Sistemas de transporte são sistemas complexos compostos de diferentes entidades que interagem entre si. A otimização do uso da infraestrutura de transporte existente, que é cada vez mais necessária dado o crescente aumento da demanda por mobilidade, passa pela simulação de novas tecnologias que podem vir a ser utilizadas no futuro, como a comunicação inter-veicular (IVC) e a tarifação viária adaptativa. Esta dissertação apresenta uma abordagem baseada em agentes para simulação de comunicação inter-veicular e tarifação viária adaptativa em sistemas de transporte. Motoristas são modelados como agentes minimizadores de custo, composto pelo tempo de viagem e pelas despesas com tarifas viárias. Os motoristas podem usar IVC para expandir seu conhecimento do estado da rede viária. Entre os motoristas que usam IVC, podem existir agentes maliciosos, que buscam afastar os demais de suas rotas, através da divulgação de informações falsas. Os agentes maliciosos podem ainda agir de maneira coordenada, de modo a divulgarem informações falsas sobre as rotas de todos os agentes do grupo. Pelo lado da infraestrutura, gerentes viários percebem o fluxo de veículos nas vias da rede viária e definem as tarifas a serem aplicadas através de um esquema de aprendizado por reforço. Nos experimentos realizados, empregamos um modelo microscópico de simulação de tráfego, o que permite observar o comportamento individual de cada entidade do sistema de transporte sob estudo. O cenário onde as simulações são executadas é uma rede viária com as principais vias arteriais da cidade de Porto Alegre, Brasil. Resultados experimentais indicam que um pequeno grupo coordenado de agentes maliciosos em cenários de IVC é capaz de causar prejuízos significativos aos demais motoristas. Embora na média o grupo não consiga reduzir seu tempo de viagem, alguns agentes maliciosos são beneficiados pela coordenação do grupo. Com relação à tarifação viária, os resultados experimentais indicam que o esquema de aprendizado por reforço não possui a mesma eficácia de um esquema de tarifação fixa quando se trata da maximização de fluxo de veículos na rede viária. Ambos os esquemas de tarifação são superados por um método de otimização de tráfego que assume conhecimento completo do estado da rede viária pelos motoristas. No aspecto individual, sob tarifação via aprendizado por reforço, os custos de deslocamento dos motoristas são superiores em comparação aos custos sob tarifação fixa. O modelo baseado em agentes apresentado nesta dissertação representa uma contribuição em direção à proposição de uma metodologia para integrar modelos comportamentais de usuários de sistemas de transporte que reagem aos padrões de tráfego e medidas de controle desses padrões, com foco em métodos descentralizados e distribuídos. / Transportation systems are complex systems composed of different interacting entities. The optimization of the existing transportation infrastructure usage, which becomes increasingly necessary given the increasing demand for mobility, requires simulation of new technologies that might be used in the future, such as inter-vehicular communication (IVC) and adaptive road pricing. This dissertation presents an agent-based approach for simulation of inter-vehicular communication and adaptive road pricing in transportation systems. Drivers are modeled as cost-minimizer agents, where the cost is composed by travel time and expenditure. Drivers can use IVC to expand their knowledge of the road network state. Among the IVC users, there might be malicious agents, which try to divert other drivers from their routes by spreading false information. The malicious agents can act in a coordinated way, by spreading false information about the routes of all the agents in the group. In the infrastructure side, link managers perceive the vehicular flow in the roads and define the prices to be applied by means of a reinforcement learning scheme. In the experiments, we employ a microscopic traffic simulation model, which allows us to observe the individual behavior of each entity in the studied transportation system. The scenario where the simulations are run is a road network with the main arterial roads of the city of Porto Alegre, Brazil. Experimental results indicate that a small group of coordinated malicious agents in IVC scenarios is able to cause significant losses to the other drivers. Although in average the group does not succeed in reducing their travel times, some agents are benefited by the coordination of the group. Regarding road pricing, experimental results indicate that the reinforcement learning scheme does not achieve the same effectiveness of a fixed pricing approach regarding the maximization of vehicular flow in the road network. Both pricing schemes are outperformed by an optimization method that assumes full knowledge of the road network state by the drivers. In the individual aspect, under pricing via reinforcement learning, drivers’ costs are higher compared to their costs under fixed pricing. The agent-based model presented in this dissertation is a contribution towards a methodology to integrate behavioral models of human travelers reacting to traffic patterns and control measures of these traffic patterns, focusing on distributed and decentralized methods.

O modelo apresentado neste trabalho, denominado SimPed, foi concebido para fornecer uma boa representação da interação entre pedestres e uma abordagem realista para a interação entre pedestres e veículos em ambiente urbano. O modelo apresenta uma estrutura híbrida, combinando conceitos baseados em campo de força e as abordagens baseadas regra. Pedestres e veículos são representados por agentes e os atributos das infraestruturas são definidos através de camadas estruturais. Este trabalho, apresenta também, o desenvolvimento de dois modelos preliminares ao SimPed. O primeiro modelo apresenta conceitos básicos de movimento de pedestres. O segundo refere-se aos problemas de geração de rotas dos pedestres. O modelo SimPed é um novo modelo de movimentação de pedestres, que considera a interação entre veículos e pedestres. A fim de verificar a aplicabilidade prática do SimPed, este trabalho apresenta três testes de simulação. O primeiro teste preocupa-se com a capacidade do modelo para representar a interação entre os pedestres. O segundo analisa uma travessia de pedestres, e foi concebido para investigar a influência do campo de força dos pedestres no desempenho do tráfego de veículos. O terceiro teste se preocupa com a representação da aceitação de gaps pelos pedestres. Neste teste os valores dos gaps obtidos na simulação são comparados com valores de gaps obtidos a partir de uma coleta de dados de vídeo em um local de travessia de pedestres. Os testes indicam que o modelo SimPed fornece bons fundamentos para uma representação de qualidade do processo de travessia dos pedestres. / The model presented in this work, named as SimPed, has been devised to provide a sound representation of interaction among pedestrians and a more realistic approach for interaction between pedestrians and vehicles. The model presents a hybrid structure, combining force field and rule based approaches. Pedestrians and vehicles assume an agent based representation and the attributes of the infrastructure are defined by several structural layers. This work presents the development of 2 preliminary models and the SimPed model. The first model concerns about basic concepts of pedestrians’ movement. The second one concerns about the pedestrians’ path generation problem. The SimPed model is a new pedestrian’s movement model with vehicle and pedestrians interaction capabilities. In order to verify the practical applicability of the SimPed, this work presents three simulation tests. The first test concerns the capacity of the model to represent interaction among pedestrians. The second analyses a pedestrian crossing environment, and was devised to investigate the influence of the force-based parameter on traffic performance. The third simulation test is concerned with pedestrians´ gap acceptance representation. In this test gap acceptance values obtained from simulation are compared with gap values obtained from a video data collection of pedestrians at a crossing facility. The tests indicate that the model structure and its calibration resources provide good grounds for sound representations of realistic conditions.

Terminais de contêineres (TC) e sistemas logísticos em geral, estão atualmente imersos em estruturas de negócio e ambientes operacionais altamente complexos e dinâmicos. Nesse ambiente, pesquisadores e usuários das ferramentas de PO são requisitados a resolver novos tipos de problemas, que surgem a partir de uma crescente complexidade interativa entre os elementos que constituem esses sistemas. Entretanto, parece estar faltando aos tomadores de decisão ferramentas capazes de lidar com sistemas que necessitam a consideração de processos interdependentes, compostos por elementos interagindo e tomando decisões de maneira descentralizada. Neste cenário, a simulação baseada em agentes (SBA) é tida como uma ferramenta potencial para o desenvolvimento e análise de sistemas logísticos, uma vez que ela é capaz de construir análises de sistemas cujo comportamento está associado a propriedades emergentes decorrentes das interações entre seus componentes (agentes). Assim, a SBA é considerada uma possibilidade de abordagem para sistemas logísticos, capaz de tratar questões complexas ainda não tangenciadas por metodologias de simulação tradicionais. Apesar do potencial alegado, a aplicação da SBA ainda é incipiente no campo logístico, representada com baixo nível de maturidade na literatura. Dessa forma, o objetivo do trabalho é desenvolver uma aplicação SBA representando uma operação full-scale do pátio de um TC, a fim de confirmar a SBA como uma ferramenta potencial para representar sistemas logísticos , capaz de apoiar processos de tomada de decisão maduros. Com base no desenvolvimento proposto foi possível, metodologicamente e de maneira prática, avaliar a utilização de SBA, seus benefícios, dificuldades, desdobramentos técnicos e outras questões. Essa avaliação permitiu concluir que a SBA cumpre as promessas de flexibilidade, representatividade e potencial de sofisticação para representação de sistemas logísticos. Além disso, observou-se que a metodologia foi capaz de estender sua contribuição no sentido de expandir algumas das fronteiras conceituais da metodologia de simulação, tais como a discussão sobre o conceito de modelagem genérica e a integração entre simulação-otimização. / Container terminals (CTs), and logistics systems in general, are nowadays immersed in a dynamic and highly complex business and operational environment. Thus, researches and users of OR are being called to solve new types of logistics system problems, born from this growing interactive complexity between the system\'s elements. However, what seems to be missing in the decision-makers OR toolbox are tools able to deal with systems that need to consider several interconnected and interdepend functions and process. In this scenario, agent-based simulation (ABS) is considered to hold high promises for developing complex logistics systems, based on the fact that it is able to build analysis of systems whose behavior is associated to emergent properties deriving from interactions between its basic constituent elements (agents). ABS is considered rather a new approach for simulating systems, able to challenge more complex questions, not answered by traditional simulation methodology. Despite the advocate potential of ABS, its application still incipient within the logistic field, lacking in terms of maturity in literarture. In that way, the objective of the work is to develop an ABS application representing a full-scale CT yard management operation, in order to confirm ABS as a potential tool to represent logistics systems and support mature decision making processes. Based on the proposed development, the work is be able to conceptually, methodologically and practically evaluate the utilization of ABS - its benefits, difficulties, application unfolding, new representation boundaries and other possibilities. This evaluation allowed concluding that the ABS fulfills the high flexibility, representability and promises for logistics systems, even extending its contribution to some of the conceptual frontiers of the simulation methodology, such as generic modeling methodology discussion and simulation-optimization integration.

O modelo apresentado neste trabalho, denominado SimPed, foi concebido para fornecer uma boa representação da interação entre pedestres e uma abordagem realista para a interação entre pedestres e veículos em ambiente urbano. O modelo apresenta uma estrutura híbrida, combinando conceitos baseados em campo de força e as abordagens baseadas regra. Pedestres e veículos são representados por agentes e os atributos das infraestruturas são definidos através de camadas estruturais. Este trabalho, apresenta também, o desenvolvimento de dois modelos preliminares ao SimPed. O primeiro modelo apresenta conceitos básicos de movimento de pedestres. O segundo refere-se aos problemas de geração de rotas dos pedestres. O modelo SimPed é um novo modelo de movimentação de pedestres, que considera a interação entre veículos e pedestres. A fim de verificar a aplicabilidade prática do SimPed, este trabalho apresenta três testes de simulação. O primeiro teste preocupa-se com a capacidade do modelo para representar a interação entre os pedestres. O segundo analisa uma travessia de pedestres, e foi concebido para investigar a influência do campo de força dos pedestres no desempenho do tráfego de veículos. O terceiro teste se preocupa com a representação da aceitação de gaps pelos pedestres. Neste teste os valores dos gaps obtidos na simulação são comparados com valores de gaps obtidos a partir de uma coleta de dados de vídeo em um local de travessia de pedestres. Os testes indicam que o modelo SimPed fornece bons fundamentos para uma representação de qualidade do processo de travessia dos pedestres. / The model presented in this work, named as SimPed, has been devised to provide a sound representation of interaction among pedestrians and a more realistic approach for interaction between pedestrians and vehicles. The model presents a hybrid structure, combining force field and rule based approaches. Pedestrians and vehicles assume an agent based representation and the attributes of the infrastructure are defined by several structural layers. This work presents the development of 2 preliminary models and the SimPed model. The first model concerns about basic concepts of pedestrians’ movement. The second one concerns about the pedestrians’ path generation problem. The SimPed model is a new pedestrian’s movement model with vehicle and pedestrians interaction capabilities. In order to verify the practical applicability of the SimPed, this work presents three simulation tests. The first test concerns the capacity of the model to represent interaction among pedestrians. The second analyses a pedestrian crossing environment, and was devised to investigate the influence of the force-based parameter on traffic performance. The third simulation test is concerned with pedestrians´ gap acceptance representation. In this test gap acceptance values obtained from simulation are compared with gap values obtained from a video data collection of pedestrians at a crossing facility. The tests indicate that the model structure and its calibration resources provide good grounds for sound representations of realistic conditions.

Sistemas de transporte são sistemas complexos compostos de diferentes entidades que interagem entre si. A otimização do uso da infraestrutura de transporte existente, que é cada vez mais necessária dado o crescente aumento da demanda por mobilidade, passa pela simulação de novas tecnologias que podem vir a ser utilizadas no futuro, como a comunicação inter-veicular (IVC) e a tarifação viária adaptativa. Esta dissertação apresenta uma abordagem baseada em agentes para simulação de comunicação inter-veicular e tarifação viária adaptativa em sistemas de transporte. Motoristas são modelados como agentes minimizadores de custo, composto pelo tempo de viagem e pelas despesas com tarifas viárias. Os motoristas podem usar IVC para expandir seu conhecimento do estado da rede viária. Entre os motoristas que usam IVC, podem existir agentes maliciosos, que buscam afastar os demais de suas rotas, através da divulgação de informações falsas. Os agentes maliciosos podem ainda agir de maneira coordenada, de modo a divulgarem informações falsas sobre as rotas de todos os agentes do grupo. Pelo lado da infraestrutura, gerentes viários percebem o fluxo de veículos nas vias da rede viária e definem as tarifas a serem aplicadas através de um esquema de aprendizado por reforço. Nos experimentos realizados, empregamos um modelo microscópico de simulação de tráfego, o que permite observar o comportamento individual de cada entidade do sistema de transporte sob estudo. O cenário onde as simulações são executadas é uma rede viária com as principais vias arteriais da cidade de Porto Alegre, Brasil. Resultados experimentais indicam que um pequeno grupo coordenado de agentes maliciosos em cenários de IVC é capaz de causar prejuízos significativos aos demais motoristas. Embora na média o grupo não consiga reduzir seu tempo de viagem, alguns agentes maliciosos são beneficiados pela coordenação do grupo. Com relação à tarifação viária, os resultados experimentais indicam que o esquema de aprendizado por reforço não possui a mesma eficácia de um esquema de tarifação fixa quando se trata da maximização de fluxo de veículos na rede viária. Ambos os esquemas de tarifação são superados por um método de otimização de tráfego que assume conhecimento completo do estado da rede viária pelos motoristas. No aspecto individual, sob tarifação via aprendizado por reforço, os custos de deslocamento dos motoristas são superiores em comparação aos custos sob tarifação fixa. O modelo baseado em agentes apresentado nesta dissertação representa uma contribuição em direção à proposição de uma metodologia para integrar modelos comportamentais de usuários de sistemas de transporte que reagem aos padrões de tráfego e medidas de controle desses padrões, com foco em métodos descentralizados e distribuídos. / Transportation systems are complex systems composed of different interacting entities. The optimization of the existing transportation infrastructure usage, which becomes increasingly necessary given the increasing demand for mobility, requires simulation of new technologies that might be used in the future, such as inter-vehicular communication (IVC) and adaptive road pricing. This dissertation presents an agent-based approach for simulation of inter-vehicular communication and adaptive road pricing in transportation systems. Drivers are modeled as cost-minimizer agents, where the cost is composed by travel time and expenditure. Drivers can use IVC to expand their knowledge of the road network state. Among the IVC users, there might be malicious agents, which try to divert other drivers from their routes by spreading false information. The malicious agents can act in a coordinated way, by spreading false information about the routes of all the agents in the group. In the infrastructure side, link managers perceive the vehicular flow in the roads and define the prices to be applied by means of a reinforcement learning scheme. In the experiments, we employ a microscopic traffic simulation model, which allows us to observe the individual behavior of each entity in the studied transportation system. The scenario where the simulations are run is a road network with the main arterial roads of the city of Porto Alegre, Brazil. Experimental results indicate that a small group of coordinated malicious agents in IVC scenarios is able to cause significant losses to the other drivers. Although in average the group does not succeed in reducing their travel times, some agents are benefited by the coordination of the group. Regarding road pricing, experimental results indicate that the reinforcement learning scheme does not achieve the same effectiveness of a fixed pricing approach regarding the maximization of vehicular flow in the road network. Both pricing schemes are outperformed by an optimization method that assumes full knowledge of the road network state by the drivers. In the individual aspect, under pricing via reinforcement learning, drivers’ costs are higher compared to their costs under fixed pricing. The agent-based model presented in this dissertation is a contribution towards a methodology to integrate behavioral models of human travelers reacting to traffic patterns and control measures of these traffic patterns, focusing on distributed and decentralized methods.

This dissertation presents a collection of manuscripts that describe development of models and model implementation to analyze impact of potential A(H7N9) pandemic influenza outbreak in the U.S. Though this virus is still only animal-to-human transmittable, it has potential to become human-to-human transmittable and trigger a pandemic. This work is motivated by the negative impact on human lives that this virus has already caused in China, and is intended to support public health officials in preparing to protect U.S. population from a potential outbreak of pandemic scale. An agent-based (AB) simulation model is used to replicate the social dynamics of the contacts between the infected and the susceptible individuals. The model updates at the end of each day the status of all individuals by estimating the infection probabilities. This considers the contact process and the contagiousness of the infected individuals given by the disease natural history of the virus. The model is implemented on sample outbreak scenarios in selected regions in the U.S. The sampling results are used to estimate disease burden for the whole U.S. The results are also used to examine the impact of various virus strengths as well as the efficacy of different intervention strategies in mitigating a pandemic burden. This dissertation, also characterizes the infection time during a A(H7N9) influenza pandemic. Continuous distributions including exponential, Weibull, and lognormal are considered as possible candidates to model the infection time. Based on the negative likelihood, lognormal distribution provides the best fit. Such characterization is important, as many critical questions about the pandemic impact can be answered from using the distribution. Finally, the dissertation focuses on assessing community preparedness to deal with pandemic outbreaks using resilience as a measure. Resilience considers the ability to recover quickly from a pandemic outbreak and is defined as a function of the percentage of healthy population at any time. The analysis, estimations, and metrics presented in this dissertation are new contributions to the literature and they offer helpful perspectives for the public health decision makers in preparing for a potential threat of A(H7N9) pandemic.

This research is based on the premises that teams can be designed to optimize its performance, and appropriate team coordination is a significant factor to team outcome performance. Contingency theory argues that the effectiveness of a team depends on the right fit of the team design factors to the particular job at hand. Therefore, organizations need computational tools capable of predict the performance of different configurations of teams. This research created an agent-based model of teams called the Team Coordination Model (TCM). The TCM estimates the coordination load and performance of a team, based on its composition, coordination mechanisms, and job’s structural characteristics. The TCM can be used to determine the team’s design characteristics that most likely lead the team to achieve optimal performance. The TCM is implemented as an agent-based discrete-event simulation application built using JAVA and Cybele Pro agent architecture. The model implements the effect of individual team design factors on team processes, but the resulting performance emerges from the behavior of the agents. These team member agents use decision making, and explicit and implicit mechanisms to coordinate the job. The model validation included the comparison of the TCM’s results with statistics from a real team and with the results predicted by the team performance literature. An illustrative 26-1 fractional factorial experimental design demonstrates the application of the simulation model to the design of a team. The results from the ANOVA analysis have been used to recommend the combination of levels of the experimental factors that optimize the completion time for a team that runs sailboats races. This research main contribution to the team modeling literature is a model capable of simulating teams working on complex job environments. The TCM implements a stochastic job structure model capable of capturing some of the complexity not capture by current models. In a stochastic job structure, the tasks required to complete the job change during the team execution of the job. This research proposed three new types of dependencies between tasks required to model a job as a stochastic structure. These dependencies are conditional sequential, single-conditional sequential, and the merge dependencies.

Public transport network design deals with finding the most efficient network solution among a set of alternatives, that best satisfies the often-conflicting objectives of different network stakeholders like passengers and operators. Simulation-based Optimisation (SBO) is a discipline that solves optimisation problems by combining simulation and optimisation models. The former is used to evaluate the alternative solutions, while the latter searches for the optimal solution among them. A SBO model for designing public transport networks is developed in this dissertation. The context of the research is the MyCiTi Bus Rapid Transit (BRT) network in the City of Cape Town, South Africa. A multi-objective optimisation algorithm known as the Non-dominated Sorting Genetic Algorithm (NSGA-II) is integrated with Activity-based Travel Demand Model (ABTDM) known as the Multi-Agent Transport Simulation (MATSim). The steps taken to achieve the research objectives are first to generate a set of feasible network alternatives. This is achieved by manipulating the existing routes of the MyCiTi BRT with a computer based heuristic algorithm. The process is guided by feasibility conditions which guarantee that each network has routes that are acceptable for public transport operations. MATSim is then used to evaluate the generated alternatives, by simulating the daily plans of travellers on each network. A typical daily plan is a sequential ordering of all the trips made by a commuter within a day. Automated Fare Collection (AFC) data from the MyCiTi BRT was used to create this plan. Lastly, the NSGA-II is used to search for an efficient set of network solutions, also known as a Pareto set or a non-dominated set in the context of Multi-objective Optimisation (MOO). In each generation of the optimisation process, MATSim is used to evaluate the current solution. Hence a suitable encoding scheme is defined to enable a smooth iv translation of the solution between the NSGA-II and MATSim. Since the solution of multi-objective optimisation problems is a set of network solutions, further analysis is done to identify the best compromise solution in the Pareto set. Extensive computational testing of the SBO model has been carried out. The tests involve evaluating the computational performance of the model. The first test measures the repeatability of the model's result. The second computational test considers its performance relative to indicators like the hypervolume and spacing indicators as well as an analysis of the model's Pareto front. Lastly, a benchmarking of the model's performance when compared with other optimisation algorithms is carried out. After testing the so-called Simulation-based Transit Network Design Model (SBTNDM), it is then used to design pubic transport networks for the MyCiTi BRT. Two applications are considered for the model. The first application deals with the public transport performance of the network solutions in the Pareto front obtained from the SBTNDM. In this case study, different transport network indicators are used to measure how each solution performs. In the second scenario, network design is done for the 85th percentile of travel demand on the MyCiTi network over 12 months. The results show that the model can design robust transit networks. The use of simulation as the agency of optimisation of public transport networks represents the main innovation of the work. The approach has not been used for public transport network design to date. The specific contribution of this work is in the improved modelling of public transport user behaviour with Agent-based Simulation (ABS) within a Transit Network Design (TND) framework. This is different from the conventional approaches used in the literature, where static trip-based travel demand models like the four-step model have mostly been used. Another contribution of the work is the development of a robust technique that facilitates the simultaneous optimisation of network routes and their operational frequencies. Future endeavours will focus on extending the network design model to a multi-modal context.

<div>Social influence networks are collections of entities dealing with a shared issue on which they have individual opinions. These opinions are dynamic, changing over time due to influence from other entities. Mechanisms within the network can affect how influence leads to opinion change, such as the strength and number of social ties between agents and the decision models used by an individual to process information from its neighbors. In real-world scenarios, these mechanisms are often hidden. Much effort in social network analysis involves proposing models and attempting to replicate target output data with them. Can we instead use the evolution of opinions in a network to infer these mechanisms directly?</div><div><br></div><div>This work explores how opinion change in social influence networks can be used to determine characteristics of those networks. Broadly, this is accomplished by simulating social influence networks using various designs and initial conditions to generate opinion data, and then identifying relationships between response variables and changes to the simulation inputs. Key inputs include the population size, the influence model that controls how agents change their opinions, the network structure, the activation regime that controls the sequencing of opinion updates, and probability distributions for communication errors. Analyzing the opinions of individual agents can provide insights about the individuals (microscopic), but in this work, focus is on insights into the social influence network as a complete system (macroscopic), so opinion data is aggregated according to each response variable.</div><div><br></div><div>Response variables are designed through the lens of complexity theory. Three types of complexity measurements are applied to opinion data: regression, entropy, and a new complexity measure. In each case, relationships between design factors and response variables are diverse. The influence model and the distribution of communication errors---a factor often omitted from the literature---are consistently impactful, with their various settings producing distinct profiles in time series plots of the measurements. Activation regime is impactful to some entropy measures. Network structure has little impact on the new complexity measure, and population size has little impact in general. Overall, distinctive relationships can exist between opinions and design factors. These relationships, as well as the measures and problem-solving approaches used in this work, may be helpful to analysts working to infer the properties of real-world social influence networks from the opinion data those systems generate.</div>

The computational Grid has become an important infrastructure as an execution environment for scientific applications that require large amount of computing resources. Applications which would otherwise be unmanageable or take a prohibitively longer execution time under previous computing paradigms can now be executed efficiently on the Grid within a reasonable time. Multi-agent based simulation (MABS) is a methodology used to study and understand the dynamics of real world phenomena in domains involving interaction and/or cooperative problem solving where the participants are characterized by entities having autonomous and social behaviour. For certain domains the size of the simulation is extremely large, intractable without employing adequate computing resources such as the Grid. Although the Grid has come with immense opportunities to resource demanding applications such as MABS, it has also brought with it a number of challenges related to performance. Performance problems may have their origins either on the side of the computing infrastructure or the application itself, or both. This thesis aims at improving the performance of MABS applications by overcoming problems inherent to the behaviour of MABS applications. It also studies the extent to which the MABS technologies have been exploited in the field of simulation and find ways to adapt existing technologies for the Grid. It investigates performance monitoring and prediction systems in the Grid environment and their implementation for MABS application with the purpose of identifying application related performance problems and their solutions. Our research shows that large-scale MABS applications have not been implemented despite the fact that many problem domains that cannot be studied properly with only partial simulation. We assume that this is due to the lack of appropriate tools such as MABS platforms for the Grid. Another important finding of this work is the improvement of application performance through the use of MABS specific middleware.