Data-Driven Stochastic Programming Approach for Personnel Scheduling in Retailing

This work studies a personnel scheduling problem considering 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 empirical data or distribution information; 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 full-time employee (i.e, overtime shift), since the actual demand realization. In this work, the information, contrary to previously known exact probability distribution, of uncertainty parameter demand is partial known, namely ambiguous. Given sectional distribution information such as mean value, mean absolute deviation (MAD) and support set, an ambiguous set is established. To solve this problem, we construct heuristically a worst-case discrete joint probability distribution and utilized a sample average approximation (SAA) algorithm to approximately solve the problem.