EPSRC EP/G004234/1

The efficiency of current cargo screening processes is unknown as no benchmarks exists against which they could be measured (% detected vs. % missed). Some manufacturer benchmarks exist for individual sensors which have been tested under laboratory conditions but we have not found any benchmarks that take a holistic view of the screening procedures assessing a combination of sensors and also taking operator variability into account.

Just adding up resources and manpower used is not an effective way for assessing systems where human decision-making (e.g. interpreting sensor signals) and operator compliance to rules (e.g. when operating with equipment) play a vital role. For such systems more advanced assessment methods need to be used, taking into account that the cargo screening process is of a dynamic and stochastic nature.

We are proposing to design a plug and play software tool (cargo screening system simulator) that will map the right technology and manpower to the right commodity / threat combination. System simulation can help to understand the processes currently in place and show the consequences of changes to these processes over time. The tool will enable the optimisation of resources (equipment, manning) and processes (job organisation, interoperability of equipment, teamwork, communication, data flow) and allow to evaluate the integration of new sensor technology and how they will improve detection efficiency.

EPSRC EP/G05956X/1

The research addresses a key challenge for energy sustainability - how can individual UK cities play their vital role in the implementation of ambitious future UK energy sustainability policies between now and 2020, whilst mitigating conflicts with the local imperatives that until now have dominated local government decision making? There are many different actors/key stakeholders (and legislation) involved in decision making processes that interact/influence each other in a non linear way. It is common practice to model only a subset of the actors involved in the decision making process. However, to get a realistic representation of the real system and its decision making processes we need to model the decision makers (politicians and senior local government personnel) on the one hand and the people influencing the decision making process (energy companies and their executives, activity groups and end users) on the other hand.

We will deploy agent-based modelling and simulation to deliver models that enable cities to define their current energy situation and then reach balanced decisions in their future energy planning, implementing UK sustainability targets. The project work will also involve efforts to relate and, where possible, integrate these agent-based models with dynamical network models of the evolving energy supply-demand network developed be our collaborators at Leeds University.

EPSRC EP/D503949/1

Our previous project is part of an IDEAS Factory Network Project which investigates the role of management practices in closing the productivity gap. Our task in Nottingham is to develop a tool that helps to understand and predict the impact of different management practices on retail store productivity. After an intensive review of the literature we have decided to use agent-based modelling and simulation to support our investigations. In agent-based simulation models a complex system is represented by a collection of agents that are designed to mimic the behaviour of their real world counterparts and are programmed to follow some often quite simple behaviour rules. Macro behaviour is not modelled but it emerges from the micro decisions of the individual agents (Pourdehnad et al., 2002). Through interaction of agents with each other and their environment the simulation produces complex collective behaviour patterns.

There has been a fair amount of modelling and simulation of operational management practices, but people management practices have often been neglected although research suggests that they crucially impact upon an organization's performance (Birdi et al., 2008). One reason for this neglect relates to the key component of people management practices, an organisation's people, who may often be unpredictable in their individual behaviour.

In our project we focus on simulating various in-store scenarios grounded in empirical case studies with a leading UK retailer. We investigate if agent-based modelling and simulation can help us with assessing the impact of people management practices on customer satisfaction, under consideration of external stimulation attributable to the word of mouth and internal stimulation triggered by memory of one's own previous shopping experiences in relation to the performance of a service-oriented retail department.

To achieve this aim we have adopted a case study approach using applied research methods to collect both qualitative and quantitative data. This has enabled us to acquire a valid and reliable understanding of how the real system operates, revealing insights into the working of the system as well as the behaviour of and interactions between the different individuals and their complementary roles within the retail department. Using this knowledge and data, we have applied agent-based modelling to devise a functional representation of the case study departments. By executing the simulation models we can run experiments to investigate the effects of different management scenarios.

We have presented our work at all major annual simulation conferences and have found that interest in the research area is steadily growing. Developing capabilities in modelling human behaviour and performance becomes more and more relevant for business and organisations but also in areas like prevention of terrorism, homeland security, and emergency responses.

References:

Birdi, K., Clegg, C.W., Patterson, M., Robinson, A., Stride, C.B., Wall, T.D., and Wood, S.J. (2008). "The Impact of Human Resource and Operational Management Practices on Company Productivity: A Longitudinal Study" Personnel Psychology, accepted for publication.

Pourdehnad, J., Maani, K., and Sedehi, H. (2002). "System Dynamics and Intelligent Agent-Based Simulation: Where is the Synergy?" Proceedings of the 20th International Conference of the System Dynamics Society, 28 July - 1 August 2002, Palermo, Italy.

There are some topics that have been bugging me for a while now. I have started to discuss these at SimSoc (News and discussion about Computer Simulation in the Social Sciences). These topics are:

I have decided to publish all the models we have used for the experiments in our publications. The models require the simulation software AnyLogic. An evaluation copy can be obtained from XJ Technologies. Make sure you get AnyLogic v5.5 as the models will not work with any later version!

The models have not been developed with the intention of being released to the public, so don't expect an extensive documentation. In fact, the documentation is very rudimentary, with just a few remarks in the java source code. The concepts for the model components are described in our publications. If I have some spare time I will improve the documentation and also do some bug fixes in the older versions, so keep an eye on this section for updates.

If you use our concepts or our models or parts of our code please remember to cite us appropriately. If you have any specific questions do not hesitate to contact me. I will try to help but I cannot promise anything!