Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, 2012. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 101-105). / Increasingly, the world we live in is digital, mobile, and online. As a consequence, many of your seemingly mundane actions are recorded, archived, and for the first time widely accessible to both the generators and curators of this information. From this fire hose of digital breadcrumbs, we can learn an enormous amount about ourselves as individuals and societies. Simple questions such as where we go, who we are meeting, and how we interact when we get there can be explored with incredibly high resolution and richness. Through new emiprical and analytic tools, we can leverage information generated from rapidly expanding online social networks, revealing the beautiful and often surprising complexity of everyday human behavior. We are able to harness data from millions of cell phone users to better understand how people move through cities, use roads, and interact with their neighbors. This thesis deals with quantifying, analyzing, and ultimately modeling sociotechnical systems. More specifically, it focuses on modeling the diffusion of innovations in time and space. While there has been much work examining the affects of social network structure on innovation adoption, models to date have lacked important features such as meta-populations reflecting real geography or influence from mass media forces. This thesis shows that these are features crucial to producing more accurate predictions of a social contagion and technology adoption at the city level. Using data from the adoption of the popular micro-blogging platform, Twitter, a model of adoption on a network is presented. The model places friendships in real geographic space and exposes individuals to mass media influence. Results show that homophily both amongst individuals with similar propensities to adopt a technology and geographic location is critical to reproduce features of real spatiotemporal adoption. Furthermore, estimates suggest that mass media was responsible for increasing Twitter's user base two to four fold. To reflect this strength, traditional contagion models are extended to include an endogenous mass media agent that responds to those adopting an innovation as well as influencing agents to adopt themselves. The final chapter of this thesis addresses the future. The ubiquity of digital devices like mobile phones and tablets is opening rich new avenues of research. The massive amounts of data generated and stored by these devices can be used to gain a better understanding of the complex socio-technical systems they sense. The same tools, techniques, and analogies utilized in the first three chapters of this thesis can now literally be taken to the streets. With mobile phones that record when and where activities take place, a new window has been opened on urban systems. Future work will explore how people use cities dynamically to improve transportations systems and inform urban planners. New measurements will help understand what cities do well, when they fail, and why. At the core of this new domain, is an interdisciplinary approach to complex socio-technical systems that combines many fields and methods. This view forms a more holistic view of problems and potential solutions. The thesis presented stands as an example of data, theory, and simulation for diverse areas can be combined to gain novel insights into human behavior. / by Jameson Lawrence Toole. / S.M.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013. / 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 169-183). / The electric power sector, which accounts for approximately 40% of U.S. carbon dioxide emissions, will be a critical component of any policy the U.S. government pursues to confront climate change. In the context of uncertainty in future policy limiting emissions and future technology costs, society faces the following question: What should the electricity mix we build in the next decade look like? We can continue to focus on conventional generation or invest in low-carbon technologies. There is no obvious answer without explicitly considering the risks created by uncertainty. This research investigates socially optimal near-term electricity investment decisions under uncertainty in future policy and technology costs. It employs a novel framework that models decision-making under uncertainty with learning in an economy-wide setting that can measure social welfare impacts. Specifically, a computable general equilibrium (CGE) model is formulated as a two-stage stochastic dynamic program focused on decisions in the electric power sector. The new model is applied to investigate a number of factors affecting optimal near-term electricity investments: (1) policy uncertainty, (2) expansion rate limits on low-carbon generation, (3) low-carbon technology cost uncertainty, (4) technological learning (i.e., near-term investment lowers the expected future technology cost), and (5) the inclusion of a safety valve in future policy which allows the emissions cap to be exceeded, but at a cost. In modeling decision-making under uncertainty, an optimal electricity investment hedging strategy is identified. Given the experimental design, the optimal hedging strategy reduces the expected policy costs by over 50% compared to a strategy derived using the expected value for the uncertain parameter; and by 12-400% compared to strategies developed under a perfect foresight or myopic framework. This research also shows that uncertainty has a cost, beyond the cost of meeting a policy. In the experimental design used here, uncertainty in the future policy increases the expected cost of policy by over 45%. If political consensus can be reached and the climate science uncertainties resolved, setting clear, long-term policies can minimize expected policy costs. In addition, this work contributes to the learning-by-doing literature by presenting a stochastic formulation of technological learning in which near-term investments in a technology affect the probability distribution of the future cost of that technology. Results using this formulation demonstrate that learning rates lower than those found in the literature can lead to significant additional near-term investment in low-carbon technology in order to lower the expected future cost of the technology in case a stringent policy is adopted.Ultimately, this dissertation demonstrates that near-term investments in low-carbon technologies should be greater than what would be justified to meet near-term goals alone. Near-term low-carbon investments can lower the expected cost of future policy by developing a less carbon-intensive electricity mix, spreading the burden of emissions reductions over time, helping to overcome technology expansion rate constraints, and reducing the expected future cost of low-carbon technologies-all of which provide future flexibility in meeting a policy. The additional near-term cost of low-carbon investments is justified by the future flexibility that such investments create. The value of this flexibility is only explicitly considered in the context of decision-making under uncertainty. / by Jennifer Faye Morris. / Ph.D.

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 82-83). / Microelectronic devices traditionally were manufactured by companies that both designed and produced integrated chips. This process was important in 1970's and 1980's when the manufacturing processes required tweaking the design, understanding of the manufacturing processes and occasional need to redesign. As manufacturing techniques and standards evolved, companies have changed their business model and have started to outsource their manufacturing to merchant foundries. Semiconductor companies have also started to outsource the design and verification of their chips to third party design service companies and focus on core competence like research and development of new technologies and defining protocols. This trend has evolved even though the chips have become much more complex, hard to design and hard to manufacture. This thesis studies the different players in the supply chain, how each player has evolved and the challenges companies face in making decisions regarding outsourcing internal processes. It was found that the advancements in the downstream industries such as EDA, Design Suppliers and EMS have helped fabless companies remain competitive with IDM's (Integrated Device Manufacturers). The fabless companies compete in different markets that do not need the most advanced processing technologies used by leading-edge companies. / by Karthikeyan Malli Mohan. / S.M.in Engineering and Management

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2005. / Includes bibliographical references (leaf 70). / This thesis discusses a network design problem based on a case study with a footwear company, which intends to minimize total supply chain costs by establishing a distribution network which bypasses its primary distribution center (DC). Through the new network, called the DC bypass network, the company ships products directly from its Asian factories to a logistics hub at an entry port in the US and then on to customers, a particular group of chosen customers. We assess the project by comparing costs derived from a baseline and optimization model. A baseline model represents the company's existing logistics network while optimization models capture future supply chains with different scenarios. The models convert a real supply chain network into the relationships between nodes and links. Nodes indicate facilities while links refer to the flow of the product. In brief, this case study is about how a company evaluates its transportation network. Methods to determine a specific location or multiple locations for the DC bypass operations are discussed. Furthermore, the robustness of an optimal solution will be measured through a sensitivity analysis. Other benefits include the reduction of lead time is discussed in the further research. / by Yu-Yen Chiu. / M.Eng.in Logistics

Thesis (M.Eng.)--Massachusetts Institute of Technology, Engineering Systems Division, 2000. / Includes bibliographical references (leaf 60). / With expansion of commerce and boundaries of business, organizations have been working hard at improving processes and bills of material and have reached the bottom already. The focus is therefore, now shifting to the next logical area of optimization of supply chains. The ever-increasing competition is putting pressure on organizations to optimize their supply chains and many organizations are reevaluating their existing supply chain networks. Often, as expected, they realize that it requires a complete overhauling. There may be too many suppliers or too many distribution centers, not quite optimally aligned in the chain. The important issue is not to impact customer service adversely, and yet, make the desired changes in the network. A member company from the Affiliates Program at MIT's Center for Transportation Studies is one such company, looking at re-configuring their distribution system, questioning the need for multiple echelons in their distribution system. They are looking at reducing the number of Delivery Center locations and the possibility of doing away with the Central Distribution Centers where shipments from the plants are consolidated for shipment to the DCs. This study aims to address the following related issues: What is the impact of reducing the number of DCs? What would be the optimal location of a third DC assuming 2 DCs are known? What would be the customer allocations in the new network with 3 DCs? How would the assignments change if there was a capacity constraint posed on the DCs? These issues were approached as a facility location problem with an objective function to meet the customer demand at the minimum cost. In a typical system, the constituents of this cost would be the transportation cost - Plant to DC to Customer, facility operating cost and the cost of carrying inventory at each DC. The number, location and size of the DCs relative to the plants and customer zones would be some of the decision variables that influence these costs. The study was conducted by structuring the company data from the previous year into a model and using a mixed integer linear programming tool to arrive at the optimal solution. SAILS - ODS, a supply chain network optimization software, was used as the solver for the network model. For the purpose of this thesis, the analysis was limited to a study of the transportation costs as the driver for optimization. / by Rajiv Agarwal. / M.Eng.

Thesis (M.Eng.)--Massachusetts Institute of Technology, Engineering Systems Division, 2000. / Includes bibliographical references (leaves 52-54). / Business-to-business (B2B) marketplaces bring together buyers and sellers in different industries using the Internet to conduct or facilitate business transactions. Among these new intermediaries or "infomediaries" are several firms that address spot market transactions and long-term contract negotiations for truckload, airfreight, ocean and intermodal shipments. Most of the initial activity in freight transportation has focused on the highly fragmented truckload sector. Currently, none of these firms process enough shipments to constitute critical mass or a self-sustaining business model. Without liquidity, B2B marketplaces that rely solely on an exchange cannot present a viable alternative to existing transportation intermediaries, such as brokers and forwarders, since shippers' orders cannot be frequently matched with carriers' capacity. Channel mix and domain expertise are the critical strategic mobility barriers for B2B marketplaces. Firms must make strategic decisions early about whether to include or exclude existing intermediaries and also how carriers' direct sales forces may be displaced. The service offering must either reinforce or replace the basic functions of intermediaries. Technology leadership in applications critical to shippers (e.g., shipment consolidation, mode selection and combinatorial bidding) is a proxy for domain expertise and will largely determine a company's ability to differentiate its offerings and form a broad versus narrow line. Shippers will receive the greatest benefit from B2B marketplaces and Internet-based transportation management systems present the best opportunity for value creation. This research examines indirect channels for freight transportation and the specific functions performed by existing intermediaries. Trading models are categorized and four case studies of truckload marketplaces are presented. Frameworks are provided for channel structure and strategic groupings. / by Marc D. Boyle. / M.Eng.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2005. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Includes bibliographical references (leaves 164-168). / (cont.) To demonstrate its practical value in large-scale engineering systems, the research applied OPN to two space exploration programs and one aircraft design problem. In our experiments, OPN was able to significantly change the modeling and architectural reasoning process by automating a number of manual model construction, manipulation, and simulation tasks. / The aim of this research is to design an executable meta-language that supports system architects' modeling process by automating certain model construction, manipulation and simulation tasks. This language specifically addresses the needs in systematically communicating architects' intent with a wide range of stakeholders and to organize knowledge from various domains. Our investigation into existing architecting approaches and technologies has pointed out the need to develop a simple and intuitive, yet formal language, that expresses multiple layers of abstractions, provides reflexive knowledge about the models, mechanizes data exchange and manipulation, while allowing integration with legacy infrastructures. A small set of linguistic primitives, stateful objects and processes that transform them were identified as both required and sufficient building blocks of the meta-language, specified as an Object-Process Network (OPN). To demonstrate the applicability of OPN, a software environment has been developed and applied to define meta-models of large-scale complex system architectures such as space transportation systems. OPN provides three supporting aspects of architectural modeling. As a declarative language, OPN provides a diagrammatic formal language to help architects specify the space of architectural options. As an imperative language, OPN automates the process of creating architectural option instances and computes associated performance metrics for those instances. As a simulation language, OPN uses a function-algebraic model to subsume and compose discrete, continuous, and probabilistic events within one unified execution engine. / by Hsueh-Yung Benjamin Koo. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2009. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 191-197). / Large-scale, complex engineering systems, as for automotive manufacturing, often require significant capital investment and resources for systems configuration. Furthermore, these systems operate in environments that are constantly changing due to shifts in macroeconomic, market demand and regulations, which can significantly influence systems' performance. It is often very difficult or prohibitively expensive to change these engineering systems once they are in place. Thus, a critical question is how to design engineering systems so they can perform well under uncertainty. Conventional engineering practice often focuses on the expected value of future uncertainties, thus leaving the value of flexible designs unexplored. This research develops a new framework to design and plan large-scale and complex manufacturing systems for uncertainty. It couples a screening model to identify promising candidate solutions with an evaluation model to more extensively quantify the performance of identified solutions. The screening model adaptively explores a large decision space that is otherwise computationally intractable for conventional optimization approach. It integrates strategic and operational flexibility in a system to allow systematic consideration of multiple sources of flexibility with uncertainty. It provides a means to search the space for system's improvement by integrating the adaptive one-factor-at-a-time (OFAT) method with a Response Surface method and simulation-based linear optimization. The identified solution is then examined with Value at Risk and Gain chart and a statistics table. / (cont.) Two cases are studied in this thesis. The first case is a simple hypothetical case with two products and two plants. It considers product to plant allocation, plant capacity, and overtime operation decisions that affect manufacturing flexibility. It demonstrates the value of considering demand uncertainty and overtime operational flexibility in making manufacturing planning decisions and the interactions between multiple sources of flexibility. The second case explores these manufacturing planning decisions for Body-In-White assembly systems in the automotive industry by applying the developed screening model. It shows that the screening model leads to system design with about 40% improvement in expected net present value, reduced downside risks and increased upside gains as compared to a traditional optimization approach. / by Yingxia Yang. / Ph.D.

Thesis: S.M., Massachusetts Institute of Technology, Engineering Systems Division, 2015. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 58-61). / This work develops a systemic approach for enabling transshipment operations in congested urban areas, based on two fundamental city logistics needs: efficient utilization of existing infrastructure and flexibility for logistics operators. Specifically, this research introduces the concept of urban transshipment networks (UTNs), a collection of strategically located urban logistics spaces, for efficient and flexible last-mile delivery operations in congested urban areas. By implementing the UTN framework, logistics operators can select the transshipment locations, vehicle types and operating schedules that best fit specific distribution strategies, and, simultaneously, comply with access restrictions and overcome some of the logistics complexity of dense urban zones. This concept is particularly relevant for retail dynamics observed in large metropolitan areas in the emerging world. A two-echelon location-routing model formulation is proposed to address the UTN design problem. The formulation combines a mixed-integer programing model with a closed-form routing cost approximation. The model was tested through a consumer goods distribution case study in Latin America. Results suggest that, given proper fleet type and capacity, UTNs can significantly improve delivery process efficiency in highly congested districts. / by Daniel Esteban Merchán Dueñas. / S.M.

<p> Enterprise architecture frameworks (EAFs) have been used to plan and manage large-scale enterprise deployments for more than four decades. EAFs are important tools used by systems engineers and are integral to characterize enterprise information architectures. They are increasingly being used as a proxy for managing entire organizations &ndash; enterprises. Enterprises represent complex, multi-disciplinary, socio-technical systems. They are ubiquitous, and involve and affect a vast number of humans every day. However, as inter-disciplinary tools for the management of the enterprise, there are certain limitations to the efficacy of existing enterprise architecture frameworks. The effective management of enterprises presents significant challenge and opportunity for the systems engineering community. This research discusses the limitations of, and proposes enhancements to, existing EAFs, based on research into extant business management frameworks. An historical perspective is provided on both systems engineering and business enterprise domain frameworks. Research into the common elements of successful business management frameworks confirms the limitations of existing systems engineering frameworks and suggests key additions for enhanced efficacy. The applicability and relevance of enhancing extant enterprise architectures with elements from extant business frameworks is examined. Finally, recommendations are made for enhancements to extant frameworks and suggestions advanced on future research into efficacy. This dissertation concludes with implications of these findings for systems engineers engaged in enterprise architecture and enterprise transformation efforts and a recommendation that systems engineers take a more holistic approach in their enterprise architecture and enterprise transformation efforts.</p>