The DH Accident and Emergency Department model: a national generic model used locally

The Department of Health (DH) Accident and Emergency (A&E) simulation model was developed by Operational Research analysts within DH to inform the national policy team of significant barriers to the national target, for England, that 98% of all A&E attendances are to be completed (discharged, transferred or admitted) within 4?hours of arrival by December 2004. This paper discusses why the model was developed, the structure of the model, and the impact when used to inform national policy development. The model was then used as a consultancy tool to aid struggling hospital trusts to improve their A&E departments. The paper discusses these experiences with particular reference to the challenges of using a ‘generic’ national model for ‘specific’ local use.     

A participative and facilitative conceptual modelling framework for discrete event simulation studies in healthcare

Existing approaches to conceptual modelling (CM) in discrete-event simulation do not formally support the participation of a group of stakeholders. Simulation in healthcare can benefit from stakeholder participation as it makes possible to share multiple views and tacit knowledge from different parts of the system. We put forward a framework tailored to healthcare that supports the interaction of simulation modellers with a group of stakeholders to arrive at a common conceptual model. The framework incorporates two facilitated workshops. It consists of a package including: three key stages and sub-stages; activities and guidance; tools and prescribed outputs. The CM framework is tested in a real case study of an obesity system. The benefits of using this framework in healthcare studies and more widely in simulation are discussed. The paper also considers how the framework meets the CM requirements.     

Looking in the wrong place for healthcare improvements: A system dynamics study of an accident and emergency department

Accident and Emergency (A&E) units provide a route for patients requiring urgent admission to acute hospitals. Public concern over long waiting times for admissions motivated this study, whose aim is to explore the factors which contribute to such delays. The paper discusses the formulation and calibration of a system dynamics model of the interaction of demand pattern, A&E resource deployment, other hospital processes and bed numbers; and the outputs of policy analysis runs of the model which vary a number of the key parameters. Two significant findings have policy implications. One is that while some delays to patients are unavoidable, reductions can be achieved by selective augmentation of resources within, and relating to, the A&E unit. The second is that reductions in bed numbers do not increase waiting times for emergency admissions, their effect instead being to increase sharply the number of cancellations of admissions for elective surgery. This suggests that basing A&E policy solely on any single criterion will merely succeed in transferring the effects of a resource deficit to a different patient group.     

Widening requirements capture with soft methods: an investigation of hybrid M&S studies in health care

A simulation study consists of several stages: problem formulation, model implementation, verification and validation, experimentation and output data analysis. The application of multiple techniques in the model implementation stage is referred to as hybrid simulation, which we distinguish in this paper from a hybrid M&S study, the latter referring to studies that apply methods and techniques from disciplines like Operations Research (OR), Systems Engineering and Computer Science to one or more stages of a simulation study. Our paper focuses on the contribution of soft OR methods in the problem formulation stage of a simulation study (and by extension a hybrid M&S study). Soft Systems Methodology (SSM) has, arguably, been the most widely used qualitative approach for eliciting system requirements. In this paper, we present Qualitative System Dynamics (QSD), a soft systems method, as having potential use in the problem formulation stage of a healthcare M&S study. The contribution of this paper is thus twofold: (1) a review of the literature in SSM for healthcare operations management and (2) an examination of QSD as an additional soft OR method, complementing (rather than supplanting) existing approaches, which can further aid the understanding of the system in the problem formulation/conceptual modelling stage of a hybrid M&S study.     

A generic model for simulating office process flows

In the field of simulation, the use of application-specific software can bring many benefits to the model-builder in terms of improved productivity and reduced skill requirements. Such software must have at its core a robust generic model for the application area which offers both the generality and sufficient detail to be able to simulate realistically. To develop such a core model, a thorough understanding of the problem area and simulation is needed. This paper describes one design for a generic core model for the simulation of office process flows where work volumes are substantial. It forms a part of a software package called SCOPE which provides a user-interface for data capture, and generates simulation models to be run using the HOCUS simulation software. Also described is the facility for static modelling which has been developed to make further use of the captured data using conventional mathematical modelling.     

Model development in discrete-event simulation and system dynamics: An empirical study of expert modellers

An empirical study comparing the model development process followed by experts in discrete-event simulation (DES) and system dynamics (SD) modelling is undertaken. verbal protocol analysis (VPA) is used to study the modelling process followed by ten expert modellers (5 SD and 5 DES). Participants are asked to build simulation models based on a case study and to think aloud while modelling. The generated verbal protocols are divided into seven modelling topics: problem structuring, conceptual modelling, data inputs, model coding, verification & validation, results & experimentation and implementation and then analyzed. Our results suggest that all modellers switch between modelling topics, however DES modellers follow a more linear progression. DES modellers focus significantly more on model coding and verification & validation, whereas SD modellers on conceptual modelling. Observations are made revealing some interesting differences in the way the two groups of modellers tackle the case. This paper contributes towards the comparison of DES and SD.     

Normative agent-based simulation for supply chain planning

This paper presents an agent-based simulation framework for supply chain (SC) planning, introducing the notion of normative agent. The analysis of the relevant literature shows that most research works carried out in this area aim to handle specific problems and contexts. Although some methodologies and more generic solutions have been proposed, they are not able to cope with SCs in which regulation plays an important role, whether issued by a government agent or by an international institution. Several SCs, such as in the energy, food, chemical, and forestry areas, are highly regulated. Explicitly modelling the actors involved in the regulation of SCs using normative agents allowed us to evaluate the potential benefits of alternative strategies for planning of regulated SCs. The modelling of a biodiesel SC is presented as a case study.     

Automated generation of LFT-based parametric uncertainty descriptions from generic aircraft models

A computer assisted modelling methodology is developed for the generation of linearized models with parametric uncertainties described by Linear Fractional Transformations (LFTs). The starting point of the uncertainty modelling is a class of generic nonlinear aircraft models with explicit parametric dependence used for simulation purposes. The proposed methodology integrates specialized software tools for object-oriented modelling, for simulation, and for numerical as well as symbolic computations. The methodology has many generic features being applicable to similar nonlinear model classes.     

Simulating economic factors in adjuvant breast cancer treatment

When conducting an experimental study in healthcare systems, two problems are faced, those of uncertainty and complexity. Uncertainty is related to identifying variables for data collection (particularly if there are time and cost constraints on the modelling exercise). Complexity is related to the existence of many interacting variables (including treatment paths for patients, patient illnesses, side effects of treatments, etc.), each of a stochastic nature. This paper reports the usefulness of discrete event simulation modelling in exploring these issues. It focuses on the use of this form of simulation in supporting decision making in a randomised clinical trial (RCT). The objective of using simulation modelling is to help health economists identify the key factors active in the RCT through the development of a model of the healthcare related processes being studied by the RCT. This approach provides an opportunity to allow users to understand the role of these factors in the RCT. This research is carried out in the context of the Adjuvant Breast Cancer RCT.     

Incorporating human behaviour in simulation models of screening for breast cancer

Simulation modelling is widely used in many industries in order to assess and evaluate alternative options and to test strategies or operating rules which are too complex to be modelled analytically. Simulation software has developed its capability in parallel with the growth in computing power since the 1980s. However in practice, the results from the most sophisticated and complex simulation model may not truly reflect what happens in the real world, because such models do not account for human behaviour. For example, in the domain of healthcare simulation is often used to evaluate the outcomes from medical interventions such as new drug treatments. However in reality patients may not complete the course of a prescribed medication, perhaps because they find the side-effects unpleasant. A simulation study designed to evaluate this medication which ignores such behavioural factors may give unreliable results. In this paper we describe a model for screening for breast cancer which includes behavioural factors to model women’s decisions to attend for mammography. The model results indicate that increasing attendance through education or publicity campaigns can be equally as effective as decreasing the intervals between screens. This would have considerable cost implications for healthcare providers.     

Competitive dynamics in pharmaceutical markets: A case study in the chronic cardiac disease market

A modelling project involved system dynamics simulation of chronic cardiac disease in Bulgaria, examining the dynamic behaviour of a cardiac drug molecule in the market. The system dynamics model was calibrated using market data sourced from the Bulgarian National Health Care Fund, the Bulgarian Generic Pharmaceutical Association and a market research firm. The main results of the study showed that the timing of access to market was a critical driver in reducing prices and providing wider, as well as more affordable, access for patients to medicinal therapy. Our findings indicate that healthcare authorities may obtain savings while, at the same time, they may provide conditions for more patients to be treated depending on the timing of access to market of new generic drugs.     

Overcoming the barriers: a qualitative study of simulation adoption in the NHS

This paper addresses a key issue in the health OR literature, namely the apparent failure of OR modelling to become embedded and widely implemented within healthcare organisations. The research presented here is a case study to evaluate the adoption of one particular simulation modelling tool, Scenario Generator (S:G), which was developed by the SIMUL8 Corporation in a PPI partnership with the UK's National Health Service (NHS) Institute for Innovation and Improvement. The study involved semi-structured interviews with employees of 28 Primary Care Trusts who had all been engaged in some way with the initiative, with participants classified as ‘Not Started’, ‘Given Up’ and ‘Actively Using’. This paper presents a brief summary of barriers and facilitators to the successful use of the S:G software, but its main purpose is to focus more broadly on factors influencing the successful adoption of simulation tools in general within healthcare organisations. The insights gained in this study are relevant to improving the uptake of OR modelling in general within the NHS.     

Exploring the model development process in discrete-event simulation: insights from six expert modellers

This paper explores the model development process in discrete-event simulation (DES) by reporting on an empirical study that follows six expert modellers while building simulation models. DES is a widely used modelling approach, however little is known about the modelling processes and methodology adopted by modellers in practice. Verbal Protocol Analysis is used to collect data, where the participants are asked to speak aloud while modelling. The results show that the expert modellers spend a significant amount of time on model coding, verification and validation, and data inputs. The modellers iterate often between modelling activities. Patterns of modelling behaviour are identified, suggesting that the modellers adopt distinct modelling styles. This study is useful in that it provides an empirical view of existing DES modelling practice, which in turn can inform existing research and simulation practice as well as teaching of DES modelling to novices.     

Due dates in a manufacturing shop: An unweighted case

A dispatching rule is proposed for job shop operations where the performance criteria are due date related. The dispatching rule is constructed by combining the characteristics of the shortest process time rule and a dynamically determined earliest due date rule. The performance of the proposed rule is compared to currently well known rules across various shop environments using discrete simulation.This work was partially supported by: The National Science Foundation, Grant No. CDR-8300965; member companies of the Material Handling Research Center, the Georgia Institute of Technology and Texas A&M University.     

A generic environment for modelling future launch operations—GEM-FLO: a success story in generic modelling

Several NASA programs have been established to study and improve the current launch capability to meet the need for more aggressive space exploration in the future. Numerous launch systems have been proposed by different government and commercial organizations with the potential goal of replacing the Space Shuttle. NASA must evaluate new designs and technologies with the objective of improving upon today's Shuttle cost, performance, and turnaround time, before the government or commercial organizations pursue the large undertaking of a new launch system. To address this issue, the Generic Simulation Environment for Modelling Future Launch Operations (GEM-FLO) was developed to accurately predict processing turnaround times and other effectiveness criteria and support making key business and program decisions. GEM-FLO utilizes a generic modelling paradigm to provide a single platform for modelling different designs, which helped significantly cut the cost of these studies. This paper documents a success story in generic simulation modelling.     

Can we have confidence in generic structures?

The system dynamics concept of ‘generic structure’ is dividable into three sub-types. This paper analyses the validity of these three, using both practical and theoretical perspectives. Firstly, a new set of measures is developed for generating validity—‘confidence’—amongst a group using generic structures in a practical modelling situation. It is concluded that different confidence criteria are implicitly employed; there is an argument for trading-off model precision and analytical quality for simplicity and ease of use and future research is needed to combine these ‘process’ and ‘content’ aspects of confidence. From a theoretical stance it is shown that with two of the sub-types a scientific notion of confidence is achievable whereas the third (‘archetypes’) involves merely metaphorical thinking. It is concluded that the theoretical status of archetypes requires further development, whilst ensuring that its benefits are retained.     

Object-oriented dynamic supply-chain modelling incorporated with production scheduling

In this paper we present a new modelling approach for realistic simulation of supply-chains. It is based on an object-oriented architecture, which enables flexible specification of the supply-chain configuration along with its operational decisions and policies.A model of a generic supply-chain node is developed to capture the features present in all supply-chain entities. The generic node models in detail activities such as inventory control, manufacturing processes and order handling. The supply-chain model is constructed by linking generic nodes and specifying the physical and business attributes of each supply-chain member. The generic-node model may also be linked to external software for greater accuracy, e.g., detailed production scheduling or optimisation.The model provides a fully dynamic simulation of the supply-chain and the effect of various uncertainties can be evaluated through Monte Carlo simulation and other, more efficient, sampling techniques (not described here).A case study is presented to illustrate the applicability of the model. The case study demonstrates how the effect of policy changes on the supply-chain performance under uncertainty can be evaluated before implementation.     

A flexible and generic approach to dynamic modelling of supply chains

In this paper, we present a new modelling approach for realistic supply chain simulation. The model provides an experimental environment for informed comparison between different supply chain policies. A basic simulation model for a generic node, from which a supply chain network can be built, has been developed using an object-oriented approach. This generic model allows the incorporation of the information and physical systems and decision-making policies used by each node. The object-oriented approach gives the flexibility in specifying the supply chain configuration and operation decisions, and policies. Stochastic simulations are achieved by applying Latin Supercube Sampling to the uncertain variables in descending order of importance, which reduces the number of simulations required. We also present a case study to show that the model is applicable to a real-life situation for dynamic stochastic studies.     

A three-step local smoothing approach for estimating the mean and covariance functions of spatio-temporal Data

Spatio-temporal data are common in practice. Existing methods for analyzing such data often employ parametric modelling with different sets of model assumptions. However, spatio-temporal data in practice often have complicated structures, including complex spatial and temporal data variation, latent spatio-temporal data correlation, and unknown data distribution. Because such data structures reflect the complicated impact of confounding variables, such as weather, demographic variables, life styles, and other cultural and environmental factors, they are usually too complicated to describe by parametric models. In this paper, we suggest a general modelling framework for estimating the mean and covariance functions of spatio-temporal data using a three-step local smoothing procedure. The suggested method can well accommodate the complicated structure of real spatio-temporal data. Under some regularity conditions, the consistency of the proposed estimators is established. Both simulation studies and a real-data application show that our proposed method could work well in practice.