Optimizing human reliability in work shifts scheduling: A real case of clothing job shop

Abstract

In this research, we provide a mathematical model with an objective function of minimizing human errors for the problem of shift scheduling optimization. The unique aspects which are considered in our model are learning, forgetting, fatigue, and rest parameters (which affect the human reliability). Various problems with different human parameters, based on real cases of clothing job shops, are solved to study the efficiency and effectiveness of the optimization model. Two improved bacterial foraging algorithms are presented to handle the complexity of our problem. To examine the capability of the presented swarm intelligence algorithms in obtaining global optimum problem, we compare the results of the algorithms against the solution of basic bacterial foraging algorithm in terms of solutions quality. Results indicate that the solution approaches are more efficient in searching solution space and convergence. Analysis of different shift scheduling shows that in moderate fatigue condition, the scenario of scheduling with continuous work hours is better in terms of productivity. Also, experiments demonstrate that optimum reliable shift schedules are related to human factors. The findings of our research indicate that the presented scheduling model and improved algorithms can provide optimum shift schedules regarding human factors.