Surgery scheduling in flexible operating rooms by using a convex surrogate model of second-stage costs
研究医院共享手术室中择期与急诊手术的排程问题，提出两阶段随机规划模型，用凸分段线性替代模型快速求解，相比启发式方法可降低预期成本30%。
We study the elective surgery planning problem in a hospital with operating rooms shared by elective and emergency patients. This problem is split in two distinct phases. First, a subset of patients to be operated in the next planning period is selected and the selected patients are assigned to a block and a tentative starting time. Then, in the online phase of the problem, a policy decides how to insert the emergency patients in the schedule and may cancel planned surgeries. The overall goal is to minimize the expectation of a cost function representing the assignment of patient to blocks, case cancellations, overtime, waiting time and idle time. We model the offline problem by a two-stage stochastic program, and show that the optimal second-stage costs can be approximated by a convex piecewise linear surrogate model that can be computed in a preprocessing step. This results in a mixed integer program which can be solved very fast, even for large instances of the problem. We also describe a greedy policy for the online phase of the problem, and analyze the performance of our approach by comparing it to both heuristic methods or approaches relying on sampling average approximation (SAA) on a large set of benchmarking instances. Our simulations indicate that our approach can reduce the expected costs by as much as 30% compared to heuristic methods and it can solve problems with 1000 patients in about one minute, while SAA-approaches fail to obtain good solutions within 30 min on small instances.