Empresas em cadeias de suprimentos têm como objetivo maximizar suas produtividades, e consequentemente seus lucros. Uma maneira de estudar o comportamento destas cadeias é simulá-las utilizando uma abordagem multi-agentes. Neste trabalho, adicionamos múltiplos agentes nos níveis de um modelo amplamente adotado na literatura, denominado Beer Game, para avaliar a eficiência local e global dos fornecedores. Para tal, utilizamos perfis distintos baseados em confiança ou em preço. Medimos o impacto de usar tais políticas de seleção no capital acumulado pelos agentes, e concluímos que as parcerias baseadas em confiança são recomendáveis em cenários com ampla disseminação de informações verdadeiras, enquanto que as baseadas em lucro são mais vantajosas em cenários marcados por pouca comunicação ou pelo espalhamento de informações falsas. / Companies in supply chains have the objective of maximizing their productivities, and consequently their profits. A way of to study the behavior of these chains is to simulate them using a multi-agent-based approach. In this work, we added multiple agents at the levels of a widely adopted model in the literature, called Beer Game, to evaluate the local and global performance of suppliers. To do this, we use distinct profiles based on trust or price. We measure the impact of using such selection policies on the agents\' profit, and we could conclude that trust-based partnerships are recommended in scenarios with wide dissemination of true information, while profit-based partnerships are most advantageous in scenarios marked by poor communication or spreading false information.

Applying Agent-Based Modeling and Simulation (ABMS) methodology, this paper analyzes the impact of alternative production-sales policies on the diffusion of a new product and the generated NPV of profit. The key features of the ABMS model, that captures the marketplace as a complex adaptive system, are: (i) supply chain capacity is constrained; (ii) consumers' new product adoption decisions are influenced by marketing activities as well as positive and negative word of mouth (WOM) between consumers; (iii) interactions among consumers taking place in the context of their social network are captured at the individual level; and (iv) the new product adoption process is adaptive. Conducting over 1 million simulation experiments, we determined the "best" productionsales policies under various parameter combinations based on the NPV of profit generated over the diffusion process. The key findings are as follows: (1) on average, the build-up policy with delayed marketing is the preferred policy in the case of only positive WOM as well as the case of positive and negative WOM. This policy provides the highest expected NPV of profit on average and it also performs very smoothly with respect to changes in build-up periods. (2) It is critical to consider the significant impact of negative word-of-mouth on the outcomes of alternative production-sales policies. Neglecting the effect of negative word-of-mouth can lead to poor policy recommendations, incorrect conclusions concerning the impact of operational parameters on the policy choice, and suboptimal choice of build-up periods. (authors' abstract)

Agent-based simulation of smart environment finds its application in studying people’s movement to help the design of a variety of applications such as energy utilization, HAVC control and egress strategy in emergency situation. Traditionally, agent-based simulation is not dynamic data driven, they run offline and do not assimilate real sensor data about the environment. As more and more buildings are equipped with various sensors, it is possible to utilize real time sensor data to inform the simulation. To incorporate the real sensor data into the simulation, we introduce the method of data assimilation. The goal of data assimilation is to provide inference about system state based on the incomplete, ambiguous and uncertain sensor data using a computer model. A typical data assimilation framework consists of a computer model, a series of sensors and a melding scheme. The purpose of this dissertation is to develop a data assimilation framework for agent-based simulation of smart environment. With the developed data assimilation framework, we demonstrate an application of building occupancy estimation which focuses on position estimation using the framework. We build an agent based model to simulate the occupants’ movement s in the building and use this model in the data assimilation framework. The melding scheme we use to incorporate sensor data into the built model is particle filter algorithm. It is a set of statistical method aiming at compute the posterior distribution of the underlying system using a set of samples. It has the benefit that it does not have any assumption about the target distribution and does not require the target system to be written in analytic form .To overcome the high dimensional state space problem as the number of agents increases, we develop a new resampling method named as the component set resampling and evaluate its effectiveness in data assimilation. We also developed a graph-based model for simulating building occupancy. The developed model will be used for carrying out building occupancy estimation with extremely large number of agents in the future.

This thesis investigates the causes of the long wait-time for patients in Emergency department (ED) of Victoria General Hospital, and suggests changes for improvements. Two prominent simulation techniques have been used to replicate the ED in a simulation model. These are Discrete-event simulation (DES) and Agent-based modeling (ABM). While DES provides the basic modeling framework ABM has been used to incorporate human behaviour in the ED. The patient flow in the ED has been divided into 3 phases: input, throughput, and output. Model results show that there could be multiple interventions to reduce time taken to be seen by the doctor for the first time (also called WTBS) either in the output phase or in the input phase. The model is able to predict that a reduction in the output phase would cause reduction in the WTBS but it is not equipped to suggest how this reduction can be achieved. To reduce WTBS by making interventions in the input phase this research proposes a strategy called fast-track treatment (FTT). This strategy helps the model to dynamically re-allocate resources if needed to alleviate high WTBS. Results show that FTT can reduce WTBS times by up-to 40%.

This paper proposes a new model for studying the new product development process in an artificial environment. We show how connectionist models can be used to simulate the adaptive nature of agents' learning exhibiting similar behavior as practically experienced learning curves. We study the impact of incentive schemes (local, hybrid and global) on the new product development process for different types of organizations. Sequential organizational structures are compared to two different types of team-based organizations, incorporating methods of Quality Function Deployment such as the House of Quality. A key finding of this analysis is that the firms' organizational structure and agents' incentive system significantly interact. We show that the House of Quality is less affected by the incentive scheme than firms using a Trial & Error approach. This becomes an important factor for new product success when the agents' performance measures are conflicting. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

This thesis investigates the causes of the long wait-time for patients in Emergency department (ED) of Victoria General Hospital, and suggests changes for improvements. Two prominent simulation techniques have been used to replicate the ED in a simulation model. These are Discrete-event simulation (DES) and Agent-based modeling (ABM). While DES provides the basic modeling framework ABM has been used to incorporate human behaviour in the ED. The patient flow in the ED has been divided into 3 phases: input, throughput, and output. Model results show that there could be multiple interventions to reduce time taken to be seen by the doctor for the first time (also called WTBS) either in the output phase or in the input phase. The model is able to predict that a reduction in the output phase would cause reduction in the WTBS but it is not equipped to suggest how this reduction can be achieved. To reduce WTBS by making interventions in the input phase this research proposes a strategy called fast-track treatment (FTT). This strategy helps the model to dynamically re-allocate resources if needed to alleviate high WTBS. Results show that FTT can reduce WTBS times by up-to 40%.

In a peer-to-peer shared ride system, transportation clients with traffic demand negotiate with transportation hosts offering shared ride services for ad-hoc ridesharing in a continuously changing environment, using wireless geosensor networks. Due to the distinctive characteristic of this system—a complex and non-deterministic transportation network, and a local peer-to-peer communication strategy—clients will always have limited transportation knowledge, both from a spatial and a temporal perspective. Clients hear only from nearby hosts, and they do not know the future availability of current or new hosts. Clients can plan optimal trips prior to departure according to their current knowledge, but it is unlikely that these trips will be final optimal trip due to continuously changing traffic conditions. Therefore, it is necessary to evaluate the trip quality in this dynamic environment in order to assess different communication and wayfinding strategies. (For complete abstract open document)

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.

abstract: This thesis presents a model for the buying behavior of consumers in a technology market. In this model, a potential consumer is not perfectly rational, but exhibits bounded rationality following the axioms of prospect theory: reference dependence, diminishing returns and loss sensitivity. To evaluate the products on different criteria, the analytic hierarchy process is used, which allows for relative comparisons. The analytic hierarchy process proposes that when making a choice between several alternatives, one should measure the products by comparing them relative to each other. This allows the user to put numbers to subjective criteria. Additionally, evidence suggests that a consumer will often consider not only their own evaluation of a product, but also the choices of other consumers. Thus, the model in this paper applies prospect theory to products with multiple attributes using word of mouth as a criteria in the evaluation. / Dissertation/Thesis / Masters Thesis Applied Mathematics 2014