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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]

In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed to solve the technician routing and scheduling problem (TRSP). The TRSP consists of the assignment of technicians into teams, the assignment of teams to tasks, the construction of routes, and the selection of the day on which a service is provided by considering the proficiency level of workers and the proficiency requirement of the task. The paper considers the planning horizon as a multi-period covering 5 days, which further increases the complexity of the problem. Then a task can be fulfilled in any one of 5 days. The IPSO algorithm includes a particle swarm optimization (PSO) algorithm and one neighborhood operator. One neighborhood operator is used to avoid the local solution trap since the global best solution found by PSO is falling into a local solution trap. Further, the proposed algorithm's performance is experimentally compared with the branch-and-cut algorithm for the solution of the TRSP, on the benchmark instances generated from the literature. The computational results show that IPSO provides better solutions considering the branch-and-cut algorithm within reasonable computing time. [ABSTRACT FROM AUTHOR]