Adaptive operating rooms planning and scheduling: a rolling horizon approach

Accounting for a large portion of the hospital's total revenue and cost, better management of the operating rooms is extremely important in improving healthcare resource utilization. This paper investigates the Operating Rooms (ORs) Planning and Scheduling Problem in a hospital with a modified block scheduling policy. Thus, the candidate patients have to be assigned a date and an operating room/block as well as being sequenced in the assigned operating rooms/blocks. A reserved slack policy is considered to take care of the arrival of emergency patients. Surgery durations are considered to be randomly distributed. In this regard, a stochastic mixed integer linear programming model is proposed that includes different patient, staff and surgeon preferences: minimization of the total patient waiting time, the tardiness, the number of cancellations, the patient surgery start times, the block overtime, the number of surgeon's surgery days within the planning horizon and the sum of the idle times of the surgeons. Two different 2-phase heuristic solution approaches are developed in a rolling horizon framework in order to solve the Adaptive ORs Planning and Scheduling Problem. The efficiency of the solution framework is surveyed by applying real data obtained from hospital records through numerical experiments. The results show that the developed solution framework significantly outperforms the commercial solver CPLEX in terms of solution quality and CPU time, in medium- as well as in large-sized problems. Furthermore, the results show that the assumptions and features made to the formulation (i.e. the modified block scheduling policy, the reserved slack policy, and the stochastic surgery durations) will result in more efficient solutions