Elective Surgery Planning In Mobile Operating Theaters Under Uncertain Demand Of Emergency Patients

Mobile operating theaters(MOTs)emerged as a solution in coping with the serious shortage of medical resources in various European countries.A multi-MOT elective surgery planning problem is studied in this thesis.Each MOT consists of an operating room(OR)and a recovery room,i.e.,post-anesthesia care unit(PACU),with two beds,which can be used to perform surgeries and recovery cares for both elective and emergency patients.The problem is to determine the daily assignment of elective surgeries to MOTs under uncertain demand of emergency patients in a finite planning horizon.The objective is to minimize the total daily costs of the elective patients to MOT assignment and the MOT utilization as well as the postponement cost caused by deferring any elective surgery to the next planning horizon.Due to the possible OR blocking in the presence of limited PACU capacity,the daily MOT utilization cost cannot be obtained in an analytical function form in contrast to the existing literature.The daily MOT utilization cost is separated into the components of the daily OR and PACU utilizations that are assumed to be general functions of the total surgery and recovery durations of the elective patients,respectively.The problem is structured as a set-partitioning problem and solved by column generation.The pricing problem that arises from column generation is NP-hard.By effectively characterizing the structural properties of the optimal solution to the continuous relaxation of the pricing problem,an efficient implementation of the branch-andbound procedure can be applied to obtain the optimal integral solution to the pricing problem.For the purpose of implementation,the cost functions of the daily MOT utilization are learned with the data randomly generated according to the associated empirical distributions documented in the literature.Computational results demonstrate that the proposed approach can solve moderate-sized random instances very effectively.Computational results also show that comparing with the traditional OR planning model that(i)does not consider the daily PACU utilization cost and thereby ignores the possible blocking times between surgeries in ORs and(ii)neglects the possible idle times between surgeries in ORs caused by the random arrivals of emergency patients,the model in this thesis results in a total cost average reduction of 15.84% when emergency patients are ignored and 15.78% when they are considered.