Surgery scheduling in the presence of operating room eligibility and dedicated surgeon: an adaptive composite dispatching method

Abstract

Motivated by a real problem in a big hospital in China, we study a daily surgery scheduling problem with operating room eligibility and dedicated surgeon. We model the problem as a parallel machine scheduling problem with machine and resource constraints to minimise the makespan, and innovatively propose an adaptive composite dispatching method to deal with such a strongly NP-hard problem. The dispatching rule is a combination of three popular rules LPT, LFJ and LRW, each of which can deal with some special features of the scheduling problems, and the scaling parameters are estimated through a statistical model learned from historical data. The adaptive composite dispatching method is easy-to-implement, fast, adaptable, robust and flexible. To examine the performance of the proposed solution approach, we first carry out a series of computational experiments showing that the adaptive composite dispatching method works very well compared to the optimal solution. Using a real data set, we further conduct a case study showing that our solution approach can improve the current practice by significantly shortening the makespan and reducing the overnight work.

Keywords:

• Healthcare
• surgery scheduling
• composite dispatching rule
• data-driven modelling
• machine eligibility
• resource constraint

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used in Section 6 contains the patient's private information thus cannot be shared publicly. However, part of the data is available upon request.

Notes

1 We also consider the linear regression model, and it turns out that decision tree achieves a larger $R^2$.

2 For example, https://www.math.hkbu.edu.hk/UniformDesign/.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 71931008, 71520107003,72001220].

Notes on contributors

Shan Wang

Shan Wang is currently an Assistant Professor in the School of Business, Sun Yat-Sen University, Guangzhou, China. Her research interests are operations management, healthcare management and business analytics. She has published in Management Science, The Lancet, Journal of Combinatorial Optimization, and won several best paper awards from INFORMS and POMS.

Huiqiao Su

Huiqiao Su is currently a research scientist in Huawei Corp Ltd. Her research interests are business analytics and optimisation with their applications. She has published in Naval Research Logistics, and Journal of Combinatorial Optimization.

Guohua Wan

Guohua Wan is a distinguished professor in Antai College of Economics and Management, Shanghai Jiao Tong University. His research interests are operations and supply chain management, healthcare management, and business analytics. He has published extensively on these topics in journals such as Management Science, Operations Research, Mathematics of Operations Research, Production and Operations Management, INFORMS Journal on Computing, Naval Research Logistics, IISE Transactions, European Journal of Operational Research, and International Journal of Production Research. He currently serves as a Senior Editor of Production and Operations Management.

Liwei Zhong

Liwei Zhong is the Director of Shanghai Municipal Hospital of Traditional Chinese Medicine. His research interests are healthcare management and hospital management. He has published in The Lancet, BMJ Global Health and Journal of Combinatorial Optimization. He has won several awards from Chinese government for his achievements in healthcare management and hospital management.