Hybrid differential evolution and particle swarm optimization for Multi-visit and Multi-period workforce scheduling and routing problems

This research proposed an optimization method (Hybrid Differential Evolution and Particle Swarm Optimization, HDEPSO) using a solution technique based on two well-known techniques, Differential Evolution (DE) and Particle Swarm Optimization (PSO), to tackle a multi-visit and multi-period workforce scheduling and routing problem (MMWSRP) in field service operation of a sugarcane mill company in Thailand. The HDEPSO can be used for planning of routes and maintenance work for each sugarcane harvester to be provided by service teams of mechanical, hydraulic, and electrical technicians. The members of the service teams will be determined 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 HDEPSO was therefore developed to solve the MMWSRP and then 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 HDEPSO was much more advantageous. The HDEPSO was also tested against the DE and PSO. The computational results show that the objective value of the proposed method was decreased by 4.94% and 7.45% compared with the DE and the PSO, respectively.

Automated summary

This research proposed an optimization method called HDEPSO, based on Differential Evolution and Particle Swarm Optimization, for solving workforce scheduling and routing problems in the field service operation of a sugarcane mill company. The results showed that the HDEPSO method outperformed mixed integer programing, as well as the DE and PSO methods, especially for larger-size problems.