Data-driven robust flexible personnel scheduling

Abstract

Personnel scheduling in various industries often faces challenges due to unpredictable workloads. This paper focuses on the general flexible personnel scheduling problem at the operational level, which is characterized by uncertain demand and limited knowledge of the true distribution of this demand. To address this issue, we propose a distributionally robust model that utilizes the Wasserstein ambiguity set. This model is designed to maintain service levels across the worst-case distribution scenarios of random demand. In addition, we introduce a robust satisficing model that is oriented towards specific targets, offering practical applicability in real-world situations. Both models leverage empirical distributions derived from historical data, enabling the generation of robust personnel schedules that are responsive to uncertain demand, even when data availability is limited. We demonstrate that these robust models can be transformed into tractable counterparts. Moreover, we develop an exact depth-first search algorithm for identifying feasible daily schedules. Through a comprehensive case study and experiments using real-world data, we showcase the effectiveness and advantages of our proposed models and algorithms. The robustness of our models is thoroughly evaluated, providing valuable management insights and demonstrating their ability to tackle scheduling challenges in uncertain environments.