Open Access

Lorenz Wagner, Sebastian Schiffels, Jens O. Brunner

• Problem definition: An efficient use of medical staff, i.e., efficient scheduling, is vital for hospitals due to increasing economic pressure. However, the shortage of skilled workers in hospitals is a serious problem that partly results from unattractive working hours, often leading to decreased worker performance. The current state of the literature addressing quantitative healthcare scheduling approaches largely neglects behavioral findings regarding worker behavior. In contrast, behavioral studies reveal that the insights gained should be implemented in workforce schedules. Methodology/results: A Mixed-Integer Linear Programming model extends common hospital regulations with constraints capturing performance degradation and recovery from scheduling inconsistencies. It models how irregular shifts impair, and consistent schedules restore, worker performance. To address computational complexity, the compact model is decomposed via Dantzig-Wolfe reformulation and solved through columnProblem definition: An efficient use of medical staff, i.e., efficient scheduling, is vital for hospitals due to increasing economic pressure. However, the shortage of skilled workers in hospitals is a serious problem that partly results from unattractive working hours, often leading to decreased worker performance. The current state of the literature addressing quantitative healthcare scheduling approaches largely neglects behavioral findings regarding worker behavior. In contrast, behavioral studies reveal that the insights gained should be implemented in workforce schedules. Methodology/results: A Mixed-Integer Linear Programming model extends common hospital regulations with constraints capturing performance degradation and recovery from scheduling inconsistencies. It models how irregular shifts impair, and consistent schedules restore, worker performance. To address computational complexity, the compact model is decomposed via Dantzig-Wolfe reformulation and solved through column generation, yielding near-optimal solutions. Managerial implications: Our findings demonstrate that accommodating human factors within optimization models can lead to more worker-friendly schedules and greater operational performance in healthcare staff scheduling. Consequently, this work provides a foundation for rethinking workforce scheduling practices to create feasible and desirable schedules from both the employee and management perspectives.…