Thesis: Ph. D., Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2015. / Vita. Cataloged from PDF version of thesis. / Includes bibliographical references (pages 227-237). / Inclusive wealth (IW) measures the productive base of an economy, which is a linear index of its capital asset stocks. Changes to IW per capita over time track changes to intergenerational human well-being, thus non-declining IW per capita indicates sustainable development. National IW has only been measured retrospectively; this dissertation models and projects IW prospectively, measuring the impact of alternative infrastructure plans on IW. The focus of the work is on electricity planning in oil-exporting countries. Domestic oil consumption in these countries, driven by increasing electricity use, threatens long-term development by reducing the export revenue on which the government and economy depends. First, I develop a system dynamics model that connects electric power capacity expansion with macroeconomic development, tabulating both infrastructure costs and impacts to the capital stocks of IW over time. The Kingdom of Saudi Arabia (KSA) is the primary case study. Second, I analyze the capital stock projections generated by the model across a range of scenarios and countries. Under the baseline IW formulation, KSA experiences a negative annual growth rate to inclusive wealth per capita to 2050. However, adjusted formulations allow the possibility of periods of positive growth, and a non-oil sector that is less dependent on the oil sector will shift the IW trajectory upwards. Compared to KSA, Kuwait is likely to experience larger per capita declines in IW. The United Arab Emirates (UAE), on the other hand, will potentially experience positive growth rates in per capita IW starting in 2028. Third, I analyze the IW impacts of non-fossil investments in electricity infrastructure, specifically nuclear and solar, between now and 2050. In KSA, the produced capital benefits of non-fossil investment outweigh the oil capital costs (to finance the infrastructure) across a range of uncertainties. Including human capital benefits raises net benefits by an order of magnitude. The optimal allocation of nuclear and solar power ultimately depends on the evaluation metric used. The UAE gains least from non-fossil investment, since it uses comparatively less oil in its electricity system, while Kuwait experiences gains similar to KSA. Importantly, using IW as the basis for electricity policy evaluation yields qualitatively different prescriptions than a least-cost capacity expansion model. / by Ross D. Collins. / Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2014. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 163-179). / Motivated by inspection applications for marine structures, this thesis develops algorithms to enable their autonomous inspection. Two essential parts of the inspection problem are (1) path planning and (2) surface reconstruction. On the first problem, we develop a novel analysis of asymptotic optimality of control-space sampling path planning algorithms. This analysis demonstrated that asymptotically optimal path planning for any Lipschitz continuous dynamical system can be achieved by sampling the control space directly. We also determine theoretical convergence rates for this class of algorithms. These two contributions were also illustrated numerically via extensive simulation. Based on the above analysis, we developed a new inspection planning algorithm, called Random Inspection Tree Algorithm (RITA). Given a perfect model of a structure, sensor specifications, robot dynamics, and an initial configuration of a robot, RITA computes the optimal inspection trajectory that observes all surface points on the structure. This algorithm uses of control-space sampling techniques to find admissible trajectories with decreasing cost. As the number of iterations increases, RITA converges to optimal control trajectories. A rich set of simulation results, motivated by inspection problems for marine structures, illustrate our methods. Data gathered from all different views of the structure are assembled to reconstruct a 3D model of the external surfaces of the structure of interest. Our work also involved field experimentation. We use off-the-shelf sensors and a robotic platform to scan marine structures above and below the waterline. Using such scanned data points, we reconstruct triangulated polyhedral surface models of marine structures based on Poisson techniques. We have tested our system extensively in field experiments at sea. We present results on construction of various 3D surface models of marine structures, such as stationary jetties and slowly moving structures (floating platforms and boats). This work contributes to the autonomous inspection problem for structures and to the optimal path, inspection and task planning problems. / by Georgios Papadopoulos. / Ph. D.

Thesis (Sc.D.)--Massachusetts Institute of Technology. Dept. of Chemical Engineering, 1950. / Vita. / Bibliography: leaves 225-228. / by Pao-chen Wu. / Sc.D.

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 114-120). / This thesis proposes a research framework that leverages high-resolution traffic and urban infrastructure data to improve analytical approximation methods used to inform strategic decisions in designing last-mile distribution systems. In particular, this thesis explores the effects of the road-network on the circuity of local trips, and introduces data-driven extensions to improve predictive performance of route distance approximation methods by increasing the resolution of the underlying urban road-network. Overall, these circuity-based extensions significantly increase the real-world validity of routing approximations compared to classical methods, and entail relevant implications in the configuration of logistics networks within urban markets. The framework presented in this thesis entails three inter-dependent levels of analysis: individual trip, consolidated route and last-mile network levels. In Chapter 2, we introduce a method to quantify and analyze the network circuity of local trips leveraging contemporary traffic datasets. Using the city of Sao Paulo as the primary illustrative example and a combination of supervised and unsupervised machine learning methods, significant heterogeneities in local network circuity are observed, explained by dimensional and topological properties of the road-network. Results from Sao Paulo are compared to seven additional large and medium-sized urban areas in Latin America and the United States. At a coarse-grained level of analysis, we observe similar correlations between road-network properties and local circuity across these cities. In Chapter 3, this thesis proposes a data-driven extension to continuum approximation-based methods used to predict urban route distances. This extension efficiently incorporates the circuity of the underlying road-network into the approximation method to improve distance predictions in more realistic settings. The proposed extension significantly outperforms classic methods, which build on the assumption of travel according to the rectilinear distance metric within urban areas. By only marginally increasing the data collection effort, results of the proposed extension yield error reductions between 20-30% in mean absolute percentage error compared to classical approximation methods and are within 10 - 20% compared to near-optimal solutions obtained with a local search heuristic. Further, by providing a real-world validation of classic continuum approximation-based methods, we explore how contemporary mapping technologies and novel sources of geo-spatial and traffic data can be efficiently leveraged to improve the predictive performance of these methods. Finally, building on the augmented route distance approximation, in Chapter 4 we explore the effect of road-network circuity on the design and planning of urban last-mile distribution systems. These improved routing approximations are used within an integer linear programming model to solve large-scale, real-world instances of the two-echelon capacitated location routing problem. Using the parcel delivery operation of Brazil's largest e-commerce platform in the city of Sao Paulo as the primary example to illustrate the impact and relevance of this work, we demonstrate how explicitly accounting for local variations in road-network circuity can yield relevant implications for fleet capacity planning, the location of urban distribution facilities, and the definition of facility-specific service areas. Results indicate that failing to account for local circuity would underestimate the necessary fleet size by 20% and would increase the total last-mile network cost by approximately 8%. / by Daniel Esteban Merchán Dueñas. / Ph. D. in Engineering Systems

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, June 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 102-115). / This work aims to provide a widely applicable modeling framework that can be used to credibly investigate materials scarcity risks for various types of commodities. Different from existing literature, this work contributes to a better understanding of commodity scarcity risk, specifically copper future consumption on several fronts. Firstly, it introduces an elaborate price mechanism absent in comparable materials flow assessment. It teases out short term and long term substitution, allowing consumers to switch from one type of commodity to another based on price signals and their respective price elasticities of demand. Secondly, the model allows for individual deposit tracking, which allows the modeler to extract ore grade information as a function of consumption and reserve size. Thirdly, it models the supply side on an agent-based basis, allowing for aggregation of granular information, capturing potential emergent phenomena. We believe these three aspects, which are least addressed (none of existing work has addressed the first aspect, and few have addressed the second or the third), are important in assessing scarcity risks. Without them, scarcity assessment is likely to be biased. We hope our work may serve as some sort of foundation upon which more reliable future work on mineral scarcity evaluation can be carried out. / by Jingshu Zhang. / Ph. D. in Engineering Systems

Thesis: Ph. D., Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2015. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 123-130). / Sustainability negotiations and decisions require the integration of scientific information with stakeholder interests. Mathematical models help elucidate the physical world and therefore may orient the negotiators in a shared understanding of the physical world. Many researchers suggest collaborative modeling to facilitate integrating scientific information and stakeholder interests. In this thesis, I use methods that enable repeated instances of the same decision; the exploration of alternatives to model use (e.g. learning of a model's logic, relevant information, or irrelevant information); and the exploration of alternatives to collaborative modeling (e.g. using an expert model or not using a model). This thesis comprises two studies that use serious game role-play simulations. The first study is a computer-driven role-play simulation of governmental policy creation and the second is a five-party role-play simulation to negotiate a more sustainable end-of-life for used paper coffee cups. In the first study, model users reached the Pareto Frontier-the set of non-dominated points-more readily (13%) than non-model-users (2.5%) and model users discovered the win-win nature of electricity access with higher frequency (63%) than non-model users (9%). Participants who learned of the model's logic through presentation performed nearly as well as model users. In the second study, model use shortened the (mean) duration of the negotiation from 55 minutes to 45 minutes. Negotiating tables that co-created a model had a higher likelihood of reaching favorable agreements (44% compared to 25%). Model use did not significantly alter the value distribution among parties. Tables of negotiators used the model in two predominant manners: to test alternatives as they generated potential agreements and to verify a tentative agreement. The former resulted in higher mean table values than the latter. Together, these studies demonstrate: that mathematical models can be used in sustainability negotiations and decisions with good effect; that learning about the insights of a model is beneficial in decision making-but using a model is more beneficial; and that collaborative model building can provide better negotiation outcomes than using an expert model and can be faster than not using a model. / by Ellen Czaika. / Ph. D.

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 151-163). / Water supply infrastructure planning faces many uncertainties. Uncertainty in short-term in rainfall and runoff, groundwater storage, and long-term climate change impacts water supply forecasts. Population and economic growth drive urban water demand growth at rapid but uncertain rates. Overbuilding infrastructure can lead to expensive stranded assets and unnecessary environmental impacts, while under building can cause reliability outages with impacts on the economy, ecosystems, and human health. This dissertation assesses the potential for Bayesian learning about uncertainty to enable flexible, adaptive approaches in which infrastructure can be changed over time to reduce cost risk while achieving reliability targets. It develops a novel planning framework that: 1) classifies uncertainties and applies appropriate, differentiated uncertainty analysis tools, 2) applies Bayesian inference to physical models of hydrology and climate to develop dynamic uncertainty estimates, and 3) uses stochastic dynamic programming and engineering options analysis to assess the value of flexibility in mitigating cost and reliability risk. This framework is applied to three applications. Chapter 3 evaluates the potential for modular desalination design to manage multiple, diverse uncertainties -- streamflow, demand growth, and the cost of water shortages -- in Melbourne, Australia. Chapter 4 addresses uncertainty in groundwater resources in desalination planning in Riyadh, Saudi Arabia, and Chapter 5 addresses model uncertainty in climate change projections in a dam design problem in Mombasa, Kenya. Across all three applications, we find value in flexible infrastructure planning with a 9-28% reduction in expected cost. However, the performance of flexible approaches compared to traditional robust approaches varies considerably and is influenced by technology choice, economies of scale, discounting, the presence of irreducible stochastic variability, and the value society places on water reliability. / by Sarah Marie Fletcher. / Ph. D. in Engineering Systems

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 168-171). / Space is one of the most scarce, expensive, and difficult to manage resources in urban retail establishments. A typical retail space broadly consists of two areas, the customer facing frontroom area and the backroom area, which is used for inventory storage and other support activities. While frontrooms have received considerable amount of attention from both academics and practitioners, backrooms are an often neglected area of retail space management and design. However, the allocation of space to the backroom and its management impact multiple operational aspects of retail establishments. These include in-store labor utilization, delivery schedules, product packaging, and inventory management. Therefore, the backroom area directly affects the performance of the store because it impacts stock-outs, customer service levels, and labor productivity. Moreover, extant literature suggests that backroom related operations contribute to a large fraction of the total retail supply chain costs. Thus, optimizing the management of backroom spaces is an important lever for store performance improvement. We address the gap in the extant literature related to space management of retail backrooms by investigating the following three questions: First, what is the effect of pack size on inventory levels and space needs in the backroom? Second, how can a given backroom space be efficiently utilized through optimal inventory control? Third, what is the optimal amount of space that should be allocated to the backroom in a given retail establishment? To address the first question, we evaluate the effect of two discrete pack sizes, order pack size (OPS) and storable pack size (SPS), on inventory levels and storage space requirements in the backrooms. While SPS drives the space needs for a given inventory level, OPS drives the amount of excess inventory and therefore, the space needs. Using inventory theory and probability theory, we quantify the amount of excess inventory and the expected stock-out probability for a given OPS in the case of a normally distributed demand. To address the second question, we discuss an inventory-theoretic approach to efficiently manage a given backroom space within a limited service restaurant. Specifically, we formulate a mathematical optimization model using mixed-integer linear programing with the objective of maximizing store profit. Applying this optimization model to real store data in collaboration with a major US retailer reveals cost implications related to constrained backroom space and the sensitivity of backroom space requirements to changes in OPS and SPS. The proposed model can serve as a decision support tool for various real-world use cases. For instance, the tool can help the retailers to identify (i) items whose contribution to the store profit does not justify their space needs in the backroom, and (ii) stores that are constrained in their profitability growth by backroom space limitations. To address the third question, we introduce the notion of interdependency between the frontroom and the backroom of a retail establishment. Such interdependencies yield nontrivial trade-offs inherent to the optimal retail space allocation. Demand can be lost due to unavailability of inventory (or inventory stock-out), which is a result of scarce amount of backroom space, or due to unavailability of sufficient frontroom space (or space stock-out). Furthermore, constrained backroom spaces increase in-store labor cost and the ordering costs incurred per unit of revenue generated in a retail establishment. The strategic decision model formulated in this chapter accounts for revenue, inventory cost, labor cost and ordering cost to determine the optimal amount of backroom space that should be allocated within a retail establishment. Sensitivity analyses with respect to the change in input parameters is used to connect the backroom space allocation and its impact on store profit to the different supply chain levers that can be managed by the retailers. / by Lita Das. / Ph. D. in Engineering Systems

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / Cataloged from PDF version of thesis. "Some pages in the original document contain text that runs off the edge of the page"--Disclaimer Notice page. / Includes bibliographical references. / China's rapid economic growth in the twenty-first century has driven, and been driven by, concomitant motorization and growth of passenger and freight mobility, leading to greater energy demand and environmental impacts. In this dissertation I develop methods to characterize the evolution of passenger transport demand in a rapidly-developing country, in order to support projection and policy assessment. In Essay #1, I study the role that vehicle tailpipe and fuel quality standards ("emissions standards") can play vis-à-vis economy-wide carbon pricing in reducing emissions of pollutants that lead to poor air quality. I extend a global, computable general equilibrium (CGE) model resolving 30 Chinese provinces by separating freight and passenger transport subsectors, road and non-road modes, and household-owned vehicles; and then linking energy demand in these subsectors to a province-level inventory of primary pollutant emissions and future policy targets. While climate policy yields an air quality co-benefit by inducing shifts away from dirtier fuels, this effect is weak within the transport sector. Current emissions standards can drastically reduce transportation emissions, but their overall impact is limited by transport's share in total emissions, which varies across provinces. I conclude that the two categories of measures examined are complementary, and the effectiveness of emissions standards relies on enforcement in removing older, higher-polluting vehicles from the roads. In Essay #2, I characterize Chinese households' demand for transport by estimating the recently-developed, Exact affine Stone index (EASI) demand system on publicly-available data from non-governmental, social surveys. Flexible, EASI demands are particularly useful in China's rapidly-changing economy and transport system, because they capture ways that income elasticities of demand, and household transport budgets, vary with incomes; with population and road network densities; and with the supply of alternative transport modes. I find transport demand to be highly elastic ([epsilon][subscript x] = 1.46) at low incomes, and that income-elasticity of demand declines but remains greater than unity as incomes rise, so that the share of transport in households' spending rises monotonically from 1.6 % to 7.5 %; a wider, yet lower range than in some previous estimates. While no strong effects of city-level factors are identified, these and other non-income effects account for a larger portion of budget share changes than rising incomes. Finally, in Essay #3, I evaluate the predictive performance of the EASI demand system, by testing the sensitivity of model fit to the data available for estimation, in comparison with the less flexible, but widely used, Almost Ideal demand system (AIDS). In rapidly-evolving countries such as China, survey data without nationwide coverage can be used to characterize transport systems, but the omission of cities and provinces could bias results. To examine this possibility, I estimate demand systems on data subsets and test their predictions against observations for the withheld fraction. I find that simple EASI specifications slightly outperform AIDS under cross-validation; these offer a ready replacement in standalone and CGE applications. However, a trade-off exists between accuracy and the inclusion of policy-relevant covariates when data omit areas with high values of these variables. Also, while province-level fixed-effects control for unobserved heterogeneity across units that may bias parameter estimates, they increase prediction error in out-of-sample applications-revealing that the influence of local conditions on household transport expenditure varies significantly across China's provinces. The results motivate targeted transport data collection that better spans variation on city types and attributes; and the validation technique aids transport modelers in designing and validating demand specifications for projection and assessment. / by Paul Natsuo Kishimoto. / Ph. D. in Engineering Systems

Thesis: Ph. D. Engineering Systems: Human-Systems Engineering, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 146-152). / Current approaches to nuclear security can produce elegantly designed physical protection systems (PPS) that may be limited by untenable assumptions or well stated-albeit vague and imprecise-descriptions of how to improve nuclear security culture itself. According to one nuclear security culture expert: While the International Atomic Energy Agency has released methodologies on evaluating vulnerabilities and physical protection, it has not yet introduced guidelines on assessing the human-factor in detection, delay, and response. (Khripunov, 2014, pp. 39-40) (Emphasis added) This dissertation argues that such a link lies in understanding how organizational influences affect the completion of tasks required for PPS to meet expected nuclear security performance goals. In this dissertation, I propose the System-Theoretic Framework for Security (the STFS) for evaluating system-level interactions between PPS and human/organizational behaviors to describe overall security performance. / Invoking key tenets of systems theory and organization science, the STFS uses the concept of "security task completion" to explain how the interactions between PPS and human/organizational behaviors result in security performance at nuclear facilities. Yet, empirical data is needed to explore the efficacy of this approach for incorporating organizational influences into security performance. As such, my research objectives were to: 1. Improve the understanding of how PPS and human/organizational behaviors interact to produce security performance at nuclear facilities, 2. Identify a manageable (but not exhaustive) set of organizational influences on this interaction, and 3. Develop a framework for assessing these interactions and organizational influences on security performance at nuclear facilities. I used a mixed methods research design to develop the STFS. / My first study consisted of 18 narrative interviews across different areas of nuclear security expertise and my second study examined the case of the 2012 security incident at the Y-12 National Security Complex. These two studies provided evidence for the security task completion construct (as a new causal mechanism), behavioral performance requirements (assumptions on which the causal mechanism is based), a set of organizational influences and quality indicators related to nuclear security performance. While this framework does not address every aspect of achieving high security performance, the STFS offers a structured thought process and direction for further development regarding how technologies and organizations interact to affect individual behaviors that contribute to security at nuclear facilities. / by Adam D. Williams. / Ph. D. Engineering Systems: Human-Systems Engineering / Ph.D.EngineeringSystems:Human-SystemsEngineering Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society