2026-06-04 コンコルディア大学
◆研究チームは、手術時間の変動や医療資源の制約を考慮した数理最適化モデルを構築し、限られた手術室や医療スタッフをより効率的に割り当てるアルゴリズムを開発した。シミュレーション評価の結果、この手法は従来のスケジューリング方式と比較して待機時間の短縮や手術室稼働率の向上を実現できることが示された。また、緊急症例への対応能力を維持しながら、予定手術患者へのサービス改善も期待できる。
◆研究チームは、本技術が医療機関の運営効率向上だけでなく、患者満足度の向上や医療費削減にも寄与すると考えている。高齢化や医療需要の増加が進む中、医療資源を最大限活用するための意思決定支援ツールとして実用化が期待される。
＜関連情報＞
- https://www.concordia.ca/cunews/encs/2026/06/04/research-a-new-scheduling-tool-could-help-hospitals-reduce-surgical-wait-times.html
- https://www.tandfonline.com/doi/full/10.1080/00207543.2026.2637778
強化学習に基づく、手術室の計画とスケジューリングを統合した列生成アルゴリズム A reinforcement-learning-based column generation algorithm for integrated operating room planning and scheduling
Mahdi Dolatkhah,Hossein Hashemi Doulabi,Walter Rei & Michel Gendreau
International Journal of Production Research Published:13 Mar 2026
DOI:https://doi.org/10.1080/00207543.2026.2637778
Abstract
Operating room planning and scheduling are vital components of hospital management, contributing to improved efficiency, patient satisfaction, staff well-being, and overall quality of care delivery. In this paper, we propose a novel mixed integer programming model to formulate integrated operating room planning and scheduling problems, where several mandatory and elective surgeries are to be assigned and scheduled in operating rooms on different days. Aside from the standard working hours in each operating room, we also take into account the potential for performing surgeries in overtime periods. In addition, our approach also takes into account the availability of surgeons by considering their allowed surgical time on each day. We propose a column generation (CG) algorithm to solve large-scale instances. In order to enhance the CG, we integrate the Reinforcement Learning Algorithm and the Genetic Algorithm and develop a hybrid algorithm to generate initial columns for the CG algorithm. For our analysis, we employed two sets of test instances: one consisting of synthetic data and the other based on real-world cases from a local hospital in Naples, Italy. Computational experiments demonstrate that our proposed model and methodology yields an average optimality gap of 1.23% for synthetic instances and 1.49% on real-world scenarios, significantly outperforming previous solution methodologies in the literature. Additionally, we demonstrate that the developed CG algorithm provides a high-quality solution for large-scale instances where other models and methods fail to obtain even a feasible solution. To further evaluate robustness under uncertainty, we examined scenarios with ±20% variability in surgery durations. The results indicate that incorporating a 120-minute buffer time minimises the overall cost. Moreover, we investigated the impact of emergency surgeries by either introducing additional cases or escalating surgical priorities. For synthetic instances, the inclusion of emergency surgeries increased the total rescheduling cost by 4.13%, whereas in the real-world Naples cases, priority escalation led to only a 0.11% increase, highlighting the resilience of our proposed model in practical hospital settings.
