Applying Discrete Event Simulation (DES) in Healthcare

2015 ◽  
pp. 390-410
Author(s):  
Stavros T. Ponis ◽  
Angelos Delis ◽  
Sotiris P. Gayialis ◽  
Panagiotis Kasimatis ◽  
Joseph Tan
Keyword(s):  
Discrete Event Simulation ◽  
Capacity Planning ◽  
Planning Process ◽  
Discrete Event ◽  
Healthcare Facilities ◽  
Performance Improvements ◽  
Event Simulation ◽  
Health Insurance Provider ◽  
And Performance

This paper highlights the opportunities and challenges of applying Discrete Event Simulation (DES) to support capacity planning of a network of outpatient facilities. Despite an abundance of studies using simulation techniques to examine the operation and performance of outpatient clinics, the problem of capacity allocation and planning of medical services within a network of outpatient healthcare facilities appears to be underexplored. Here, a case study of a health insurance provider that operates a network of six outpatient medical facilities in the US is used to illustrate and explore the synthesizing and adaptive, yet parsimonious nature of using DES methodology for network design and capacity planning. Results of this case study demonstrate that significant performance improvements for the network operator can be achieved with applying DES method to support the network facility capacity planning process.

Applying Discrete Event Simulation (DES) in Healthcare

2013 ◽  
Vol 8 (3) ◽  
pp. 58-79 ◽  
Author(s):  
Stavros T. Ponis ◽  
Angelos Delis ◽  
Sotiris P. Gayialis ◽  
Panagiotis Kasimatis ◽  
Joseph Tan
Keyword(s):  
Discrete Event Simulation ◽  
Capacity Planning ◽  
Planning Process ◽  
Discrete Event ◽  
Capacity Allocation ◽  
Healthcare Facilities ◽  
Performance Improvements ◽  
Event Simulation ◽  
Health Insurance Provider

This paper highlights the opportunities and challenges of applying Discrete Event Simulation (DES) to support capacity planning of a network of outpatient facilities. Despite an abundance of studies using simulation techniques to examine the operation and performance of outpatient clinics, the problem of capacity allocation and planning of medical services within a network of outpatient healthcare facilities appears to be underexplored. Here, a case study of a health insurance provider that operates a network of six outpatient medical facilities in the US is used to illustrate and explore the synthesizing and adaptive, yet parsimonious nature of using DES methodology for network design and capacity planning. Results of this case study demonstrate that significant performance improvements for the network operator can be achieved with applying DES method to support the network facility capacity planning process.

An artificial neural network approach to discrete-event simulation

1995 ◽  
Vol 9 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Ian Flood ◽  
Kenneth Worley
Keyword(s):  
Neural Network ◽  
Discrete Event Simulation ◽  
Network Architecture ◽  
Discrete Event ◽  
Network Approach ◽  
Neural Network Approach ◽  
Event Simulation ◽  
The Neural Network ◽  
Component Processes ◽  
And Performance

AbstractThis paper proposes and evaluates a neural network-based method for simulating manufacturing processes that exhibit both noncontinuous and stochastic behavior processes more conventionally modeled, using discrete-event simulation algorithms. The incentive for developing the technique is its potential for rapid execution of a simulation through parallel processing, and facilitation of the development and improvement of models particularly where there is limited theory describing the dependence between component processes. A brief introduction is provided to a radial-Gaussian neural network architecture and training process, the system adopted for the work presented in this paper. A description of the basic approach proposed for applying this technology to simulation is then described. This involves the use of a modularized neural network approach to model construction and the prediction of the occurrence of events using information retained from several previous states of the simulation. A class of earth-moving systems, comprising a push-dozer and a fleet of scrapers, is used as the basis for assessing the viability and performance of the proposed approach. A series of experiments show the neural network to be capable of both capturing the characteristic behavior and making an accurate prediction of production rates of scraper-based earth-moving systems. The paper concludes with an indication of some areas for further development and evaluation of the technique.

scholarly journals The application of a hybrid simulation modelling framework as a decision-making tool for TPM improvement

2019 ◽  
Vol 25 (3) ◽  
pp. 476-498 ◽  
Author(s):  
Omogbai Oleghe ◽  
Konstantinos Salonitis
Keyword(s):  
Discrete Event Simulation ◽  
Hybrid Model ◽  
Discrete Event ◽  
Simulation Modelling ◽  
Hybrid Modelling ◽  
Content Type ◽  
Event Simulation ◽  
Modelling Framework ◽  
Field Notes

Purpose The purpose of this paper is to promote a system dynamics-discrete event simulation (SD-DES) hybrid modelling framework, one that is useful for investigating problems comprising multifaceted elements which interact and evolve over time, such as is found in TPM. Design/methodology/approach The hybrid modelling framework commences with system observation using field notes which culminate in model conceptualization to structure the problem. Thereafter, an SD-DEShybrid model is designed for the system, and simulated to proffer improvement programmes. The hybrid model emphasises the interactions between key constructs relating to the system, feedback structures and process flow concepts that are the hallmarks of many problems in production. The modelling framework is applied to the TPM operations of a bottling plant where sub-optimal TPM performance was affecting throughput performance. Findings Simulation results for the case study show that intangible human factors such as worker motivation do not significantly affect TPM performance. What is most critical is ensuring full compliance to routine and scheduled maintenance tasks and coordinating the latter to align with rate of machine defect creation. Research limitations/implications The framework was developed with completeness, generality and reuse in view. It remains to be applied to a wide variety of TPM and non-TPM-related problems. Practical implications The developed hybrid model is scalable and can fit into an existing discrete event simulation model of a production system. The case study findings indicate where TPM managers should focus their efforts. Originality/value The investigation of TPM using SD-DES hybrid modelling is a novelty.

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