Suchergebnisse

The book presents new ideas to model and solve the flexible shift planning problem of personnel workers in the service industry. First, a new modeling approach is proposed that requires shifts to be generated implicitly rather than employing a predefined set of shift types like three 8-hour or two 12-hour shifts to cover varying forecast demand. The objective is to minimize the total cost of the assignments given the general and individual labor restrictions. Second, to find high quality assignments two solution methodologies are presented. A heuristic decomposition strategy decomposes the problem into weekly subproblems whereas a branch-and-price algorithm that uses several branching rules decomposes the model by worker types. The master problem uses a set covering formulation whereas the subproblems are mixed integer programs. The modeling and solution methodologies are developed and tested using the example of physicians from an anesthesia department of a German university hospital.

AbstractIn a master surgery scheduling (MSS) problem, a hospital's operating room (OR) capacity is assigned to different medical specialties. This task is critical since the risk of assigning too much or too little OR time to a specialty is associated with overtime or deficit hours of the staff, deferral or delay of surgeries, and unsatisfied—or even endangered—patients. Most MSS approaches in the literature focus only on the OR while neglecting the impact on downstream units or reflect a simplified version of the real‐world situation. We present the first prediction model for the integrated OR scheduling problem based on machine learning. Our three‐step approach focuses on the intensive care unit (ICU) and reflects elective and urgent patients, inpatients and outpatients, and all possible paths through the hospital. We provide an empirical evaluation of our method with surgery data for Universitätsklinikum Augsburg, a German tertiary care hospital with 1700 beds. We show that our model outperforms a state‐of‐the‐art model by 43% in number of predicted beds. Our model can be used as supporting tool for hospital managers or incorporated in an optimization model. Eventually, we provide guidance to support hospital managers in scheduling surgeries more efficiently.