A diagnostic framework for demand amplification problems in supply chains

This dissertation delivers a framework to diagnose the Bull-Whip Effect (BWE) in supply chains and then identify methods to minimize it. Such a framework is needed because in spite of the significant amount of literature discussing the bull-whip effect, many companies continue to experience the wide variations in demand that are indicative of the bull-whip effect. While the theory and knowledge of the bull-whip effect is well established, there still is the lack of an engineering framework and method to systematically identify the problem, diagnose its causes, and identify remedies. ^ The present work seeks to fill this gap by providing a holistic, systems perspective to bull-whip identification and diagnosis. The framework employs the SCOR reference model to examine the supply chain processes with a baseline measure of demand amplification. Then, research of the supply chain structural and behavioral features is conducted by means of the system dynamics modeling method. ^ The contribution of the diagnostic framework, is called Demand Amplification Protocol (DAMP), relies not only on the improvement of existent methods but also contributes with original developments introduced to accomplish successful diagnosis. DAMP contributes a comprehensive methodology that captures the dynamic complexities of supply chain processes. The method also contributes a BWE measurement method that is suitable for actual supply chains because of its low data requirements, and introduces a BWE scorecard for relating established causes to a central BWE metric. In addition, the dissertation makes a methodological contribution to the analysis of system dynamic models with a technique for statistical screening called SS-Opt, which determines the inputs with the greatest impact on the bull-whip effect by means of perturbation analysis and subsequent multivariate optimization. The dissertation describes the implementation of the DAMP framework in an actual case study that exposes the approach, analysis, results and conclusions. The case study suggests a balanced solution between costs and demand amplification can better serve both firms and supply chain interests. Insights pinpoint to supplier network redesign, postponement in manufacturing operations and collaborative forecasting agreements with main distributors.^

An integrated multi-agent framework for optimizing time, cost and environmental impact of construction processes

Environmentally conscious construction has received a significant amount of research attention during the last decades. Even though construction literature is rich in studies that emphasize the importance of environmental impact during the construction phase, most of the previous studies failed to combine environmental analysis with other project performance criteria in construction. This is mainly because most of the studies have overlooked the multi-objective nature of construction projects. In order to achieve environmentally conscious construction, multi-objectives and their relationships need to be successfully analyzed in the complex construction environment. The complex construction system is composed of changing project conditions that have an impact on the relationship between time, cost and environmental impact (TCEI) of construction operations. Yet, this impact is still unknown by construction professionals. Studying this impact is vital to fulfill multiple project objectives and achieve environmentally conscious construction. This research proposes an analytical framework to analyze the impact of changing project conditions on the relationship of TCEI. This study includes green house gas (GHG) emissions as an environmental impact category. The methodology utilizes multi-agent systems, multi-objective optimization, analytical network process, and system dynamics tools to study the relationships of TCEI and support decision-making under the influence of project conditions. Life cycle assessment (LCA) is applied to the evaluation of environmental impact in terms of GHG. The mixed method approach allowed for the collection and analysis of qualitative and quantitative data. Structured interviews of professionals in the highway construction field were conducted to gain their perspectives in decision-making under the influence of certain project conditions, while the quantitative data were collected from the Florida Department of Transportation (FDOT) for highway resurfacing projects. The data collected were used to test the framework. The framework yielded statistically significant results in simulating project conditions and optimizing TCEI. The results showed that the change in project conditions had a significant impact on the TCEI optimal solutions. The correlation between TCEI suggested that they affected each other positively, but in different strengths. The findings of the study will assist contractors to visualize the impact of their decision on the relationship of TCEI.

Essays on city size distribution and real estate bubbles

This is a dissertation about urban systems; within this broad subject I tackle three issues, one that focuses on an observed inter-city relationship and two that focus on an intra-city phenomenon. In Chapter II I adapt a model of random emergence of economic opportunities from the firm growth literature to the urban dynamics situation and present several predictions for urban system dynamics. One of these predictions is that the older the city the larger and more diversified it is going to be on average, which I proceed to verify empirically using two distinct datasets. In Chapter III I analyze the Residential Real Estate Bubble that took place in Miami-Dade County from 1999 to 2006. I adopt a Spatial-Economic model developed for the Paris Bubble episode of 1984–1993 and formulate an innovative test of the results in terms of speculative intensity on the basis of proxies of investor activity available in my dataset. My results support the idea that the best or more expensive areas are also where the greatest speculative activity takes place and where the rapid increase in prices begins. The most significant departure from previous studies that emerges in my results is the absence of a wider gap between high priced areas and low priced areas in the peak year. I develop a measure of dispersion in value among areas and contrast the Miami-Dade and Paris episodes. In Chapter IV I analyze the impact on tax equity of a Florida tax-limiting legislation known as Save Our Homes. I first compare homesteaded and non-homesteaded properties, and second, look within the subset of homesteaded properties. I find that non-homesteaded properties increase their share of taxes paid relative to homesteaded properties during an up market, but that this is reversed during a down market. For the subset of homesteaded properties I find that the impact on tax equity of SOH will depend on differential growth rates among higher and lower valued homes, but during times of rapid home price appreciation, in a scenario of no differential growth rates in property values, SOH increases progressivity relative to the prior system.

Frameworks for Systemic and Structural Analysis of Financial Innovations in Infrastructure

Financial innovations have emerged globally to close the gap between the rising global demand for infrastructures and the availability of financing sources offered by traditional financing mechanisms such as fuel taxation, tax-exempt bonds, and federal and state funds. The key to sustainable innovative financing mechanisms is effective policymaking. This paper discusses the theoretical framework of a research study whose objective is to structurally and systemically assess financial innovations in global infrastructures. The research aims to create analysis frameworks, taxonomies and constructs, and simulation models pertaining to the dynamics of the innovation process to be used in policy analysis. Structural assessment of innovative financing focuses on the typologies and loci of innovations and evaluates the performance of different types of innovative financing mechanisms. Systemic analysis of innovative financing explores the determinants of the innovation process using the System of Innovation approach. The final deliverables of the research include propositions pertaining to the constituents of System of Innovation for infrastructure finance which include the players, institutions, activities, and networks. These static constructs are used to develop a hybrid Agent-Based/System Dynamics simulation model to derive propositions regarding the emergent dynamics of the system. The initial outcomes of the research study are presented in this paper and include: (a) an archetype for mapping innovative financing mechanisms, (b) a System of Systems-based analysis framework to identify the dimensions of Systems of Innovation analyses, and (c) initial observations regarding the players, institutions, activities, and networks of the System of Innovation in the context of the U.S. transportation infrastructure financing.

Freshwater Resource Supply Modeling for Developed and Undeveloped Watersheds

Globally, the current state of freshwater resource management is insufficient and impeding the chance at a sustainable future. Human interference within the natural hydrologic cycle is becoming dangerously irreversible and the need to redefine resource managerial approaches is imminent. This research involves the development of a coupled natural-human freshwater resource supply model using a System Dynamics approach. The model was applied to two case studies, Somalia, Africa and the Phoenix Active Management Area in Arizona, USA. It is suggested that System Dynamic modeling would be an invaluable tool for achieving sustainable freshwater resource management in individual watersheds. Through a series of thought experiments, a thorough understanding of the systems’ dynamic behaviors is obtainable for freshwater resource managers and policy-makers to examine various courses of action for alleviating freshwater supply concerns. This thesis reviews the model, its development and an analysis of several thought experiments applied to the case studies.

Seawater carbonate chemistry and hydrogen ions and carbonate chemistry in calcifying fluid of coral Astrangia poculata during experiments, 2011

A generalized physicochemical model of the response of marine organisms' calcifying fluids to CO2-induced ocean acidification is proposed. The model is based upon the hypothesis that some marine calcifiers induce calcification by elevating pH, and thus Omega aragonite, of their calcifying fluid by removing protons (H+). The model is explored through two end-member scenarios: one in which a fixed number of H+ is removed from their calcifying fluid, regardless of atmospheric pCO2, and another in which a fixed external-internal proton ratio ([H+]E/[H+]I) is maintained. The model is able to generate the full range of calcification response patterns observed in prior ocean acidification experiments and is consistent with the assertion that organisms' calcification response to ocean acidification is more negative for marine calcifiers that exert weaker control over their calcifying fluid pH. The model is empirically evaluated for the temperate scleractinian coral Astrangia poculata with in situ pH microelectrode measurements of the coral's calcifying fluid under control and acidified conditions. These measurements reveal that (1) the pH of the coral's calcifying fluid is substantially elevated relative to its external seawater under both control and acidified conditions, (2) the coral's [H+]E/[H+]I remains constant under control and acidified conditions, and (3) the coral removes fewer H+ from its calcifying fluid under acidified conditions than under control conditions. Thus, the carbonate system dynamics of A. poculata's calcifying fluid appear to be most consistent with the fixed [H+]E/[H+]I end-member scenario. Similar microelectrode experiments performed on additional taxa are required to assess the model's general applicability.

TEST BINARY IMPORT

Der Müller und die fünf Räuber, Überfall²³

A Data Driven Tool supported CLD Creation Approach

The creation of Causal Loop Diagrams (CLDs) is a major phase in the System Dynamics (SD) life-cycle, since the created CLDs express dependencies and feedback in the system under study, as well as, guide modellers in building meaningful simulation models. The cre-ation of CLDs is still subject to the modeller's domain expertise (mental model) and her ability to abstract the system, because of the strong de-pendency on semantic knowledge. Since the beginning of SD, available system data sources (written and numerical models) have always been sparsely available, very limited and imperfect and thus of little benefit to the whole modelling process. However, in recent years, we have seen an explosion in generated data, especially in all business related domains that are analysed via Business Dynamics (BD). In this paper, we intro-duce a systematic tool supported CLD creation approach, which analyses and utilises available disparate data sources within the business domain. We demonstrate the application of our methodology on a given business use-case and evaluate the resulting CLD. Finally, we propose directions for future research to further push the automation in the CLD creation and increase confidence in the generated CLDs.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Using modeling as a co-design approach in the planning process of new care environments

Current research shows a relationship between healthcare architecture and patient-related Outcomes. The planning and designing of new healthcare environments is a complex process; the needs of the various end-users of the environment must be considered, including the patients, the patients’ significant others, and the staff. The aim of this study was to explore the experiences of healthcare professionals participating in group modelling utilizing system dynamics in the pre-design phase of new healthcare environments. We engaged healthcare professionals in a series of workshops using system dynamics to discuss the planning of healthcare environments in the beginning of a construction, and then interviewed them about their experience. An explorative and qualitative design was used to describe participants’ experiences of participating in the group modelling projects. Participants (n=20) were recruited from a larger intervention study using group modeling and system dynamics in planning and designing projects. The interviews were analysed by qualitative content analysis. Two themes were formed, representing the experiences in the group modeling process: ‘Partaking in the G-M created knowledge and empowerment’and ‘Partaking in the G-M was different from what was expected and required time and skills’. The method can support participants in design teams to focus more on their healthcare organization, their care activities and their aims rather than focusing on detailed layout solutions. This clarification is important when decisions about the design are discussed and prepared and will most likely lead to greater readiness for future building process.

Techniques for batch reinforcement learning in robotics

This thesis addresses the Batch Reinforcement Learning methods in Robotics. This sub-class of Reinforcement Learning has shown promising results and has been the focus of recent research. Three contributions are proposed that aim to extend the state-of-art methods allowing for a faster and more stable learning process, such as required for learning in Robotics. The Q-learning update-rule is widely applied, since it allows to learn without the presence of a model of the environment. However, this update-rule is transition-based and does not take advantage of the underlying episodic structure of collected batch of interactions. The Q-Batch update-rule is proposed in this thesis, to process experiencies along the trajectories collected in the interaction phase. This allows a faster propagation of obtained rewards and penalties, resulting in faster and more robust learning. Non-parametric function approximations are explored, such as Gaussian Processes. This type of approximators allows to encode prior knowledge about the latent function, in the form of kernels, providing a higher level of exibility and accuracy. The application of Gaussian Processes in Batch Reinforcement Learning presented a higher performance in learning tasks than other function approximations used in the literature. Lastly, in order to extract more information from the experiences collected by the agent, model-learning techniques are incorporated to learn the system dynamics. In this way, it is possible to augment the set of collected experiences with experiences generated through planning using the learned models. Experiments were carried out mainly in simulation, with some tests carried out in a physical robotic platform. The obtained results show that the proposed approaches are able to outperform the classical Fitted Q Iteration.

The Control of a Mathematical Analog of a Tentacle

A 2-dimensional dynamic analog of squid tentacles was presented. The tentacle analog consists of a multi-cell structure, which can be easily replicated to a large scale. Each cell of the model is a quadrilateral with unit masses at the corners. Each side of the quadrilateral is a spring-damper system in parallel. The spring constants are the controls for the system. The dynamics are subject to the constraint that the area of each quadrilateral must remain constant. The system dynamics was analyzed, and various equilibrium points were found with different controls. Then these equilibrium points were further determined experimentally, demonstrated to be asymptotically stable. A simulation built in MATLAB was used to find the convergence rates under different controls and damping coefficients. Finally, a control scheme was developed and used to drive the system to several configurations observed in real tentacle.

Balancing Belligerents or Feeding the Beast: Transforming Conflict Traps

Since the end of the Cold War, recurring civil conflicts have been the dominant form of violent armed conflict in the world, accounting for 70% of conflicts active between 2000-2013. Duration and intensity of episodes within recurring conflicts in Africa exhibit four behaviors characteristic of archetypal dynamic system structures. The overarching questions asked in this study are whether these patterns are robustly correlated with fundamental concepts of resiliency in dynamic systems that scale from micro-to macro levels; are they consistent with theoretical risk factors and causal mechanisms; and what are the policy implications. Econometric analysis and dynamic systems modeling of 36 conflicts in Africa between 1989 -2014 are combined with process tracing in a case study of Somalia to evaluate correlations between state characteristics, peace operations and foreign aid on the likelihood of observed conflict patterns, test hypothesized causal mechanisms across scales, and develop policy recommendations for increasing human security while decreasing resiliency of belligerents. Findings are that observed conflict patterns scale from micro to macro levels; are strongly correlated with state characteristics that proxy a mix of cooperative (e.g., gender equality) and coercive (e.g., security forces) conflict-balancing mechanisms; and are weakly correlated with UN and regional peace operations and humanitarian aid. Interactions between peace operations and aid interventions that effect conflict persistence at micro levels are not seen in macro level analysis, due to interdependent, micro-level feedback mechanisms, sequencing, and lagged effects. This study finds that the dynamic system structures associated with observed conflict patterns contain tipping points between balancing mechanisms at the interface of micro-macro level interactions that are determined as much by factors related to how intervention policies are designed and implemented, as what they are. Policy implications are that reducing risk of conflict persistence requires that peace operations and aid interventions (1) simultaneously increase transparency, promote inclusivity (with emphasis on gender equality), and empower local civilian involvement in accountability measures at the local levels; (2) build bridges to horizontally and vertically integrate across levels; and (3) pave pathways towards conflict transformation mechanisms and justice that scale from the individual, to community, regional, and national levels.

Modelo para gestionar el conocimiento en el sector textil de Medellín, empleando dinámica de sistemas

Este artículo evalúa la relación de causalidad entre la gestión del conocimiento y las capacidades de innovación tecnológica, y el efecto de esta relación sobre los resultados operacionales del sector textil en la ciudad de Medellín. Se empleó la metodología de dinámica de sistemas, con simulación de esce­narios para valorar las condiciones actuales de las organizaciones del sector en términos de acumulación de conocimiento y capacidades. La información se obtuvo mediante entrevistas a expertos y acceso a información especializada del sector. Se evidencia que una mejora de la relación entre la gestión del conocimiento e innovación tecnológica genera un incremento aproximado del 15% en los ingresos operacionales del sector. Asimismo, se encontró que a medida que las variables comunes de interés (Es­trategias organizacionales, canales de comunicación, formación, cultura, acciones de fortalecimiento en I+D), se acercan a los valores deseados, la acumulación de conocimiento y de capacidades de innovación tecnológica alcanzan los valores objetivos.