Human behavior plays an important role in infectious disease epidemics. The choice of preventive actions taken by individuals can completely change the epidemic outcome. Computational epidemiologists usually employ large-scale agent-based simulations of human populations to study disease outbreaks and assess intervention strategies. Such simulations rarely take into account the decision-making process of human beings when it comes to preventive behaviors. Absence of realistic agent behavior can undermine the reliability of insights generated by such simulations and might make them ill-suited for informing public health policies. In this thesis, we address this problem by developing a methodology to create and calibrate an agent decision-making model for a large multi-agent simulation, in a data driven way. Our method optimizes a cost vector associated with the various behaviors to match the behavior distributions observed in a detailed survey of human behaviors during influenza outbreaks. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations. / Master of Science / In the real world, individuals can decide to adopt certain behaviors that reduce their chances of contracting a disease. For example, using hand sanitizers can reduce an individual‘s chances of getting infected by influenza. These behavioral decisions, when taken by many individuals in the population, can completely change the course of the disease. Such behavioral decision-making is generally not considered during in-silico simulations of infectious diseases. In this thesis, we address this problem by developing a methodology to create and calibrate a decision making model that can be used by agents (i.e., synthetic representations of humans in simulations) in a data driven way. Our method also finds a cost associated with such behaviors and matches the distribution of behavior observed in the real world with that observed in a survey. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Jessica L. Heier Stamm / Coordination between humanitarian organizations is critical during the response effort to a disaster, as coordinating aid improves efficiency, reduces duplication of efforts, and ultimately leads to better outcomes for beneficiaries. One particular challenge arises when temporary facilities must be established post-disaster due to the destruction of buildings. For example, the 2015 Nepal earthquakes created a need for the placement of over 4,000 temporary learning facilities after several school buildings were damaged or destroyed. It is important that humanitarians coordinate well to fill these needs efficiently and effectively, while maintaining equity among beneficiaries in the affected areas. This means ensuring that enough facilities are provided in a timely manner, and are distributed fairly to all in need. The goals of this thesis are to study coordination strategies focusing primarily on the placement of temporary educational facilities for children following a disaster. This research also aims to gather useful data by surveying active humanitarians in order to better understand their decisions made in the field. This work uses the results of this survey, along with publicly available data published after the 2015 Nepal earthquakes to create an agent-based simulation model, and uses the Nepal case study to demonstrate the efficacy of the model framework. This research finds that organizations' initial location of operation can greatly impact the number of facilities they are collectively able to establish, the geographic disparity across the region, and the organizations' utilization. Specifically, while focusing efforts on the districts with the most need is most efficient and effective, a more uniform approach yields a more equitable response. This work also finds that there can be a trade-off between overall effectiveness and the number of partnerships established in the field. These findings show a need for further study into the intricacies of coordination between humanitarian workers. This author advocates for the use of information sharing mechanisms among practitioners, as well as further utilization of agent-based modeling as a means of studying the complex nature of disaster response. Specifically there is a need to further study educational needs as a logistical problem, and strategies for solving the post-disaster facility location problem.

This thesis explores how multi-agent-based simulation can be used for transport policy analysis. Transport policies are often used as a means to reach governmental goals, such as environmental targets to reduce the impact of transportation. To predict how transportation is influenced by policies, public authorities often make use of simulation models. A structured review of such models is made focussing on important transport chain characteristics. We argue that to properly predict the actual environmental, economic, and logistical effects of transport policies, the logistical decisions made in transport chains must be modelled appropriately. Such decisions, e.g., concern the choice of producer and traffic mode, planning of transportation, production, and terminal handling. The review concludes that models currently used for transport policy analysis fail to capture many of these characteristics. We argue that agent-based models have the potential to include these aspects since they are able to explicitly model the actual decision making in transport chains. We have identified a set of generic roles in transport chains where each role is responsible for certain decisions. A multi-agent-based simulator, TAPAS, has been developed in which these roles are modelled as agents. Thus, the decision making in transport chains and its influence by the application of transport policies are captured. The decisions lead to the execution of the logistical operations which in turn have consequences on the logistics, economic, and environmental performance. The usage of TAPAS is illustrated by presenting two scenarios based on realworld transport chains. Simulation experiments of the scenarios have been performed where different types of transport policies are introduced. The simulation results are analysed, e.g., by comparing the results to similar studies and by sensitivity analysis of input parameters. To facilitate the validation and generalisation of simulation results we suggest making use of typical transport chains and roles characterised by, e.g., product type and geographical locations. The type of studies that TAPAS can support are described and compared to studies typically made with traditional models. Transport policies which are relevant to examine are described and their potential influence on transport chains are analysed. The possible usage of TAPAS is discussed and related to different types of users. Public authorities can, e.g., use TAPAS to complement studies using traditional models. This can improve the accuracy of the simulation results by the inclusion of more logistical aspects. Large companies are another type of user which, e.g., can use TAPAS to analyse new market segments, such as new product types or new consumers, where historical data is not available.

In this thesis, the COMAC ARJ21 fuselage’s final assembly process is used as a case study. High production rate (i.e. number of aircraft assembled per year) with reasonable cost is the overall aim in this example. The output of final assembly will essentially affect the prior and subsequent processes of the overall ARJ21 production. From the collected field data, it was identified that a number of disruptions (or bottlenecks) in the assembly sequence were caused by breakdowns and maintenance of the (semi-)automatic assembly machines like portable computer numerical control (CNC) drilling machine, rivet gun and overhead crane. The focus of this thesis is therefore on the maintenance strategies (i.e. Condition-Based Maintenance (CBM)) for these equipment and how they impact the throughput of the fuselage assembly process. The fuselage assembly process is modelled and analysed by using agent-based simulation in this thesis. The agent approach allows complex process interactions of assembly, equipment and maintenance to be captured and empirically studied. In this thesis, the built network is modelled as the sequence of activities in each stage. Each stage is broken down into critical activities which are parameterized by activity lead-time and equipment used. CBM based models of uncertain degradation and imperfect maintenance are used in the simulation study. A scatter search is used to find multi-objective optimal solutions for the CBM regime, where the maintenance-related cost and production rate are the optimization objectives. In this thesis, in order to ease computation intensity caused by running multiple simulations during the optimization and to simplify a multi-objective formulation, multiple Min-Max weightings are applied to trace Pareto front. The empirical analysis reviews the trade-offs between the production rate and maintenance cost and how these objectives are influenced by the design parameters.

Risk-based regulation has grown rapidly as a component of Government decision making, and as such, the need for an established evidence-based framework for decisions about risk has become the new mantra. However, the process of brokering scientific evidence is poorly understood and there is a need to improve the transparency of this brokering process and decisions made. This thesis attempts to achieve this by using agent-based simulation to model the influence that power structures and participating personalities has on the brokering of evidence and thereby the confidence-building exercise that characterises risk-based regulation. As a prerequisite to the adoption of agent-based techniques for simulating decisions under uncertainty, this thesis provides a critical review of the influence power structure and personality have on the brokering of scientific evidence that informs risk decisions. Three case studies, each representing a different perspective on risk-based regulation are presented: nuclear waste disposal, the disposal of avian-influenza infected animal carcases and the reduction of dietary salt intake. Semi-structured interviews were conducted with an expert from each case study, and the logical sequence in which decisions were made was mapped out and used to inform the development of an agent-based simulation model. The developed agent-based model was designed to capture the character of the brokering process by transparently setting out how evidence is transmitted from the provider of evidence to the final decision maker. It comprises of two agents, a recipient and provider of evidence, and draws upon a historic knowledge base to permit the user to vary components of the interacting agents and of the decision-making procedure, demonstrating the influence that power structure and personality has on agent receptivity and the confidence attached to a number of different lines of evidence. This is a novel step forward because it goes beyond the scope of current risk management frameworks, for example, permitting the user to explore the influence that participants have in weighing and strengthening different lines of evidence and the impact this has on the final decision outcome.

Healthcare systems are traditionally characterised by complexity and heterogeneity. With the continuous increase in size and shrinkage of available resources, the healthcare sector faces the challenge of delivering high quality services with fewer resources. Healthcare organisations cannot be seen in isolation since the services of one such affects the performance of other healthcare organisations. Efficient management and forward planning, not only locally but rather across the whole system, could support healthcare sector to overcome the challenges. An example of closely interwoven organisations within the healthcare sector is the emergency medical services (EMS). EMS operate in a region and usually consist of one ambulance service and the available accident and emergency (A&E) departments within the coverage area. EMS provide, mainly, pre-hospital treatment and transport to the appropriate A&E units. The life-critical nature of EMS demands continuous systems improvement practices. Modelling and Simulation (M&S) has been used to analyse either the ambulance services or the A&E departments. However, the size and complexity of EMS systems constitute the conventional M&S techniques inadequate to model the system as a whole. This research adopts the approach of distributed simulation to model all the EMS components as individual and composable simulations that are able to run as standalone simulation, as well as federates in a distributed simulation (DS) model. Moreover, the hybrid approach connects agent-based simulation (ABS) and discrete event simulation (DES) models in order to accommodate the heterogeneity of the EMS components. The proposed FIELDS Framework for Integrated EMS Large-scale Distributed Simulation supports the re-use of existing, heterogeneous models that can be linked with the High Level Architecture (HLA) protocol for distributed simulation in order to compose large-scale simulation models. Based on FIELDS, a prototype ABS-DES distributed simulation EMS model was developed based on the London EMS. Experiments were conducted with the model and the system was tested in terms of performance and scalability measures to assess the feasibility of the proposed approach. The yielded results indicate that it is feasible to develop hybrid DS models of EMS that enables holistic analysis of the system and support model re-use. The main contributions of this thesis is a distributed simulation methodology that derived along the process of conducting this project, the FIELDS framework for hybrid EMS distributed simulation studies that support re-use of existing simulation models, and a prototype distributed simulation model that can be potentially used as a tool for EMS analysis and improvement.

La co-construction et la réutilisation de modèles font l'objet de plusieurs travaux dans le domaine de la simulation. Cependant, dans le domaine plus spécifique de la Simulation Orientée Agent (SOA), nous pouvons constater un manque sur ces deux points malgré un besoin fort de la part des thématiciens. 
La co-construction est essentielle pour optimiser la mise en commun du savoir de différents experts, mais nous faisons souvent face à des divergences de points de vue. Les méthodologies existantes pour la co-construction en SOA ne permettent qu'un faible niveau de collaboration entre thématiciens durant la phase initiale de modélisation, ainsi qu'entre les des thématiciens avec les modélisateurs ou les modélisateurs-informaticiens... Pour faciliter cette co-construction, nous proposons de suivre une méthodologie de conception favorisant cette collaboration. 
La réutilisation de modèle octroie un gain de temps significatif, une amélioration du modèle et l'apport de nouvelle connaissance. Les méthodologies en SOA dans ce domaine existent. Cependant, dans le spectre de réutilisation, elles sont souvent limitées au niveau du modèle complet ou de l'agent avec l'impossibilité de "descendre" plus bas. 
L'expérience de EDMMAS, un cas concret d'un modèle issu de trois réutilisations successives, nous a permis de constater une nouvelle complexité qui découle de la démultiplication des comportements des agents et crée un décalage conséquent entre le modèle opérationnel et le modèle conceptuel. Notre objectif est de promouvoir la réutilisation aussi bien des modèles, que des agents et de leurs comportements.Pour répondre à ces questionnements, nous proposons dans ce manuscrit une manière de codifier et d'intégrer la connaissance provenant de disciplines différentes dans le modèle, tout en utilisant des modules "composables" qui facilitent la réutilisation. Nous proposons (i) une nouvelle architecture Agent (aMVC), appliquée dans un cadre multidynamique (DOM), avec l'appui (ii) d'une approche méthodologique (MMC) basée sur la décomposition et réutilisation des comportements. 
Cet ensemble de propositions, (i) et (ii), permet de conduire un projet pluridisciplinaire de SOA avec un grand nombre d'acteurs, facilitant la co-construction des modèles grâce à l'instauration de nouvelles synergies entre les différents acteurs participant à la modélisation. Les concepteurs pourront travailler de manière autonome sur leur dynamique et la plateforme fera l'intégration de ces dernières en assurant la cohésion et la robustesse du système. Nos contributions offrent la capacité de créer les briques élémentaires du système de manière indépendante, de les associer et de les combiner pour former des agents, selon des dynamiques conformément à l'approche DOM. Elles permettent ainsi de comparer la logique selon différentes possibilités pour une même dynamique et d'ouvrir la perspective d'étudier un grand nombre d'alternatives de modélisation d'un même système complexe, et de les analyser ensuite à une échelle très fine. / Co-building and reuse of models are at the center of several studies in the field of simulation. However, in the more specific field ofMulti-Agent Based Simulation (MABS), there is a lack of methodology to resolve these two issues, despite a strong need by experts.Model co-building is essential to optimize knowledge sharing amongst different experts, but we often face divergent viewpoints. Existing methodologies for the MABS co-building allow only a low level of collaboration among experts during the initial phase of modeling, and between domain experts with modelers or computer scientists... In order to help this co-building, we propose and follow a methodology to facilitate this collaboration. Model reuse can provide significant time savings, improve models’ quality and offer new knowledge. Some MABS methodologies in this area exist. However, in the spectrum of reuse, they are often limited to a full model’s reuse or agent’s reuse with the impossibility of reusing smaller parts such as behaviors. The EDMMAS experiment was a concrete case of three successive model reuses. It allowed us to observe new complexity arising from the increase of agents’ behaviors. This creates a gap between operational model and conceptual model.Our goal is to promote the reuse of models, agents and their behaviors.To answer these questions, we propose in this thesis a new way to codify and integrate knowledge from different disciplines in the model, while using "composable"modules that facilitate reuse.We propose (i) a new agent architecture (aMVC), applied to a multidynamical approach (DOM), with the support (ii) of a methodology (MMC) based on the decompositionand reuse of behaviors.Proposals (i) and (ii) allow us to lead a multidisciplinary MABS project with a large number of actors, helping the co-building of models through the introduction of synergies among the different actors involved in the modeling. They can work independently on their dynamics and the platformwill integrate those, ensuring cohesion and robustness of the system. Our contributions include the ability to create the building blocks of the system independently, associate and combine them to formagents. This allows us to compare possibilities for the same dynamic and open the prospect of studyingmany alternate models of the same complex system, and then analyze at a very fine scale.

No description available.

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.

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.