Efficient optimization algorithms for surgical scheduling under uncertainty

Shing Chih Tsai, Yingchieh Yeh, Chen Yun Kuo

研究成果: Article同行評審

12 引文 斯高帕斯(Scopus)


In this paper, we develop a stochastic optimization model for a surgical scheduling problem considering a single operating room. We arrange a set of elective surgeries into appropriate time blocks, and determine their planned start time and specific sequence. Due to the complexity of the original formulation, we reformulate our model as a two-stage mixed-integer problem. We consider the planning decision in the first stage and the sequencing decision in the second stage (based on the first one). The goal of this paper is to obtain a nearly optimal schedule in reasonable computational time. The term “optimal” is defined as the lowest surgically related cost while achieving the given threshold with respect to some specific deterministic or stochastic performance measures. The optimization model involves expected and probabilistic formulations that are analytically intractable. This implies that traditional mathematical programming techniques cannot be used directly. Therefore, we propose adapted rapid-screening and stochastic-approximation algorithms to deal with the first-stage and the second-stage problems, respectively. In both algorithms, we can apply either the Laplace transform or simulation methods to either evaluate or estimate the desired performance measures. The experimental results demonstrate that the proposed algorithms are more favorable compared to existing approaches.

頁(從 - 到)579-593
期刊European Journal of Operational Research
出版狀態Published - 2021 9月 1

All Science Journal Classification (ASJC) codes

  • 一般電腦科學
  • 建模與模擬
  • 管理科學與經營研究
  • 資訊系統與管理


深入研究「Efficient optimization algorithms for surgical scheduling under uncertainty」主題。共同形成了獨特的指紋。