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How can decision makers be supported in the improvement of an emergency department?: A simulation, optimization and data mining approach
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-4604-6429
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-4180-6003
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-5100-4077
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
2017 (English)In: Operations Research for Health Care, ISSN 2211-6923, E-ISSN 2211-6931, Vol. 15, p. 102-122Article in journal (Refereed) Published
Abstract [en]

The improvement of emergency department processes involves the need to take into considerationmultiple variables and objectives in a highly dynamic and unpredictable environment, which makes thedecision-making task extremely challenging. The use of different methodologies and tools to support thedecision-making process is therefore a key issue. This article presents a novel approach in healthcarein which Discrete Event Simulation, Simulation-Based Multi-Objective Optimization and Data Miningtechniques are used in combination. This methodology has been applied for a system improvementanalysis in a Swedish emergency department. As a result of the project, the decision makers were providedwith a range of nearly optimal solutions and design rules which reduce considerably the length of stayand waiting times for emergency department patients. These solutions include the optimal number ofresources and the required level of improvement in key processes. The article presents and discussesthe benefits achieved by applying this methodology, which has proven to be remarkably valuable fordecision-making support, with regard to complex healthcare system design and improvement.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 15, p. 102-122
Keywords [en]
Discrete Event Simulation, Simulation-Based Multi-Objective Optimization, Data mining, Decision support, Decision-making, Operational research in health care
National Category
Health Care Service and Management, Health Policy and Services and Health Economy Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
URN: urn:nbn:se:his:diva-14404DOI: 10.1016/j.orhc.2017.10.003ISI: 000415311000010Scopus ID: 2-s2.0-85032745554OAI: oai:DiVA.org:his-14404DiVA, id: diva2:1157057
Available from: 2017-11-15 Created: 2017-11-15 Last updated: 2019-07-02Bibliographically approved
In thesis
1. Bringing Together Lean, Simulation and Optimization: Defining a framework to support decision-making in system design and improvement
Open this publication in new window or tab >>Bringing Together Lean, Simulation and Optimization: Defining a framework to support decision-making in system design and improvement
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The rapid changes in the market including globalization, the requirement for personalizedproducts and services by the customers, shorter product life-cycles, the exponential growthof technological advances, and the demographical changes, will demand organizations toeffectively improve and design their systems in order to survive. This is the actual paradigmcharacterizing the industrial and service sectors. This scenario presents a considerablechallenge to decision makers who will need to decide about how to design and improve amore than ever complex system without compromising the quality of the decision taken.Lean, being a widely applied management philosophy with very powerful principles, itsmethods and tools are static in nature and have some limitations when it comes to the designand improvement of complex and dynamic systems. Some authors have proposed thecombined use of simulation with Lean in order to overcome these limitations. Furthermore,optimization and post-optimization tools coupled to simulation, provide knowledge aboutoptimal or nearly optimal system configurations to choose from. However, even if Leanprinciples, methods and tools, as well as simulation and optimization, pursue the objectiveof supporting organizations regarding system design and improvement, a bilateral approachfor their combination and its benefits have barely been addressed in the literature.Many studies focus only on how specific Lean tools and simulation can be combined, treatingLean purely as a toolbox and not considering how Lean can support the simulation process.The aim of this research is to address this knowledge gap by analyzing the mutualbenefits and presenting a framework for combining Lean, simulation and optimization tobetter support decision makers in system design and improvement where the limitationsof Lean tools and simulation are overcome by their combination. This framework includesa conceptual framework explaining the relationships between the Lean philosophy, methodsand tools with simulation and optimization; the purposes for this combination and stepby step processes to achieve these purposes; the identification of the roles involved in eachprocess; a maturity model providing guidelines on how to implement the framework; existingbarriers for the implementation; and ethical considerations to take into account. Anindustrial handbook has also been written which explains how to deploy the framework.The research has been conducted in three main stages including an analysis of the literatureand the real-world needs, the definition and formulation of the framework, and finally, itsevaluation in real-world projects and with subject matter experts. The main contributionof this research is the reflection provided on the bilateral benefits of the combination, aswell as the defined and evaluated framework, which will support decision makers take qualitydecisions in system design and improvement even in complex scenarios.

Abstract [sv]

De snabba förändringarna på marknaden såsom globalisering, ökat krav på personliga produkteroch tjänster från kunderna, kortare produktlivscykler, tekniska framsteg med exponentielltillväxt samt demografiska förändringar medför ökat krav på att organisationer effektivtförbättrar och utformar sina system. Detta är det nuvarande paradigmet som karaktäriserarindustri- och tjänstesektorerna. Det är ett scenario som utgör en stor utmaningför beslutsfattare, vilka kommer att behöva ta beslut i än mer komplexa system där det blirsvårare att utforma och förbättra system med bibehållen kvalitet. Lean är en allmänt tilllämpadproduktionsfilosofi med kraftfulla principer, med metoder och verktyg som är statiskatill sin natur vilka har begränsningar när det gäller utformning och förbättring avkomplexa och dynamiska system. En del författare har föreslagit den kombinerade användningenav simulering med Lean för att övervinna dessa begränsningar. Dessutom erbjudersimulering kombinerat med optimering och postoptimeringsverktyg genererandet av kunskapom optimala eller nästan optimala systemkonfigurationer. Även om både Lean-principermed dess metoder och verktyg och simulering syftar till att stödja organisationer vidutformning och förbättring av sina system tar litteraturen upp få fördelar med ett kombinerattillvägagångssätt. Flera studier fokuserar endast på hur specifika verktyg inom Leankan kombineras med simulering men behandlar inte hur Lean som filosofi kan stödja simuleringsprocessen.Syftet med denna forskning är att ta itu med denna kunskapsluckagenom att analysera fördelar med kombinationen Lean, simulering och optimering, samtbeskriva hur dess svagheter var för sig kan överbryggas när de kombineras. Resultatet presenterasi ett ramverk med beskrivning av dess genomförande, vilket syftar till att genereraett bättre beslutsstöd vid utformning och förbättring av komplexa och dynamiska system.Ramverket innefattar ett konceptuellt ramverk som förklarar relationerna mellan Lean-filosofin,dess metoder och verktyg, med simulering och optimering; olika ändamål för kombinationenbeskrivs, samt genomgång av steg för steg processer ges för att uppnå dessaändamål; identifiering av de roller som är inblandade i varje process beskrivs; en mognadsmodellpresenteras som ger riktlinjer för hur man implementerar ramverket; befintligahinder för genomförandet och etiska överväganden att ta hänsyn till lyfts också fram. Slutligenhar en industriell handbok skrivits som förklarar hur man ska implementera ramverket.Forskningen har genomförts i tre faser, bland annat en behovsanalys utifrån litteraturenoch verkliga projekt, definitionen av ramverket, och avslutningsvis utvärderas det genomverkliga projekt och med ämnesexperter. Huvudbidraget för denna forskning är reflektionenöver det extra utbytet med kombinationen Lean, simulering och optimering medXIIdess fördelar, vilket bygger på det framtagna ramverket och dess utvärdering. Där ramverkethar för avsikt att stödja beslutsfattare att fatta kvalitetsbeslut vid utformning och identifieringav förbättringar i sina system även i komplexa scenarier.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2019. p. 430
Series
Dissertation Series ; 29 (2019)
Keywords
lean, simulation, optimization, decision-making
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17368 (URN)978-91-984918-1-4 (ISBN)
Public defence
2019-08-16, Insikten, Portalen, Skövde, 09:30 (English)
Opponent
Supervisors
Note

List of papers / Delarbete 7 of 8. Goienetxea Uriarte, A., Ng A. H. C. and Urenda Moris, M. (2019). Bringing together Lean and simulation: A comprehensive review. Submitted. 

List of papers / Delarbete 8 of 8. Goienetxea Uriarte, A., Ng A. H. C. and Urenda Moris, M. (2019). Bringing together Lean, simulation and optimization: A reflection and framework proposal. Submitted.

Available from: 2019-07-03 Created: 2019-07-02 Last updated: 2019-07-03Bibliographically approved

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Goienetxea et al. 2017(4495 kB)434 downloads
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Goienetxea Uriarte, AinhoaRuiz Zúñiga, EnriqueUrenda Moris, MatíasNg, Amos H. C.

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Health Care Service and Management, Health Policy and Services and Health EconomyProduction Engineering, Human Work Science and Ergonomics

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