Feedback methods for inverse simulation of dynamic models for engineering systems applications

Inverse simulation is a form of inverse modelling in which computer simulation methods are used to find the time histories of input variables that, for a given model, match a set of required output responses. Conventional inverse simulation methods for dynamic models are computationally intensive and can present difficulties for high-speed applications. This article includes a review of established methods of inverse simulation, giving some emphasis on iterative techniques that were first developed for aeronautical applications. It goes on to discuss the application of a different approach that is based on feedback principles. This feedback method is suitable for a wide range of linear and non-linear dynamic models and involves two distinct stages. The first stage involves design of a feedback loop around the given simulation model, and in the second stage, that closed-loop system is used for inversion of the model. Issues of robustness within closed-loop systems used in inverse simulation are not significant as there are no plant uncertainties or external disturbances. Thus, the process is simpler than that required for the development of a control system of equivalent complexity. Engineering applications of this feedback approach to inverse simulation are described through case studies that put particular emphasis on non-linear and multi-input multi-output models.     

Simulation modelling is 50! Do we need a reality check?

Simulation modelling is a fascinating research field. The techniques and tools of simulation modelling have been used to research and investigate the behaviour of various systems in a wide range of areas such as commerce, computer networks, defence, health, manufacturing and transportation. Indeed, the study of the use of these techniques and tools, and the development of new forms of these, are a rich source of research in their own right. Simulation modelling is about to reach the 50th anniversary of the development of GSP (General Simulation Program), the first simulation modelling language (Tocher and Owen, 1960). There have been several historical accounts of simulation modelling research. To complement these, we have performed a review of the recent history of simulation modelling. This study targeted three leading journals dedicated to this field. These are the ACM Transactions of Modeling and Computer Simulation, Simulation: Transactions of The Society for Modeling and Simulation International and Simulation Modelling Practice and Theory (formerly Simulation Practice and Theory). The study covered the first 6 years of this century (2000–2005) and included 576 papers. The key observation of this work was the relative lack of ‘real world’ involvement in simulation modelling research and an even greater lack of evidence of ‘real world’ benefit, arguably very alarming outcomes for an applied field. To further investigate this observation two additional surveys were carried out, one to study if real world papers appeared in the more widely known OR/MS literature (837 papers in 12 journals) and one to study if such papers appeared in Manufacturing and Logistics, an application area closely associated with simulation modelling (1077 papers in 10 journals). The results of these surveys confirmed our observations. We ask if this is the natural evolution of a field that has existed for half a century or an indication of a worrying problem? This paper reports on our findings and discusses whether or not simulation modelling research urgently needs to face a ‘reality check.’     

Dynamic iteration may fail for partitioned network simulation

In many technical applications, complex systems are composed of subsystems that are modelled independently using a network approach. This co‐modelling ansatz is completed very naturally by a co‐simulation approach based on dynamic iteration, where the respective DAE submodels are solved iteratively. For partitioned electric network simulation we show that this approach may fail. As alternative we discuss overlapping techniques based on topological network information.     

Exploring alternative routes to realising the benefits of simulation in healthcare

Discrete event simulation should offer numerous benefits in designing healthcare systems but the reality is often problematic. Healthcare modelling faces particular challenges: genuine, fundamental variations in practice and an opposition to any suggestion of standardisation from some professional groups. This paper compares the experiences of developing a new simulation in an Accident and Emergency (A&E) Department, a subsequent adaptation for modelling an outpatient clinic and applications of a generic A&E simulation. These studies provide examples of three distinct approaches to realising the potential benefits of simulation: the bespoke, the reuse and the generic route. Reuse has many advantages: it is relatively efficient in exploiting previous modelling experience, delivering timely results although providing scope for adaptations to local practice. Explicitly demonstrating this willingness to adapt to local conditions and engaging with stakeholders is particularly important in healthcare simulation.     

Bank-office Process Management in the Financial Services: a Simulation Approach Using a Model Generator

For the last ten years or so, developments in simulation software and methodology have been focused in two areas: (1) automating the model-building process; and (2) interfacing with other techniques and systems. That both academic and commercial research continues to be carried out in these areas is evidence of the continued popularity of the simulation technique. The most popular application areas where simulation modelling is used include manufacturing (engineering and process), warehousing and communications; not surprisingly, research and developments have therefore been concentrated in these areas. This paper describes the development of a simulation model generator for a different application area, that of clerical office processing of paper and documents for financial service companies. It outlines the reasons why there is a need for such a model generator and discusses the initial design requirements. The paper goes on to detail the developments and re-defined criteria required as the model generator brief changes as a result of practical applications.     

Studies Regarding the Alternate Simulation Techniques for Machine Toolsand Industrial Robots

In the paper are presented results regarding the design, simulation and testing of some robotized structures with parallel kinematics in order to identify appropriate simulation techniques and to reveal the properties of particular studied characteristics. The paper is structured in three parts: introduction regarding the tasks of the simulations techniques and the developed applications, comparison of the results obtained in simulations, considering the working space modelling accuracy and the speed of workspace identification and the last part conclusions. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Decision development in facilitated modelling workshops

While the facilitated modelling literature recognises the importance of the group process within facilitated modelling workshops, published empirical research rarely examines their dynamic nature. In this paper, we address this gap in the literature in two ways. First, we propose to locate facilitated modelling workshops as the main focus of investigation, and adopt decision development as the analytical lens. Second, we provide guidance regarding how to implement a research strategy that is informed by such a focus. We start by mapping the different conceptualisations of decision development that seem embedded within the facilitated modelling tradition, and contrast them with theoretical models from the group communication field. Our analysis identifies a number of potentially useful areas for the study of facilitated modelling workshops from a decision development perspective, and articulates a number tentative research questions and testable propositions amenable to empirical research. Central to our proposal are research methods for the study of dynamic group processes. We thus discuss the steps required to extract group process data from facilitated modelling workshops that are usable and open to analysis. This includes a review of issues regarding research design, coding scheme development, data coding and choice of analytical techniques. Finally, we offer conclusions and briefly discuss some feasibility issues related to the implementation of our proposal.     

Data envelopment analysis,operational research and uncertainty

This paper discusses a number of applications of data envelopment analysis and the nature of uncertainty in those applications. It then reviews the key approaches to handling uncertainty in data envelopment analysis (DEA) (imprecise DEA, bootstrapping, Monte Carlo simulation and chance constrained DEA) and considers their suitability for modelling the applications. The paper concludes with suggestions about the challenges facing an operational research analyst in applying DEA in real-world situations.     

Object-oriented modelling and simulation of a motorcycle

The design of a control oriented motorcycle model for the simulation of two-wheeled vehicles is widely recognized to be a very challenging task, as a complete analytical model is not directly available, due to its complexity and its high sensitivity to parameters' variations. Accordingly, a reliable model should be based on multibody modelling tools endowed with automated symbolic manipulation capabilities. This paper presents a simulation model for the dynamic behaviour of a motorcycle based on the object-oriented modelling paradigm developed in Modelica, within the Dymola environment. Specifically, we illustrate the modular approach to motorcycle modelling and discuss the tire-road interaction model, which is the crucial part of the proposed model. The validity of the proposed simulation model is assessed on real data, measured on an instrumented test vehicle. Further, to perform the verification phase a virtual driver model has been implemented, which allows to track both a roll angle and a target speed profile during different maneuvers. In particular, the behaviour of the driver is modelled as an inverse pendulum, with a rotational degree of freedom along the forward axis. This allows accounting for the driver lean angle, which is necessary to fully capture the real driving behaviour and its effects on the overall vehicle dynamics.     

Applications of simulation within the healthcare context

A large number of studies have applied simulation to a multitude of issues relating to healthcare. These studies have been published in a number of unrelated publishing outlets, which may hamper the widespread reference and use of such resources. In this paper, we analyse existing research in healthcare simulation in order to categorise and synthesise it in a meaningful manner. Hence, the aim of this paper is to conduct a review of the literature pertaining to simulation research within healthcare in order to ascertain its current development. A review of approximately 250 high-quality journal papers published between 1970 and 2007 on healthcare-related simulation research was conducted. The results present a classification of the healthcare publications according to the simulation techniques they employ; the impact of published literature in healthcare simulation; a report on demonstration and implementation of the studies’ results; the sources of funding; and the software used. Healthcare planners and researchers will benefit from this study by having ready access to an indicative article collection of simulation techniques applied to healthcare problems that are clustered under meaningful headings. This study facilitates the understanding of the potential of different simulation techniques in solving diverse healthcare problems.     

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.     

Atomistic modelling in metrology: algorithms and current challenges

Mathematical modelling plays an increasingly important role in measurement science where many problems of fundamental and practical importance require solutions at meso- and nano-scales. In quantum and materials metrology atomistic simulation has developed to a level where it is competing in accuracy with laboratory measurements. This article gives a brief overview of current applications of atomistic simulation to problems of metrology. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Modelling and simulation in business and industry: insights into the processes and practices of expert modellers

The simulation modelling process and individual practices of simulation modellers impact on the outcome of simulation studies. Despite this, the modelling and simulation literature lacks a rich body of evidence on the actual process and practices of modellers in the real world. This qualitative study aims to address this shortcoming by attempting to discover the underlying simulation modelling process of 20 expert modellers working within their typical contexts. Our study makes a valuable addition to simulation research by investigating how the expert modellers develop their simulation models and how their context may affect their simulation practices. The main finding is that most of the participants do not have a clearly defined or a formal process for developing their models. Instead, they follow a set of key steps and their individual practices depend on the context of the study. A number of contextual factors such as the term of model use, the size of the model and the complexity of the model, may affect the way in which a modeller goes about developing a simulation model. For instance, the extent to which a model is documented depends on the model life. This research contributes to enhancing our understanding of the simulation modelling process in varying contexts.     

On the approximation of empirical data for service system simulations

The difficulties associated with parameter estimation for phase-type approximations of empirical data distributions in queue modelling are well known. While significant progress has been achieved in improving such approximations, difficulties in parameter estimation still limit the extent to which queue modelling is applied in practice. This paper presents a simplified technique for approximating empirical data in service system simulations, based on the specialised Cox phase-type distribution. When utilised in simulation modelling of a service system, the specialised Cox distribution is shown to provide improved approximations to the combined waiting and service time distribution without the need for complex parameter estimation techniques. This approach should enable much greater flexibility in the application of queue modelling to service systems.     

Intelligent management systems in operations: a review

Operations management is an area that has recently started to benefit from the use of AI techniques such as expert systems, neural networks and genetic algorithms. These techniques can extend the usefulness of OR modelling and enable new types of decision tasks to be supported by computer-based systems. This paper attempts to review ‘intelligent’ decision support systems and their potential to address some of the problems faced in various areas of operations management. Some useful techniques developed in the field of artificial intelligence are outlined and examples of attempts to use these approaches to support decision making in various areas of operations management are described. Recognising the scale of a complete review of all these areas, emphasis has been given to the most significant and more recent publications.     

The case for the use of multiple imputation missing data methods in stochastic frontier analysis with illustration using English local highway data

Multiple imputation (MI) methods have been widely applied in economic applications as a robust statistical way to incorporate data where some observations have missing values for some variables. However in stochastic frontier analysis (SFA), application of these techniques has been sparse and the case for such models has not received attention in the appropriate academic literature. This paper fills this gap and explores the robust properties of MI within the stochastic frontier context. From a methodological perspective, we depart from the standard MI literature by demonstrating, conceptually and through simulation, that it is not appropriate to use imputations of the dependent variable within the SFA modelling, although they can be useful to predict the values of missing explanatory variables. Fundamentally, this is because efficiency analysis involves decomposing a residual into noise and inefficiency and as a result any imputation of a dependent variable would be imputing efficiency based on some concept of average inefficiency in the sample. A further contribution that we discuss and illustrate for the first time in the SFA literature, is that using auxiliary variables (outside of those contained in the SFA model) can enhance the imputations of missing values. Our empirical example neatly articulates that often the source of missing data is only a sub-set of components comprising a part of a composite (or complex) measure and that the other parts that are observed are very useful in predicting the value.     

Modelling nationwide hospital length of stay: opening the black box

Hospital length of stay is considered to be a reliable and valid proxy for measuring the consumption of hospital resources. Average length of stay, however, albeit easy to quantify and calculate, does not suitably reflect the nature of such underlying distributions and may therefore mask the effects that the different streams of patients have on the system. This paper uses routinely collected and readily available nationwide data on stroke-related patients, aged 65 years and over, who were discharged from English hospitals over a 1-year period. This will be the basis for a running example illustrating the alternative methods of analysis and models of patients' length of stay. The methods include statistical methods: survival analysis, mixed exponential and phase-type distributions; and decision modelling techniques: compartmental and simulation models. The paper concludes by summarizing these various modelling techniques and by highlighting the similarity of the estimated parameters of patient flow as calculated by the phase-type distribution and compartmental modelling techniques.     

Stochastic signal analysis: Numerical representation and applications to system theory

Alternative procedures for representing a stochastic signal as a denumerable series with uncorrelated terms are developed. The study includes considerations about parametric rescaling and its repercussions on the orthogonal expansion, as well as numerical techniques for approaching such an expansion. In addition, applications to the Gaussian characterization of stochastic signals, filtering and stochastic modelling are treated.     

Experiments,modelling and simulation of adhesive curing processes in bonded three dimensional curved piezo metal composite structures

This contribution deals with the modelling and simulation of curing phenomena in adhesively bonded piezo metal composites which consists of a piezoelectric module enclosed by an adhesive layer which in turn is surrounded by two metal sheets. A short survey on the neccessary experimental investigations to characterise the adhesive's material behaviour is given and important aspects on the corresponding phenomenological modelling approach are presented. Both steps take into account the curing reaction, changes of volume, like chemical shrinkage, and inelastic mechanical behaviour which is temperature and curing dependent. Finally, the simulation strategy for the modelling within a finite element environment is depicted. By this, residual stresses, secondary deformations and loads on the piezo modules can be predicted, which is exemplified by a comparative study verifying a novel manufacturing strategy. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)