A Markov decision model with dead ends for operating room planning considering dynamic patient priority

Abstract

This paper addresses an operating room planning problem with surgical demands from both the elective patients and the non-elective ones. A dynamic waiting list is established to prioritize and manage the patients according to their urgency levels and waiting times. In every decision period, sequential decisions are taken by selecting high-priority patients from the waiting list to be scheduled. With consideration of random arrivals of new patients and uncertain surgery durations, the studied problem is formulated as a novel Markov decision process model with dead ends. The objective is to optimize a combinatorial cost function involving patient waiting times and operating room over-utilizations. Considering that the conventional dynamic programming algorithms have difficulties in coping with large-scale problems, we apply several adapted real-time dynamic programming algorithms to solve the proposed model. In numerical experiments, we firstly apply different algorithms to solve the same instance and compare the computational efficiencies. Then, to evaluate the effects of dead ends on the policy and the computation, we conduct simulations for multiple instances with the same problem scale but different dead ends. Experimental results indicate that incorporating dead ends into the model helps to significantly shorten the patient waiting times and improve the computational efficiency.

An approximate dynamic programming approach to the admission control of elective patients

“…Specifically, J. Zhang et al (2018) employ VI and the above-mentioned RTDP algorithms to solve patient admission control problems. Their experimental results show that VPI-RTDP outperforms VI significantly and that it is the best DP-based algorithm in terms of accuracy and efficiency.…”