On the design of hybrid simulation models, focussing on the agent-based system dynamics combination

©Cranfield University, 2014There 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

Global vulnerability to near-Earth object impact

A clear appreciation of the consequences resulting from an asteroid impact is required in order to understand the near Earth object (NEO) hazard. Three main processes require modelling to analyse the entire impact event. These are the atmospheric entry phase, land impact events and ocean impact events. A range of impact generated effects (IGEs) are produced by different impact scenarios. It is these IGEs that present the threat to human populations world wide, and the infrastructure they utilise. A software system for analysing the NEO threat has been developed, entitled NEOimpactor, to examine the social and economic consequences from land and ocean impacts. Existing mathematical models for the three principal impact processes have been integrated into one complete system, which has the capability to model the various effects of a terrestrial asteroid impact and, critically, predict the consequences for the global population and infrastructure. Analysis of multiple impact simulations provides a robust method for the provision of an integrated, global vulnerability assessment of the NEO hazard. The primary graphical outputs from NEOimpactor are in the form of ‘relative consequence’ maps, and these have been designed to be comprehensible to a non-specialist audience. By the use of a series of multiple-impact simulations, the system has identified the five countries most at risk from the impact hazard, as well as indicating the various factors influencing vulnerability

Green neighbourhoods: the role of big data in low voltage networks’ planning

In this chapter, we aim to illustrate the benefits of data collection and analysis to the maintenance and planning of current and future low voltage net- works. To start with, we present several recently developed methods based on graph theory and agent-based modelling for analysis and short- and long-term prediction of individual households electric energy demand. We show how maximum weighted perfect matching in bipartite graphs can be used for short-term forecasts, and then review recent research developments of this method that allow applications on very large datasets. Based on known individual profiles, we then review agent-based modelling techniques for uptake of low carbon technologies taking into account socio-demographic characteristics of local neighbourhoods. While these techniques are relatively easily scalable, measuring the uncertainty of their results is more challenging. We present confidence bounds that allow us to measure uncertainty of the uptake based on different scenarios. Finally, two case-studies are reported, describing applications of these techniques to energy modelling on a real low-voltage net- work in Bracknell, UK. These studies show how applying agent-based modelling to large collected datasets can create added value through more efficient energy usage. Big data analytics of supply and demand can contribute to a better use of renewable sources resulting in more reliable, cheaper energy and cut our carbon emissions at the same time

Science based modelling for supporting integrated coastal zone management

Approach: A systematic review of the integrated coastal zone management (ICZM) approach and all related tools for science-based modeling is undertaken