A Markov decision model with dead ends for operating room planning considering dynamic patient priority
RAIRO - Operations Research, ISSN: 0399-0559, Vol: 53, Issue: 5, Page: 1819-1841
2019
- 16Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures16
- Readers16
- 16
Article Description
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.
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