service teams according to their skills and skill levels and service routes for each individual service team so that the operation cost is minimized. At first, mixed integer programing was used to determine the best solution. This technique is, however, not suitable for large-size problems. A Hybrid Differential Evolution and Particle Swarm Optimization method was therefore developed to solve the problem. The method was tested against the mixed integer programing for small-size problems and it was found that both methods were equally effective. However, for larger-size problems, shortcomings of the mixed-integer technique became obvious whereas the hybrid method was much more advantageous. The hybrid method was also tested against the Differential Evolution and Particle Swarm optimization methods. The hybrid method yielded a solution with significantly better solution quality