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

HEALTH CARE MANAGEMENT SCIENCE 2017年第1期20卷 55-75页

作者： Feng, Yen-Yi Wu, I-Chin Chen, Tzu-Li Mackay Mem Hosp Dept Emergency Med Taipei 10449 Taiwan Fu Jen Catholic Univ Dept Informat Management New Taipei 24205 Taiwan

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.

关键词： Simulation optimization Non-dominated sorting genetic algorithm multi-objective computing budget allocation Medical resource allocation Emergency department

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multi-objective simulation optimization for medical capacity allocation in emergency department

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JOURNAL OF SIMULATION 2016年第1期10卷 50-68页

作者： Chen, T-L Wang, C-C Fu Jen Catholic Univ New Taipei 24205 Taiwan

This study aims to investigate medical resource allocation problems by multi-objective simulation optimization to address the long-term overcrowding situations experienced in hospital emergency departments (EDs). With resource constraints considered, decision makers at EDs must determine the number of doctors, nurses, lab technicians, and other medical equipment to allocate efficiently these medical resources and simultaneously minimize the average patient length of stay in the system and the medical resource wasted cost. Therefore, this study first proposes a multi-objective stochastic optimization model to identify the optimal number of all medical resources at the EDs. In addition, a multi-objective simulation optimization algorithm by integrating non-dominated sorting particle swarm optimization (NSPSO) with multi-objective computing budget allocation (MOCBA) and an ED simulation model is developed and constructed to address this problem, respectively. Specifically, NSPSO searches for potential solutions to medical resource allocation problems. MOCBA identifies effective sets of feasible Pareto medical resource allocation solutions and effective allocation of simulation replications. An ED simulation model based on the operation flows of EDs in Taiwan was constructed to estimate the expected performance value of each resource allocation solution generated by NSPSO. The effectiveness and performance of integrated NSPSO and MOCBA was verified by computational experiments.

关键词： simulation optimization non-dominated sorting particle swarm optimization multi-objective computing budget allocation resource allocation emergency department

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Differentiated service inventory optimization using nested partitions and MOCBA

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COMPUTERS & OPERATIONS RESEARCH 2009年第5期36卷 1703-1710页

作者： Chew, Ek Peng Lee, Loo Hay Teng, Suyan Koh, Choon Hwee Natl Univ Singapore Dept Ind & Syst Engn Singapore 119260 Singapore

In this paper, we consider a differentiated service inventory problem with multiple demand classes. Given that the demand from each class is stochastic, we apply a continuous review policy with dynamic threshold curves to provide differentiated services to the demand classes in order to optimize both the cost and the service level. The difficult features associated with the problem are the huge search space, the multi-objective problem which requires finding a non-dominated set of solutions and the accuracy in estimating the parameters. To address the above issues, we propose an approach that uses simulation to estimate the performance, nested partitions (NP) method to search for promising solutions, and multi-objective optimal computing budget allocation (MOCBA) algorithm to identify the non-dominated solutions and to efficiently allocate the simulation budget, Some computational experiments are carried Out to test the effectiveness and performance of the proposed solution framework. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Nested partitions method multi-objective computing budget allocation Pareto optimality Differentiated service Inventory

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multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2008年第2期189卷 476-491页

作者： Lee, Loo Hay Chew, Ek Peng Teng, Suyan Chen, Yankai Natl Univ Singapore Dept Ind & Syst Engn Singapore 119260 Singapore

Simulation optimization has received considerable attention from both simulation researchers and practitioners. In this study.. we develop a solution framework which integrates multi-objective evolutionary algorithm (MOEA) with multi-objective computing budget allocation (MOCBA) method for the multi-objective simulation optimization problem. We apply it on a multi-objective aircraft spare parts allocation problem to find a set of non-dominated solutions. The problem has three features: huge search space, multi-objective, and high variability. To address these difficulties, the solution framework employs simulation to estimate the performance, MOEA to search for the more promising designs, and MOCBA algorithm. to identify the non-dominated designs and efficiently allocate the simulation budget. Some computational experiments are carried out to test the effectiveness and performance of the proposed solution framework. (C) 2007 Elsevier B.V. All rights reserved.

关键词： multi-objective simulation optimization evolutionary computing multi-objective computing budget allocation Pareto optimality spare parts inventory problem

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