Any one organization or agency, whether for-profit or non-profit organizations that are seeking to enhance their efficiency, improve production technology, thereby achieving the goal of improving productivity, with a view to the current competitive environment. Efficiency measurement is very important, it can help decision makers understand whether the organization achieve technology progress and innovation objectives. In recent years, the government and civil organizations devote themselves to measure the change of organizational efficiency and productivity. Academia constantly research and develop various models of efficiency and productivity analysis, and application to actual cases analysis. Efficiency and productivity analysis has leapt to the mainstream of production economic studies. This empirical study adopts the census data of the classification of the Chamber of Commerce and industry of manufacturing in Taiwan, using two-stage approach to explore dynamic agglomeration externalities of 2-digit manufacturing. In the first stage, we apply dynamic network data envelopment analysis and Malmquist productivity index to calculate static efficiency and dynamic efficiency of 2-digit manufacturing. In the second stage, we apply Tobit regression analysis to verify a manufacturing geographical concentration effects on productive efficiency. We also adopt two-stage least squares methods (2SLS) to validate dynamic agglomeration externalities effects of manufacturing. Based on the results of this empical study, we propose some specific practical policy alternatives and management strategies. In the last 20 years, the strctures of Taiwan manufacturing industries have significant changes, the livelihood industry and of the sharp decline in industry, the chemical industry, electronics industry, metal machinery industry is growing fast. There is an obvous agglomeration tendency toward northern Taiwan region. In static efficiency, labour-intensive manufacturing industries tend to be diminishing return to scale rendering, while knowledge-intensive industries are rendering the increasing trend. The scale efficiency of eastern region manufacturing is very low, resulting in their productive efficiency significantly lower than the northern, central, southern regional manufacturing. In dynamic efficiency, the total factor productivity (TFP) of Taiwan manufacturing industries are rendering the growth trend, achieving the goal of innovation effect. However, the technical efficiency of manufacturing are rendering decline trend. This study found that the most important impact factor on production efficiency is the internal economies of scale. Localization economies, urbanization economies, and other static agglomeration economies external effect gradually reduce. Moreover, this study also found that Taiwan manufacturing industries have notable MAR professional dynamic external economics and notable Porter regional competitive dynamic external economic effect. Besides, Taiwan manufacturing industries has noticeable human resource dynamic external economics, but we also found low wages is beneficial to regional economic growth. We should not expand to explain Taiwan manufacturing-sweatshops. This phenomenon may be caused by high salaries, high rents, high land costs and high labor costs, these factors offset the interest of agglomeration economies. Finally, Taiwan and mainland China signed a cross-strait economic cooperation framework agreement (ECFA) in Chongqing on 29 June 2010. Taiwan manufacturing inevitably be impacted and influenced by ECFA. This is an important topic worthy of further study and discussion in the future.

The cell transmission model (CTM) based single destination system optimal dynamic traffic assignment (SD-SO-DTA) model has been widely applied to situations such as mass evacuations on a transportation network. Although formulated as a linear programming (LP) model, embedded multi-period cell network representation yields an extremely large model for real-size networks. As a result, most of these models are not solvable using existing LP solvers. Solutions obtained by LP also involve holding vehicles at certain locations, violating CTM flow dynamics. This doctoral research is aimed at developing innovative algorithms that overcome both computational efficiency and solution realism issues. We first prove that the LP formulation of the SD-SO-DTA problem is equivalent to the earliest arrival flow (EAF), and then develop efficient algorithms to solve EAF. Two variants of the algorithm are developed under different model assumptions and network operating conditions. For the case of time-varying network parameters, we develop a network flow algorithm on a time-expanded network. The main challenge in this approach is to address the issue of having backward wave speed lower than forward wave speed. This situation leads to non-typical constraints involving coefficients with value of less than 1. In this dissertation we develop a new network algorithm to solve this problem in optimal, even with coefficients of value less than 1. Additionally, the developed approach solves for optimal flows that exhibit non-vehicle-holding properties, which is a major breakthrough compared to all existing solution techniques for SD-SODTA. For the case of time-invariant network parameters, we reduce the SD-SO-DTA to a standard EAF problem on a dynamic network, which is constructed on the original roadway network without dividing it into cells. We prove that the EAF under free flow status is one of the optimal solutions of SD-SO-DTA, if cell properties follow a trapezoidal/triangular fundamental diagram. We use chain flows obtained on a static network to induce dynamic flows, an approach applicable to large-scale networks. Another contribution of this research is to provide a simple and practical algorithm solving the EAF with multiple sources, which has been an active research area for many years. Most existing studies involve submodular function optimization as subroutines, and thus are not practical for real-life implementation. This study’s contribution in this regard is the development of a practical algorithm that avoids submodular function optimization. The main body of the given method is comprised of |S⁺| iterations of earliest arrival s - t flow computations, where |S⁺| is the number of sources. Numerical results show that our multi-source EAF algorithm solves the SD-SO-DTA problem with time-invariant parameters to optimum.

abstract: Short-notice disasters such as hurricanes involve uncertainties in many facets, from the time of its occurrence to its impacts’ magnitude. Failure to incorporate these uncertainties can affect the effectiveness of the emergency responses. In the case of a hurricane event, uncertainties and corresponding impacts during a storm event can quickly cascade. Over the past decades, various storm forecast models have been developed to predict the storm uncertainties; however, access to the usage of these models is limited. Hence, as the first part of this research, a data-driven simulation model is developed with aim to generate spatial-temporal storm predicted hazards for each possible hurricane track modeled. The simulation model identifies a means to represent uncertainty in storm’s movement and its associated potential hazards in the form of probabilistic scenarios tree where each branch is associated with scenario-level storm track and weather profile. Storm hazards, such as strong winds, torrential rain, and storm surges, can inflict significant damage on the road network and affect the population’s ability to move during the storm event. A cascading network failure algorithm is introduced in the second part of the research. The algorithm takes the scenario-level storm hazards to predict uncertainties in mobility states over the storm event. In the third part of the research, a methodology is proposed to generate a sequence of actions that simultaneously solve the evacuation flow scheduling and suggested routes which minimize the total flow time, or the makespan, for the evacuation process from origins to destinations in the resulting stochastic time-dependent network. The methodology is implemented for the 2017 Hurricane Irma case study to recommend an evacuation policy for Manatee County, FL. The results are compared with evacuation plans for assumed scenarios; the research suggests that evacuation recommendations that are based on single scenarios reduce the effectiveness of the evacuation procedure. The overall contributions of the research presented here are new methodologies to: (1) predict and visualize the spatial-temporal impacts of an oncoming storm event, (2) predict uncertainties in the impacts to transportation infrastructure and mobility, and (3) determine the quickest evacuation schedule and routes under the uncertainties within the resulting stochastic transportation networks. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2020

The sixth-generation (6G) of wireless communication systems will significantly rely on fog/edge network architectures for service provisioning. To satisfy stringent quality of service requirements using dynamically available resources at the edge, new network access schemes are needed. In this paper, we consider a cognitive dynamic edge/fog network where primary users (PUs) may temporarily share their resources and act as fog nodes for secondary users (SUs). We develop strategies for distributed dynamic fog probing so SUs can find out available connections to access the fog nodes. To handle the large-state space of the connectivity availability that includes availability of channels, computing resources, and fog nodes, and the partial observability of the states, we design a novel distributed Deep Q-learning Fog Probing (DQFP) algorithm. Our goal is to develop multi-user strategies for accessing fog nodes in a distributed manner without any centralized scheduling or message passing. By using cooperative and competitive utility functions, we analyze the impact of the multi-user dynamics on the connectivity availability and establish design principles for our DQFP algorithm.

No description available.

The Internet has made tremendous progress since its inception. The kingpin has been the transmission control protocol (TCP), which supports a large fraction of communication. With the Internet's wide-spread access, users now have increased expectations. The demands have evolved to an extent which TCP was never designed to support. Since network stacks do not provide the necessary functionality for modern applications, developers are forced to implement them over and over again --- as part of the application or supporting libraries. Consequently, application developers not only bear the burden of developing application features but are also responsible for building networking libraries to support sophisticated scenarios. This leads to considerable duplication of effort. The challenge for TCP in supporting modern use cases is mostly due to limiting assumptions, simplistic communication abstractions, and (once expedient) implementation shortcuts. To further add to the complexity, the limited TCP options space is insufficient to support extensibility and thus, contemporary communication patterns. Some argue that radical changes are required to extend the networks functionality; some researchers believe that a clean slate approach is the only path forward. Others suggest that evolution of the network stack is necessary to ensure wider adoption --- by avoiding a flag day. In either case, we see that the proposed solutions have not been adopted by the community at large. This is perhaps because the cost of transition from the incumbent to the new technology outweighs the value offered. In some cases, the limited scope of the proposed solutions limit their value. In other cases, the lack of backward compatibility or significant porting effort precludes incremental adoption altogether. In this dissertation, we focus on the development of a communication model that explicitly acknowledges the context of the conversation and describes (much of) modern communications. We highlight how the communication stack should be able to discover, interact with and use available resources to compose richer communication constructs. The model is able to do so by using session, flow and endpoint abstractions to describe communications between two or more endpoints. These abstractions provide means to the application developers for setting up and manipulating constructs, while the ability to recognize change in the operating context and reconfigure the constructs allows applications to adapt to the changing requirements. The model considers two or more participants to be involved in the conversation and thus enables most modern communication patterns, which is in contrast with the well-established two-participant model. Our contributions also include an implementation of a framework that realizes such communication methods and enables future innovation. We substantiate our claims by demonstrating case studies where we use the proposed abstractions to highlight the gains. We also show how the proposed model may be implemented in a backwards compatible manner, such that it does not break legacy applications, network stacks, or middleboxes in the network infrastructure. We also present use cases to substantiate our claims about backwards compatibility. This establishes that incremental evolution is possible. We highlight the benefits of context awareness in setting up complex communication constructs by presenting use cases and their evaluation. Finally, we show how the communication model may open the door for new and richer communication patterns. / PHD

Objetivo. Desenvolvimento de uma estrutura para aplicação de simulação numérica estocástica no estudo de espalhamento de doenças em metapopulações de maneira que esta incorpore a topologia dinâmica de contatos entre as subpopulações, verificando as peculiaridades do modelo e aplicando este modelo às redes de movimentação de animais de Pernambuco para estudar o papel das feiras de animais. Método. Foi utilizado o paradigma de modelos híbridos para tratar do espalhamento de doenças nas metapopulações que, das nossas aplicações, resultou na união de duas estratégias de modelagem: Modelos Baseados no Indivíduo e o Algorítimo de Simulação Estocástica. Aplicamos os modelos híbridos em redes de movimentação de animais reais e fictícias para destacar as diferenças dos modelos híbridos com diferentes abordagens de migração (pendular e definitiva) e comparamos estes modelos com modelos clássicos de equações diferenciais. Ainda, através do pacote hybridModels, estudamos o papel das feiras de animais em cenários de epidemia de febre aftosa na rede de movimentação de animais de Pernambuco, introduzindo a doença numa feira de animais contida numa amostra da base de Guia de Trânsito Animal e calculamos a cadeia de infecção dos estabelecimentos. Resultados. Constatamos que no estudo de epidemias com o uso de modelo híbrido, a migração pendular, na média, subestima o número de animais infectados no cenário de comercialização de animais (migração defi nitiva), além de traduzir uma dinâmica de espalhamento enganosa, ignorando cenários mais complexo oferecido pela migração definitiva. Criamos o pacote hybridModels que generaliza os modelos híbridos com migração definitiva e com ele aplicamos um modelo híbrido SIR na rede de Pernambuco e verificamos que as feiras de animais de Pernambuco são potentes disseminadores de doenças transmissíveis. Conclusão. Apesar de custo computacional maior no estudo de espalhamento de doenças, a migração definitiva é o mais adequado tipo de conexão entre as subpopulações de animais de produção. Ainda, de acordo com as nossas analises, as feiras de animais estão entre os mais importantes nós na rede de movimentação de Pernambuco e devem ter lugar de destaque nas estratégias de controle e vigilância epidemiológica / Objective. Development of framework applied to stochastic numerical simulation for the study of disease spreading in metapopulations, in a way that it incorporates the dynamic topology of contacts between subpopulations, checking the framework peculiarities and applying it to the animal movement network of Pernambuco to study the role of animal markets. Method. We used hybrid models paradigm to treat disease spread in metapopulations. From our applications it has resulted in the union of two modeling strategies: Individual-based model and the Algorithm for Stochastic Simulation. We applied hybrid models in real and fictitious networks to highlight the differences between different animal movement approaches (commuting and migration) and we compared these models with classic models of differential equations. Furthermore, through the hybridModels package, we studied the role of animal markets in epidemic scenarios of Foot and Mouth Disease (FMD) in animal movement networks of Pernambuco, introducing the disease in an animal market of a sample from the Animal Transit Record of Pernambuco’s database and calculating the contact infection chain of premises. Results. We noted that in the study of epidemics using a hybrid model, commuting can underestimates the number of infected animals in the animal trade scenario (migration), and resulting in a misleading spreading dynamic by ignoring a more complex scenario that occurs with migration. We created the hybridModels package that generalizes the hybrid models with migration, applied a SIR hybrid model to the animal movement network of Pernambuco and verified that animal markets are important disease spreaders. Conclusion. Despite its higher computational cost in the study of epidemics in animal movement networks, migration is the most suitable type of connection between subpopulations. Furthermore, animal markets of Pernambuco are among the most important nodes for disease transmission and should be considered in strategies of surveillance and disease control

Kooperatives Verhalten lässt sich in vielen Bereichen menschlichen Zusammenlebens sowie im gesamten Tierreich beobachten. In evolutionären Modellen wurde gezeigt, dass Netzwerkstrukturen die Kooperation erhöhen können. Empirische Studien versuchten vergeblich diesen Mechanismus auch bei Menschen nachzuweisen. Es scheint, als würden Netzwerke nur dann die Kooperation erhöhen, wenn die Strukturen nicht statisch sind, sondern dynamisch. Das heißt, dass die Individuen die Möglichkeit haben, ihre Partner zu wechseln. Eine wichtige – aber bislang unerforschte – Eigenschaft dynamischer Netzwerke ist jedoch, dass derartige Wechsel von Partnern in der Regel Kosten verursachen, ob in Form von Zeit oder Ressourcen. Kapitel I meiner Arbeit schließt diese Lücke, in dem es sich mit den Effekten von Kosten auf dynamischen Netzwerken befasst. Ich konnte nachweisen, dass Menschen seltener Interaktionen mit Partnern beendeten, wenn die Kontaktaufnahme mit einem neuen Partner mit Kosten verbunden war. Bei sehr hohen Kosten, wurden Partner so selten gewechselt, dass das Netzwerk fast statisch war. Interessanterweise blieb die Kooperation dennoch sehr hoch. Das bedeutet, dass für kooperatives Verhalten entscheidend ist, ob man die Möglichkeit hat, Partner zu wechseln. Im Gegensatz zu bisherigen Annahmen ist es daher nicht wichtig, wie oft tatsächlich Partner gewechselt werden, sondern lediglich ob es die Möglichkeit dazu gibt. In Kapitel II beschäftige ich mich mit optimalem Entscheidungsverhalten. Im sogenannten Judge-Advisor-System geht es darum, dass eine Person, der Judge, eine unbekannte numerische Größe schätzen will. Dazu erhält der Judge eine zweite unabhängige Schätzung als Rat von einer zweiten Person, des Advisor. Schließlich ist die Frage, wie der Judge optimal den Rat verwerten kann um seine Anfangsschätzung zu verbessern. Bisherige Forschung konzentrierte sich hauptsächlich auf zwei mögliche Methoden, (i) das Bilden des Mittelwerts und (ii) das Wählen der besseren Anfangsschätzung. Das Hauptargument für diese einfachen Methoden ist deren häufige Verwendung in bisherigen Experimenten. Allerdings wurden sehr wohl auch andere Gewichtungen beobachtet und daher ist eine gründliche Analyse der optimalen Gewichtung erforderlich. In der vorliegenden Arbeit leitete ich ein normatives Modell her, das beschreibt, unter welchen Bedingungen welche Methode das bestmögliche Ergebnis liefert. Es wurden drei Methoden verglichen: (i) das Bilden des Durchschnitts, (ii) das Wählen der besseren Anfangsschätzung, und (iii) das Bilden eines gewichtetet Mittelwerts, wobei das Gewicht vom Kompetenzunterschied abhängt. Welche Methode optimal ist, hängt davon ab, wie groß der Kompetenzunterschied ist und wie gut er vom Judge erkannt wird. Die Durchschnittbildung ist immer dann vorteilhaft, wenn der Kompetenzunterschied nicht groß ist, oder nur schwer richtig eingeschätzt werden kann. Wenig überraschend lohnt sich das Wählen der besseren Anfangsschätzung, wenn der Kompetenzunterschied hinreichend groß ist, vorausgesetzt es wird tatsächlich die bessere Anfangsschätzung gewählt. Wenn der Kompetenzunterschied vom Judge gut eingeschätzt werden kann, ist eine Entsprechende Gewichtung immer die beste Methode, unabhängig vom tatsächlichen Unterschied. In Übereinstimmung mit bisheriger Forschung wurde auch die Kombination von Durchschnittbildung und Wählen der besseren Anfangsschätzung untersucht. Diese Kombinationsmethode beruht darauf, bei als gering eingeschätztem Kompetenzunterschied den Durchschnitt zu bilden und ansonsten die bessere Anfangsschätzung zu wählen. Interessanterweise schneidet diese Kombinationsmethode sehr schlecht ab, was hauptsächlich daran liegt, dass zu oft die falsche Anfangsschätzung genommen würde. Insgesamt ist das gewichtete Mittel also eine geeignete Methode für einen großen Parameterbereich.

Cette thèse de doctorat traite de différentes questions concernant la communication et la transmission d’informations dans le cadre de la théorie des jeux. J’analyse différents dilemmes auxquels peut être confronté un joueur qui envoie des informations. Ces dilemmes correspondent aux questions suivantes: "Devrais-je investir dans un message vérifiable?", "Quand dois-je transmettre mon information?" et "Est-il préférable de ne pas envoyer mon information et uniquement de recueillir l’information des autres?". Cette thèse comprend une introduction et trois chapitres. L’introduction contient une motivation générale pour les trois problèmes que je présente dans cette thèse. Je donne une vue d’ensemble détaillée de tous les chapitres, j’examine la littérature relative au sujet et je la compare à mes résultats. / This Ph.D dissertation addresses different issues concerning communication and information transmission in a game theoretical framework. I analyze different dilemmas that a player who sends information has to deal with. These dilemmas correspond to the following questions: "Should I invest into a verifiable message?", "When should I pass my information?" and "Is it better if I do not send my information, but collect information from others?". This thesis includes an introduction and three chapters. The introduction contains a general motivation for the three different problems that I model in this thesis. I give a detailed overview of all the chapters, survey the related literature and compare it to my results.

Objetivo. Desenvolvimento de uma estrutura para aplicação de simulação numérica estocástica no estudo de espalhamento de doenças em metapopulações de maneira que esta incorpore a topologia dinâmica de contatos entre as subpopulações, verificando as peculiaridades do modelo e aplicando este modelo às redes de movimentação de animais de Pernambuco para estudar o papel das feiras de animais. Método. Foi utilizado o paradigma de modelos híbridos para tratar do espalhamento de doenças nas metapopulações que, das nossas aplicações, resultou na união de duas estratégias de modelagem: Modelos Baseados no Indivíduo e o Algorítimo de Simulação Estocástica. Aplicamos os modelos híbridos em redes de movimentação de animais reais e fictícias para destacar as diferenças dos modelos híbridos com diferentes abordagens de migração (pendular e definitiva) e comparamos estes modelos com modelos clássicos de equações diferenciais. Ainda, através do pacote hybridModels, estudamos o papel das feiras de animais em cenários de epidemia de febre aftosa na rede de movimentação de animais de Pernambuco, introduzindo a doença numa feira de animais contida numa amostra da base de Guia de Trânsito Animal e calculamos a cadeia de infecção dos estabelecimentos. Resultados. Constatamos que no estudo de epidemias com o uso de modelo híbrido, a migração pendular, na média, subestima o número de animais infectados no cenário de comercialização de animais (migração defi nitiva), além de traduzir uma dinâmica de espalhamento enganosa, ignorando cenários mais complexo oferecido pela migração definitiva. Criamos o pacote hybridModels que generaliza os modelos híbridos com migração definitiva e com ele aplicamos um modelo híbrido SIR na rede de Pernambuco e verificamos que as feiras de animais de Pernambuco são potentes disseminadores de doenças transmissíveis. Conclusão. Apesar de custo computacional maior no estudo de espalhamento de doenças, a migração definitiva é o mais adequado tipo de conexão entre as subpopulações de animais de produção. Ainda, de acordo com as nossas analises, as feiras de animais estão entre os mais importantes nós na rede de movimentação de Pernambuco e devem ter lugar de destaque nas estratégias de controle e vigilância epidemiológica / Objective. Development of framework applied to stochastic numerical simulation for the study of disease spreading in metapopulations, in a way that it incorporates the dynamic topology of contacts between subpopulations, checking the framework peculiarities and applying it to the animal movement network of Pernambuco to study the role of animal markets. Method. We used hybrid models paradigm to treat disease spread in metapopulations. From our applications it has resulted in the union of two modeling strategies: Individual-based model and the Algorithm for Stochastic Simulation. We applied hybrid models in real and fictitious networks to highlight the differences between different animal movement approaches (commuting and migration) and we compared these models with classic models of differential equations. Furthermore, through the hybridModels package, we studied the role of animal markets in epidemic scenarios of Foot and Mouth Disease (FMD) in animal movement networks of Pernambuco, introducing the disease in an animal market of a sample from the Animal Transit Record of Pernambuco’s database and calculating the contact infection chain of premises. Results. We noted that in the study of epidemics using a hybrid model, commuting can underestimates the number of infected animals in the animal trade scenario (migration), and resulting in a misleading spreading dynamic by ignoring a more complex scenario that occurs with migration. We created the hybridModels package that generalizes the hybrid models with migration, applied a SIR hybrid model to the animal movement network of Pernambuco and verified that animal markets are important disease spreaders. Conclusion. Despite its higher computational cost in the study of epidemics in animal movement networks, migration is the most suitable type of connection between subpopulations. Furthermore, animal markets of Pernambuco are among the most important nodes for disease transmission and should be considered in strategies of surveillance and disease control