Tour scheduling for part-time employee with variable productivity

Abstract

Goal of our research is incorporation of human factors engineering into the scheduling theory in order to exploit optimised human performance. Tour scheduling problem in which part-time employees have variable performance was studied in this paper. We provided mathematical model, and its objective function was minimisation of staffing costs. Decision variables of model were determination of shifts duration and employees assignments. The unique characteristic of this study was consideration of ergonomic aspect (fatigue rate of employees) in tour scheduling problem. We used genetic algorithm to conquest difficulty of the model and to find desirable solution in a reasonable running time. In order to show effectiveness and efficiency of the model we generated sets of problems with different sizes. Using LINGO, we examined the performance of genetic algorithm for variety of instances. The comparison results demonstrated satisfactory performance of genetic algorithm in terms of computational time and quality of solutions. Also sensitivity analysis for the problems indicated that genetic algorithm had good ability to search solution space efficiently even for larger problems.