An Employee Scheduling with Combining Average and Worst Case Considerations

Abstract

This work studies an employee scheduling problem considering average and worst case simultaneously under uncertainty demand (i.e., customer traffic) in retailing. Stochastic employee scheduling comprises two stages, the here-and-now decision (i.e., first-stage), before the actual demand is known, is to allocate number of full-time employees to shifts by using some forecast or empirical data; the wait-and-see decision (i.e., second-stage) involving takes some recourse actions, such as recruits part-time employees and extends shift length of fulltime employees (i.e, overtime shift), since the actual demand realization. In order to avoid large customers loss arises as extremely worst scenario happens, the worst case cost is taken into consideration in the second-stage objective function. The objective, in our model, is minimizing the fixed cost of fulltime employee and weight sum of average and worst case cost of recourse action. Thus, the model offers some flexibility to decision makers by putting relative emphasis on the average and worst case’s recourse cost in term of different scheduling schemes. To solve this problem, a modified sample average approximation algorithm, which combining the clustering technique, is proposed.