A hybrid genetic algorithm for operating room scheduling

In this research, we studied operating room scheduling problem of assigning a set of surgeries to several multifunctional operating rooms. The objectives are to maximize the utilization of the operating rooms, to minimize the overtime-operating cost, and to minimize the wasting cost for the unused time. To begin with, a revised mathematical model is constructed to assign surgeries to operating rooms within one week. Then, we proposed four easy-to-implement heuristics that can guarantee to find feasible solutions for the studied problem efficiently. Furthermore, we presented four local search procedures that can improve a given solution significantly. Finally, a hybrid genetic algorithm (HGA) that incorporated with initial solutions, local search procedures and elite search procedure is applied to the studied problem. Computational results show that for small problem instances, the HGA can find near optimal solutions efficiently while for large problem instances, the HGA performs significantly better than the four proposed heuristics. We concluded that surgery schedules obtained by using HGA has less wasting cost for the unused time, much higher utilization of operating rooms, and produce less overtime-operating cost.

Allocating operating room block time using historical caseload variability

Operating room (OR) allocation and planning is one of the most important strategic decisions that OR managers face. The number of ORs that a hospital opens depends on the number of blocks that are allocated to the surgical groups, services, or individual surgeons, combined with the amount of open posting time (i.e., first come, first serve posting) that the hospital wants to provide. By allocating too few ORs, a hospital may turn away surgery demand whereas opening too many ORs could prove to be a costly decision. The traditional method of determining block frequency and size considers the average historical surgery demand for each group. However, given that there are penalties to the system for having too much or too little OR time allocated to a group, demand variability should play a role in determining the real OR requirement. In this paper we present an algorithm that allocates block time based on this demand variability, specifically accounting for both over-utilized time (time used beyond the block) and under-utilized time (time unused within the block). This algorithm provides a solution to the situation in which total caseload demand can be accommodated by the total OR resource set, in other words not in a capacity-constrained situation. We have found this scenario to be common among several regional healthcare providers with large OR suites and excess capacity. This algorithm could be used to adjust existing blocks or to assign new blocks to surgeons that did not previously have a block. We also have studied the effect of turnover time on the number of ORs that needs to be allocated. Numerical experiments based on real data from a large health-care provider indicate the opportunity to achieve over 2,900 hours of OR time savings through improved block allocations.