Tour scheduling for part-time employee with variable productivity

Overall goal of our research is to incorporate human factors engineering into scheduling theory in order to exploit optimized human performance. Tour scheduling problem in which part-time employees have variable performance have been studied in this paper. Our model objective function is minimization of costs of staffing and tries to determine the best shift duration and employees assignment in shifts. The unique characteristic of this study is consideration of ergonomic aspect (fatigue rate of employees) in tour scheduling problem. We used genetic algorithm to conquest difficulty of our model and to find desirable solution in a reasonable running time. In order to show effectiveness and efficiency of our model we generated sets of test problems with different sizes. Using LINGO, we examined the performance of genetic algorithm for a variety of instances. The comparison results demonstrated satisfactory performance of genetic algorithm in terms of computational time and quality of solutions.