A simple solution to technician routing and scheduling problem using improved genetic algorithm.

This paper proposes an improved genetic algorithm (IGA) to provide feasible solutions to the technician routing and scheduling problem (TRSP). The TRSP covers the feasible team generation, the assignment of feasible teams to suitable tasks, the proficiency level of workers, routings considering the allowed days, and the skill desire of the task. The paper deals with a five-day multi-period planning horizon, and a task is performed in any one of 5 days. The IGA consists of crossover, mutation, and three neighborhood structures. Mutation and crossover largely try to avoid getting caught in the local solution trap. Three neighborhood structures improve genetic algorithm (GA) by searching for better solutions. Further, the performance of the proposed algorithm is experimentally compared with GA and improved particle swarm optimization (IPSO) algorithm by providing the TRSP solutions on the generated benchmark instances. The numerical results indicate that IGA offers fast and better solutions considering GA and IPSO algorithms. [ABSTRACT FROM AUTHOR]