Tuong Manh Vu

Nowadays, manufacturing systems are shifting rapidly with the significant change in technology, business, and industry to become more complex and involved in more difficult issues, customised products, variant services and products, unavailable machines, and rush jobs. In the current practices, there are limited models or approaches that are dealing with these complexities. Most of the scheduling models in literature are proposed as centralised approaches. Researchers recently started to pay attention to reduce energy consumption in manufacturing due to the rising cost and the environmental impact. The energy consumption factor has been lately introduced into scheduling research among other traditional objectives such as time, cost and quality. Although reducing energy in manufacturing systems is very important, few researchers have considered energy consumption factor into scheduling in dynamic flexible manufacturing systems. This paper proposes an agent-based dynamic bio-objective robustness for energy and time in a job shop. Two types of agent are introduced which are machine agent and product agent. A new decision making and negotiation model for multi-agent systems is developed. Two types of dynamic unexpected events in the shop floor are introduced: dynamic job arrival and machines breakdown. A case study is provided in order to verify the result.

Previous research on social dilemmas demonstrated that various forms of punishment for free-riding can increase contribution levels in public goods games. The way individual group members react to the possibility of punishment can be also affected by individual differences in punishment sensitivity. Therefore, depending individual differences in punishment sensitivity of group members, different levels of punishment can be more or less effective to prevent free riding behaviour. This paper uses agent-based modelling to model the effect of punishment sensitivity on contribution levels in a public goods game. The paper then examines the correlation between punishment sensitivity and variability of free riding behaviour under different punishment conditions.

Agent-based Simulation is used for different purposes in Computer Science and Economics. While computer scientists design agents for controlling complex systems or for instilling objects with autonomy and intelligence, economists use agents for gaining a better understanding of the dynamics within economic systems. For the modelling task both communities use their specialised modelling approaches which are very different from each other. We believe that the tools used for the purpose of designing software agents have great potential to be useful for designing agent-based models to study Economics problems. In this paper, we provide some general guidance for building Agent-based Computational Economics (ACE) models using Software Engineering (SE) modelling methods. The applicability of these methods is demonstrated by studying a real-life multi-player dilemma using an ACE and SE approach.

Agent-Based Simulation (ABS) environments are somewhat of a black box to many modelers and their inner workings are often only understood by the people who developed them. We intend to shed some light into the inner workings of such systems. For the purposes of understanding such systems more in detail, we have developed our own simple ABS environment in C++ using hierarchical state machines. In this paper, we describe the inner workings of our ABS environment in detail, then test the performance of our ABS environment by comparing it to that of an "off the shelf" commercial package. While some programming knowledge is required to understand the paper in all its depth, we believe that non programming experts will also benefit from this paper as it provides insight into the underlying mechanisms operating within an ABS using graphical representations and explanations that avoid heavy technical jargon.

The Belief-Desire-Intention (BDI) software model is an example of a reasoning architecture for a bounded rational software agent. In our research we plan to expand the application of the BDI software model to the area of simulating human behaviour in social and socio-technical systems. To this effect, in this paper we explore the differences in using a classical crisp rule-based approach and a fuzzy rule-based approach for the reasoning within the BDI system. As a test case we have chosen a football penalty shootout. We have kept the case study example deliberately simple so that we can focus on the effects the different BDI implementations have on the decisions made. Our experiments highlight that the crisp system can result in unwanted "preferred" actions because of sudden leaps or drops between different ranges of decision variables, while the fuzzy system results have smoother transitions which results in more consistent decisions. The behaviour, as showcased in this simple context, underlines that a change from crisp to fuzzy rule based systems as the underlying reasoning model in BDI systems can provide the path to a superior approach for the simulation of human behaviour, which we will explore further in the future.

In Agent-based Modelling and Simulation (ABMS), a system is modelled as a collection of agents, which are autonomous decision-making units with diverse characteristics. The interaction of the individual behaviours of the agents results in the global behaviour of the system. Because ABMS offers a methodology to create an artificial society in which actors with their behaviour can be designed and results of their interaction can be observed, it has gained attention in social sciences such as Economics, Ecology, Social Psychology, and Sociology.

In Economics, ABMS has been used to model many strategic situations. One of the popular strategic situations is the Public Goods Game (PGG). In the PGG, participants secretly choose how many of their private money units to put into a public pot. Social scientists can conduct laboratory experiments of PGGs to study human behaviours in strategic situations. Research findings from these laboratory studies have inspired studies using computational agents and vice versa. However, there is a lack of guidelines regarding the detailed development process and the modelling of agent behaviour for agent-based models of PGGs. We believe that this has contributed to ABMS of PGG not having been used to its full potential.

This thesis aims to leverage the potential of ABMS of PGG, focusing on the development methodology of ABMS and the modelling of agent behaviour. We construct a development framework with incorporated software engineering techniques, then tailored it to ABMS of PGG. The framework uses the Unified Modelling Language (UML) as a standard specification language, and includes a simulation development lifecycle, a step-by-step development guideline, and a short guide for modelling agent behaviour with statecharts. It utilizes software engineering methods to provide a structured approach to identify agent interactions, and design simulation architecture and agent behaviour. The framework is named ABOOMS (Agent-Based Object-Oriented Modelling and Simulation).

After applying the ABOOMS framework to three case studies, the framework demonstrates flexibility in development with two different modelling principles (Keep-It-Simple-Stupid vs. Keep-It-Descriptive-Stupid), capability in supporting complex psychological mechanisms, and ability to model dynamic behaviours in both discrete and continuous time. Additionally, the thesis developed an agent-based model of a PGG in a continuous-time setting. To the best of our knowledge such agent-based models do not exist. During the development, a new social preference, Generous Conditional Cooperators, was introduced to better explain the behavioural dynamics in continuous-time PGG. Experimentation with the agent-based model generated dynamics that are not presented in discrete-time setting. Thus, it is important to study both discrete and continuous time PGG, with laboratory experiment and ABMS. Our new framework allows to do the latter in a structured way.

With the ABOOMS framework, economists can develop PGG simulation models in a structured way and communicate them with a formal model specification. The thesis also showed that there is a need for further investigation on behaviours in continuous-time PGG. For future works, the framework can be tested with variations of PGG or other related strategic interactions.