On the design of hybrid simulation models, focussing on the agent-based system dynamics combination - Swinerd, Chris (Cranfield University, 2014)
There is a growing body of literature reporting the application of hybrid simulations to inform decision making. However, guidance for the design of such models, where the output depends upon more than one modelling paradigm, is limited. The benefits of realising this guidance include facilitating efficiencies in the general modelling process and reduction in project risk (both across measures of time, cost and quality). Focussing on the least well researched modelling combination of agent-based simulation with system dynamics, a combination potentially suited to modelling complex adaptive systems, the research contribution presented here looks to address this shortfall. Within a modelling process, conceptual modelling is linked to model specification via the design transition. Using standards for systems engineering to formally define this transition, a critical review of the published literature reveals that it is frequently documented. However, coverage is inconsistent and consequently it is difficult to draw general conclusions and establish best practice. Therefore, methods for extracting this information, whilst covering a diverse range of application domains, are investigated. A general framework is proposed to consistently represent the content of conceptual models; characterising the key elements of the content and interfaces between them. Integrating this content in an architectural design, design classes are then defined. Building on this analysis, a decision process is introduced that can be used to determine the utility of these design classes. This research is benchmarked against reported design studies considering system dynamics and discrete-event simulation and demonstrated in a case study where each design archetype is implemented. Finally, the potential for future research to extend this guidance to other modelling combinations is discussed.
©Cranfield University, 2014
Keywords: Modelling; Computer simulation; Decision making