In this study, the researchers propose an effective algorithm that minimizes the operating room overtime cost, bed shortage cost, and patient waiting cost.
Authors
Sachin Kumar Mangla, Full Professor and Director, Research Center for Digital Circular Economy for Sustainable Development Goals (DCE-SDG), Jindal Global Business School, O.P. Jindal Global University, Haryana, India.
Zongli Dai, School of Economics and Management, Dalian University of Technology, Dalian, China.
Sandun C. Perera, College of Business, University of Nevada, Reno, Nevada, USA.
Jian-Jun Wang, School of Economics and Management, Dalian University of Technology, Dalian, China
Guo Li, School of Management and Economics, Beijing Institute of Technology, China; Center for Energy and Environmental Policy Research, Beijing Institute of Technology, China; Sustainable Development Research Institute for Economy and Society of Beijing, China.
Summary
Amid the epidemic outbreaks such as COVID-19, a large number of patients occupy inpatient and intensive care unit (ICU) beds, thereby making the availability of beds uncertain and scarce. Thus, elective surgery scheduling not only needs to deal with the uncertainty of the surgery duration and length of stay in the ward, but also the uncertainty in demand for ICU and inpatient beds.
We model this surgery scheduling problem with uncertainty and propose an effective algorithm that minimizes the operating room overtime cost, bed shortage cost, and patient waiting cost. Our model is developed using fuzzy sets whereas the proposed algorithm is based on the differential evolution algorithm and heuristic rules.
We set up experiments based on data and expert experience respectively. A comparison between the fuzzy model and the crisp (non-fuzzy) model proves the usefulness of the fuzzy model when the data is not sufficient or available. We further compare the proposed model and algorithm with several extant models and algorithms, and demonstrate the computational efficacy, robustness, and adaptability of the proposed framework.
Published in: Computers & Industrial Engineering
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