Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm

Yen-Yi Feng, I-Chin Wu and Tzu-Li Chen ()
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Yen-Yi Feng: Mackay Memorial Hospital
I-Chin Wu: Fu Jen Catholic University
Tzu-Li Chen: Fu Jen Catholic University

Health Care Management Science, 2017, vol. 20, issue 1, No 4, 55-75

Abstract: Abstract The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic; furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms.

Keywords: Simulation optimization; Non-dominated sorting genetic algorithm; Multi-objective computing budget allocation; Medical resource allocation; Emergency department (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (16)

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DOI: 10.1007/s10729-015-9335-1

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