A hybrid simulation and optimisation approach for capacity allocation of operating rooms under uncertainty

Abstract

Operating rooms are the most important assets of hospitals whose capacity allocation to different specialties is critical and challenging. This paper proposes a hybrid simulation and optimisation approach for the strategic surgery capacity allocation problem over a yearly horizon. First, a simulation model is proposed to specify an appropriate percentage of the total available time of operating rooms in each period which is allocated to emergency patients. Then, a new mathematical model is developed to determine the optimal capacity plan (i.e. resource allocation) for operating rooms by handling both elective and emergency patients under uncertainty of surgery demands, surgery times and length of stays. The model aims to minimise the total under-time and the number of patients on the waiting list according to their surgery priorities. A tailored fix and relax algorithm is devised to solve the problem efficiently. Finally, a real case study in a private hospital along with some test problems are provided to evaluate the proposed model and its solution method. The numerical results demonstrate the desirable performance of the proposed solution approach. Moreover, the results indicate a significant reduction in the total under-time and the number of beds allocated in each period.

KEYWORDS:

• Operating rooms
• capacity allocation
• simulation
• fix and relax algorithm
• fuzzy chance-constrained programming

Compliance with ethical standards

Ethical approval

The authors certify that this paper does not contain any studies or involvement with human participants or animals performed by any authors in any organisation or entity with any financial or non-financial interest in the subject matter or materials discussed in this paper.

Disclosure statement

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

Data availability statement

The data that support the findings of this study are available from the authors upon reasonable request.

Notes

1 Hospital information system.

Additional information

Notes on contributors

Ahmad Ghasemkhani

Ahmad Ghasemkhani is a Ph.D. candidate in Industrial Engineering at the University of Oklahoma, located in Norman, Oklahoma, USA. He holds a Master's degree in Industrial Engineering from the University of Tehran, Iran. Ahmad's research interests primarily focus on the Resilience of Networks, Supply chains, and healthcare systems specifically in the areas of operating room scheduling and data analysis. He has made valuable contributions to the field of healthcare optimization and supply chain, with his papers published in reputable journals and conferences.

S. Ali Torabi

S. Ali Torabi is a full Professor in Operations and Supply Chain Management at the School of Industrial Engineering, College of Engineering, University of Tehran, Iran. He specializes in developing novel mathematical models and solutions methods for business decision problems with a focus on supply chain management related decision problems mostly by using Multiple Criteria Decision-Making techniques and uncertainty programming approaches. He has published many papers in accredited Journals such as EJOR, TRE, COR, FSS, IJPE, IJPR, JORS and CAIE. He has been among the Highly Cited researchers based on the reports of Thomson Reuters in 2015-2018.

Mahdi Hamid

Mahdi Hamid is a Ph.D. candidate in Industrial Engineering from the College of Engineering, University of Tehran, Iran. He obtained his master's degree from the School of Industrial Engineering at the University of Tehran, Iran, in 2015. His research interests are mathematical modelling, operation research, logistics, simulation, data analysis, and healthcare engineering. He has published more than 40 papers in reputable conferences and academic journals.