Abstrakt

Managing variance in the integrated surgery and physician scheduling problem

Mario Vanhoucke*

Uncertainty is an element that cannot be underestimated nor ignored in health care optimiza-tion. Whereas deterministic optimization problems are formulated with known parameters, real world problems almost invariably include some unknown parameters. The Integrated Physician and Surgery Scheduling Problem (IPSSP) can be formulated as a stochastic programming model since the surgery duration comes with a prede ned level of uncertainty. The stochastic IPSSP is formulated as a two stage stochastic program with integer recourse, which is solved by the L-shaped method. In this paper, we evaluate the computational enhancements of the exact L-shaped method. In order to solve stochastic problems concerning many scenarios, we developed a sampling method to generate approximate solutions to the stochastic IPSSP. Simulation results prove our approach to be better than the expected value equivalent while only requiring a little more computation time, without taking into account the predictive error in the duration of the cases.