An improved particle swarm optimization algorithm to solve hybrid flowshop scheduling problems with the effect of human factors – A case study - Marichelvam, M.K.; Geetha, M.; Tosun, Ömür (Computers & operations research, 2020)
•An improved particle swarm optimization algorithm is proposed to solve the hybrid flow shop scheduling problems to minimize the weighted sum of makespan and total flow time.•The skill factor, age factor, the learning and forgetting factors are considered.•Computational results reveal the effectiveness of the proposed algorithm. This paper addresses the multi-stage hybrid flowshop scheduling problem with identical parallel machines at each stage by considering the effect of human factors. The various levels of labours and the effects of their learning and forgetting are studied. The minimization of the weighted sum of the makespan and total flow time is the objective function. Since the problem is NP-hard, an improved version of the particle swarm optimization (PSO) algorithm is presented to solve the problem. A dispatching rule and a constructive heuristic are incorporated to improve the initial solutions of the PSO algorithm. The variable neighbourhood search (VNS) algorithm is also hybridized with the PSO algorithm to attain the optimal solutions consuming less computational time. An industrial scheduling problem of an automobile manufacturing unit is discussed. Moreover, several instances of the random benchmark problem are used to validate the performance of the proposed algorithm. Computational experiments have been performed and the results prove the effectiveness of the proposed approach.