Which is more appropriate: a multi-perspective comparison between systems dynamics and discrete event simulation

System Dynamics (SD) and Discrete Event Simulation (DES) are two established simulation tech-niques for simulating the dynamics of a system. Both have been widely used in modelling business de-cisions. This paper presents meta-comparison between the two approaches based on literature survey. Upon reviewing the existing literature it has been identified that existing comparisons could be classi-fied under three main perspectives: Systems perspective, Problems perspective and Methodology per-spective. The nature of system and nature of problem have been argued as primary factors for decid-ing modelling methodology. Therefore SD and DES comparisons have been classified on the basis of systems, problems and inherent aspects and capabilities of both modelling methods. It has been ar-gued that development of sound models need fit between system, problem and methodology. The suc-cess of model depends on it’s technical soundness as well as it’s successful implementation. In order to develop successful models this vision has been further extended to incorporate stakeholders, re-sources and time

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

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

Technique for improving care integration models

Recent developments in technologies and improved life style have had a positive impact on prolonging human life contributing to the increasing elderly population. As a consequence, many countries (particularly developed ones) started to experience higher proportions of elderly people (over 65). This has consequently generated the need for care for the elderly that is necessitating the integration of health and social care to accommodate their complex needs. A number of modelling methods have been employed to assist those concerned to cope with health and social care but albeit separately. The literatures so far, identified several techniques that have been employed mostly to model the care integration. However, literatures also suggest that there are some challenges still persist when modelling integrated care. It can be argued that these techniques are not capable of handling the complexities associated with the requirements of integrated systems. This paper attempts to prove the reason why despite the fact that many models of integrated care have been developed, problems are still exist. Based on the literatures, the problems exist due to the unsuitable techniques used to model the IC systems as most of the developed models are using single technique. Therefore, new technique to improve the care integration model is suggested

Bond-Order Time Series Analysis for Detecting Reaction Events in Ab Initio Molecular Dynamics Simulations.

Ab initio molecular dynamics is able to predict novel reaction mechanisms by directly observing the individual reaction events that occur in simulation trajectories. In this article, we describe an approach for detecting reaction events from simulation trajectories using a physically motivated model based on time series analysis of ab initio bond orders. We found that applying a threshold to the bond order was insufficient for accurate detection, whereas peak finding on the first time derivative resulted in significantly improved accuracy. The model is trained on a reference set of reaction events representing the ideal result given unlimited computing resources. Our study includes two model systems: a heptanylium carbocation that undergoes hydride shifts and an unsaturated iron carbonyl cluster that features CO ligand migration and bridging behavior. The results indicate a high level of promise for this analysis approach to be used in mechanistic analysis of reactive AIMD simulations more generally

Patch-based Hybrid Modelling of Spatially Distributed Systems by Using Stochastic HYPE - ZebraNet as an Example

Individual-based hybrid modelling of spatially distributed systems is usually expensive. Here, we consider a hybrid system in which mobile agents spread over the space and interact with each other when in close proximity. An individual-based model for this system needs to capture the spatial attributes of every agent and monitor the interaction between each pair of them. As a result, the cost of simulating this model grows exponentially as the number of agents increases. For this reason, a patch-based model with more abstraction but better scalability is advantageous. In a patch-based model, instead of representing each agent separately, we model the agents in a patch as an aggregation. This property significantly enhances the scalability of the model. In this paper, we convert an individual-based model for a spatially distributed network system for wild-life monitoring, ZebraNet, to a patch-based stochastic HYPE model with accurate performance evaluation. We show the ease and expressiveness of stochastic HYPE for patch-based modelling of hybrid systems. Moreover, a mean-field analytical model is proposed as the fluid flow approximation of the stochastic HYPE model, which can be used to investigate the average behaviour of the modelled system over an infinite number of simulation runs of the stochastic HYPE model.Comment: In Proceedings QAPL 2014, arXiv:1406.156

Discrete and continuous time simulations of spatial ecological processes predict different final population sizes and interspecific competition outcomes

Cellular automata (CAs) are commonly used to simulate spatial processes in ecology. Although appropriate for modelling events that occur at discrete time points, they are also routinely used to model biological processes that take place continuously. We report on a study comparing predictions of discrete time CA models to those of their continuous time counterpart. Specifically, we investigate how the decision to model time discretely or continuously affects predictions regarding long-run population sizes, the probability of extinction and interspecific competition. We show effects on predicted ecological outcomes, finding quantitative differences in all cases and in the case of interspecific competition, additional qualitative differences in predictions regarding species dominance. Our findings demonstrate that qualitative conclusions drawn from spatial simulations can be critically dependent on the decision to model time discretely or continuously. Contrary to our expectations, simulating in continuous time did not incur a heavy computational penalty. We also raise ecological questions on the relative benefits of reproductive strategies that take place in discrete and continuous time

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches