Part-Time Workforces Scheduling with Variable Productivity

This research aims to incorporate human factors engineering into the scheduling theory in order to exploit optimized human performance. Staff scheduling issue in which part-time employees have variable performance was studied in this paper. Objective function of the mathematical model is to minimize staffing costs and provided model tries to determine best shifts duration and employees assignments. The unique characteristic and novelty of this study is consideration of ergonomic aspect (fatigue rate of employees) in staff scheduling problem. We used genetic algorithm to solve model. In order to examine effectiveness and efficiency of the model, a set of problems were solved. Also efficiency of GA algorithm results were compared against LINGO results. Comparison of results demonstrated that GA algorithm has good ability to find satisfying solution in reasonable computational running time. This study showed that we can model human fatigue in employee scheduling and planning and consider flexible working shifts to decrease labor cost and increase production efficiency. In order to study policies of decreasing labor fatigue and increasing his/her working capacity, applying provided model were suggested for comparing cost of policies (such as education, job rotation, automation and …) against economic benefits of them in scheduling and choosing the best.