Advance Admission Scheduling via Resource Satisficing

38 Pages Posted: 12 Jun 2019 Last revised: 2 Jan 2020

See all articles by Minglong Zhou

Minglong Zhou

Fudan University - School of Management

Melvyn Sim

National University of Singapore (NUS) - NUS Business School

Sean Lam Shao Wei

National University of Singapore (NUS) - Programme in Health Services and Systems Research; Singapore Health Services Pte Ltd

Date Written: Dec 30, 2019

Abstract

We study the problem of advance scheduling of ward admission requests in a public hospital, which affects the usage of critical resources such as operating theaters and hospital beds. Given the stochastic arrivals of patients and their uncertain usage of resources, it is often infeasible for the planner to devise a risk-free schedule to meet these requests without violating resource capacity constraints and creating negative effects that include healthcare overtime, longer patient waiting times, and even bed shortages. The difficulty of quantifying these costs and the need to safeguard against their overutilization lead us to propose a resource satisficing framework that renders the violation of resource constraints less likely and also diminishes their impact whenever they occur. The risk of resource overutilization is captured by our resource satisficing index (RSI), which is inspired by Aumann and Serrano (2008) riskiness index and is calibrated to coincide with the expected utilization rate when the random resource usage corresponds to some referenced probability distribution commonly associated with the type of resource. RSI, unlike the expected utilization rate, is risk sensitive and could better mitigate the risks of overutilization. Our satisficing approach aims to balance out the overutilization risks by minimizing the largest RSIs among all resources and time periods, which, under our proposed partial adaptive scheduling policy, can be formulated and solved via a converging sequence of mixed-integer optimization problems. A computational study establishes that our approach reduces resource overutilization risks to a greater extent than does the benchmark method using the first fit (FF) heuristics.

Keywords: optimization, scheduling

Suggested Citation

Zhou, Minglong and Sim, Melvyn and Wei, Sean Lam Shao, Advance Admission Scheduling via Resource Satisficing (Dec 30, 2019). Available at SSRN: https://ssrn.com/abstract=3394845 or http://dx.doi.org/10.2139/ssrn.3394845

Minglong Zhou (Contact Author)

Fudan University - School of Management ( email )

No. 670, Guoshun Road
No.670 Guoshun Road
Shanghai, 200433
China

HOME PAGE: http://https://sites.google.com/view/minglongzhou

Melvyn Sim

National University of Singapore (NUS) - NUS Business School ( email )

1 Business Link
Singapore, 117592
Singapore

Sean Lam Shao Wei

National University of Singapore (NUS) - Programme in Health Services and Systems Research ( email )

8 College Road
Singapore
Singapore

Singapore Health Services Pte Ltd ( email )

7 Hospital Drive
Block A, Room #02-01
Singapore, 597627
Singapore

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