Developing metaheuristic approaches to solve flow shop scheduling problem with worker assignment

This research addresses a simultaneous jobs scheduling and worker assignment problem in flow shop environment in which there are some workers with different skills who can operate the jobs with different speed. The primary aim of the research is to schedule the jobs and assign the worker so that maximum completion time (Cmax) is minimized. To tackle this problem, a mixed integer linear programming model is introduced and is coded in CPLEX software so that it can obtain the optimal solutions in reasonable time. Due to NP-hardness of the research problem, CPLEX cannot achieve the optimal solutions for large-scale problems. Thus, two metaheuristic approaches based on particle swarm optimization (PSO) is proposed here. In order to trapping the PSO algorithm in local optima with high probability, the performance of the PSO algorithm is improved by simulated annealing (SA) algorithm (IPSO). The experimental results show that the IPSO algorithm can generate better results in entire scales and the superiority of the IPSO is significant in the large scale.