A Risk-averse Employee Scheduling with Uncertainty Demand

This work studies an employee scheduling problem considering risk aversion 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 recruit part-time employees and extend shift length of full-time employees (i.e, overtime shift), since the actual demand realization. In order to adapt different decision environments, especially facing decision makers with different risk preference, and guarantee scheduling resilience, the risk measure (e.g, conditional value-at-risk (CVaR)) is taken into consideration in the general two-stage stochastic programming framework. A sample average approximation algorithm is used to solve the risk-averse employee scheduling problem with uncertain demand. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.