4,607 research outputs found

    Towards the Holy Grail: combining system dynamics and discrete-event simulation in healthcare

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    The idea of combining discrete-event simulation and system dynamics has been a topic of debate in theoperations research community for over a decade. Many authors have considered the potential benefits ofsuch an approach from a methodological or practical standpoint. However, despite numerous examples ofmodels with both discrete and continuous parameters in the computer science and engineering literature,nobody in the OR field has yet succeeded in developing a genuinely hybrid approach which truly integratesthe philosophical approach and technical merits of both DES and SD in a single model. In this paperwe consider some of the reasons for this and describe two practical healthcare examples of combinedDES/SD models, which nevertheless fall short of the “holy grail” which has been so widely discussed inthe literature over the past decade

    A multi-paradigm, whole system view of health and social care for age-related macular degeneration

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    A toolkit of designs for mixing discrete event simulation and system dynamics

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    In recent years there has been significant interest in multimethodology and the mixing of OR/MS methods, including Discrete Event Simulation (DES) with System Dynamics (SD). Several examples of mixing DES and SD are described in the literature but there is no overarching framework which characterises the spectrum of options available to modellers. This paper draws on a sample of published case studies, in conjunction with the theoretical literature on mixing methods, to propose a toolkit of designs for mixing DES and SD which can be implemented as a set of questions which a modeller should ask in order to guide the choice of design and inform the associated project methodology. The impetus for this work was the perceived need to transfer insight from reported practice in order to formalise how the two methods can be and have been mixed

    From Hybrid Simulation to Hybrid Systems Modelling

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    This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Hybrid Simulation (HS) is the combined application of simulation approaches like SD, DES and ABS in the model implementation stage of a simulation study. Its objective is to better represent the system under scrutiny. Hybrid Systems Modelling (HSM), on the other hand, is the combined application of simulation with methods and techniques from disciplines such as Applied Computing, Computer Science, Engineering and the wider OR. HSM can be applied to multiple stages of a simulation study. In this paper, we present a classification of HS and extend it to include HSM approaches which use simulation with other OR techniques. The paper contributes to the debate on what constitutes HS and offers a unifying conceptual representation for mixing simulation approaches with HSM methods and techniques

    An investigation into modeling and simulation approaches for sustainable operations management

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    Modeling and simulation (M&S) studies have been widely used in industry to gain insights into existing or proposed systems of interest. The majority of these studies focus on productivity-related measures to evaluate systems' performance. This paradigm, however, needs to be shifted to cope with the advent of sustainability, as it is increasingly becoming an important issue in the managerial and the organizational agendas. The application of M&S to evaluate the often-competing metrics associated with sustainable operations management (SOM) is likely to be a challenge. The aim of this review is to investigate the underlying characteristics of SOM that lend towards modeling of production and service systems, and further to present an informed discussion on the suitability of specific modeling techniques in meeting the competing metrics for SOM. The triple bottom line, which is a widely used concept in sustainability and includes environmental, social, and economic aspects, is used as a benchmark for assessing this. Findings from our research suggest that a hybrid (combined) M&S approach could be an appropriate method for SOM analysis; however, it has its challenges.This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors

    Conceptual System Dynamics and Agent-Based Modelling Simulation of Interorganisational Fairness in Food Value Chains: Research Agenda and Case Studies

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    open16siThe research on which this paper is based formed part of the VALUMICS project “Understanding Food Value Chain and Network Dynamics” funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 727243System dynamics and agent-based simulation modelling approaches have a potential as tools to evaluate the impact of policy related decision making in food value chains. The context is that a food value chain involves flows of multiple products, financial flows and decision making among the food value chain players. Each decision may be viewed from the level of independent actors, each with their own motivations and agenda, but responding to externalities and to the behaviours of other actors. The focus is to show how simulation modelling can be applied to problems such as fairness and power asymmetries in European food value chains by evaluating the outcome of interventions in terms of relevant operational indicators of interorganisational fairness (e.g., profit distribution, market power, bargaining power). The main concepts of system dynamics and agent-based modelling are introduced and the applicability of a hybrid of these methods to food value chains is justified. This approach is outlined as a research agenda, and it is demonstrated how cognitive maps can help in the initial conceptual model building when implemented for specific food value chains studied in the EU Horizon 2020 VALUMICS project. The French wheat to bread chain has many characteristics of food value chains in general and is applied as an example to formulate a model that can be extended to capture the functioning of European FVCs. This work is to be further progressed in a subsequent stream of research for the other food value chain case studies with different governance modes and market organisation, in particular, farmed salmon to fillet, dairy cows to milk and raw tomato to processed tomato.openMcGarraghy S.; Olafsdottir G.; Kazakov R.; Huber E.; Loveluck W.; Gudbrandsdottir I.Y.; Cechura L.; Esposito G.; Samoggia A.; Aubert P.-M.; Barling D.; Duric I.; Jaghdani T.J.; Thakur M.; Saviolidis N.M.; Bogason S.G.McGarraghy S.; Olafsdottir G.; Kazakov R.; Huber E.; Loveluck W.; Gudbrandsdottir I.Y.; Cechura L.; Esposito G.; Samoggia A.; Aubert P.-M.; Barling D.; Duric I.; Jaghdani T.J.; Thakur M.; Saviolidis N.M.; Bogason S.G

    Towards More Nuanced Patient Management: Decomposing Readmission Risk with Survival Models

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    Unplanned hospital readmissions are costly and associated with poorer patient outcomes. Overall readmission rates have also come to be used as performance metrics in reimbursement in healthcare policy, further motivating hospitals to identify and manage high-risk patients. Many models predicting readmission risk have been developed to facilitate the equitable measurement of readmission rates and to support hospital decision-makers in prioritising patients for interventions. However, these models consider the overall risk of readmission and are often restricted to a single time point. This work aims to develop the use of survival models to better support hospital decision-makers in managing readmission risk. First, semi-parametric statistical and nonparametric machine learning models are applied to adult patients admitted via the emergency department at Gold Coast University Hospital (n = 46,659) and Robina Hospital (n = 23,976) in Queensland, Australia. Overall model performance is assessed based on discrimination and calibration, as measured by time-dependent concordance and D-calibration. Second, a framework based on iterative hypothesis development and model fitting is proposed for decomposing readmission risk into persistent, patient-specific baselines and transient, care-related components using a sum of exponential hazards structure. Third, criteria for patient prioritisation based on the duration and magnitude of care-related risk components are developed. The extensibility of the framework and subsequent prioritisation criteria are considered for alternative populations, such as outpatient admissions and specific diagnosis groups, and different modelling techniques. Time-to-event models have rarely been applied for readmission modelling but can provide a rich description of the evolution of readmission risk post-discharge and support more nuanced patient management decisions than simple classification models

    STAMINA: Bioinformatics Platform for Monitoring and Mitigating Pandemic Outbreaks

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    This paper presents the components and integrated outcome of a system that aims to achieve early detection, monitoring and mitigation of pandemic outbreaks. The architecture of the platform aims at providing a number of pandemic-response-related services, on a modular basis, that allows for the easy customization of the platform to address user’s needs per case. This customization is achieved through its ability to deploy only the necessary, loosely coupled services and tools for each case, and by providing a common authentication, data storage and data exchange infrastructure. This way, the platform can provide the necessary services without the burden of additional services that are not of use in the current deployment (e.g., predictive models for pathogens that are not endemic to the deployment area). All the decisions taken for the communication and integration of the tools that compose the platform adhere to this basic principle. The tools presented here as well as their integration is part of the project STAMINA
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