Engineering Systems Division

Deregulation shaping the Electricity industry across the world is a systems challenge cutting across interdisciplinary fields of technology, economics, public policy, environment and sociology. Decision makers that shape tomorrow's policy and investors that invest in financial and technological developments in this industry need to rely on multiple decision models to make informed decisions. This thesis serves to provide one such decision model among many that could be used to understand the key dynamics shaping a highly complex industry. We employ "top-down" and "bottom-up" approaches to build system dynamics model in an attempt to distinguish between adoption and diffusion phenomenon, as a result benefiting from hybrid modeling techniques that combine structures from both models. The models are evaluated with wide range of scenarios to arrive at policy guidance and business model recommendations. The dynamic hypothesis arising from our system dynamics model...

Improvement of business performance is an enduring subject of research across a diverse set of disciplines such as accounting, operations management, marketing, finance, economics and even psychology and sociology. In any discipline the ability to create self-awareness, a capacity to evaluate and compare their own behavior to internal standards and values, is a pre-requisite of improvement. In an organization, selfawareness is created with assessments. The results of assessments can help the organization to adapt to changing market conditions by defining more valuable objectives to direct its new businesses as well as to restructure its operations for eliminating deficiencies that limit its abilities to achieve its business goals at lower costs. For an organization with core businesses in product development (PD), assessment of PD is an essential element of all business improvement initiatives. However, in order to perform a focused PD assessment, the analysis needs to be structured...

Improvement of business performance is an enduring subject of research across a diverse set of disciplines such as accounting, operations management, marketing, finance, economics and even psychology and sociology. In any discipline the ability to create self-awareness, a capacity to evaluate and compare their own behavior to internal standards and values, is a pre-requisite of improvement. In an organization, selfawareness is created with assessments. The results of assessments can help the organization to adapt to changing market conditions by defining more valuable objectives to direct its new businesses as well as to restructure its operations for eliminating deficiencies that limit its abilities to achieve its business goals at lower costs. For an organization with core businesses in product development (PD), assessment of PD is an essential element of all business improvement initiatives. However, in order to perform a focused PD assessment, the analysis needs to be structured...

This thesis aims to assess the activities and influence of automotive multinational corporations (MNCs) in developing countries as it relates to the concept of "total sustainability" within three countries: Argentina, Brazil, and Mexico. It is an innovative perspective of the systemic sustainability issues incorporated incorporate strategy, industrial policy, worker representation, and the environmental protection.

Research has focused on collecting information from journals, industry publications, and studies by international organization, on the interplay between government policy and automotive MNC activity. Focus of attention has been paid to influences on the "Pillars of Sustainability", described by Professor Nicholas Ashford, that encompass sustainable development: Environment, Economy, and Employment.

Analysis of the observations and industry/policy trends has used the Ashford framework, which focuses on both the above mentioned factors of total sustainability and the many processes that interconnect their states. Attention is also paid to emergent complex system behavior and associated risks and opportunities.

Conclusions and Recommendations are focused on systemic views of the challenges posed by automotive industry activity on the nations studied and policy recommendations on how to possibly both capture economic benefit, but also further
sustainable development efforts.

Suggested future research topics associated with this thesis would encompass the analysis of different industries, entrepreneurial enterprises, industrial policies, technologies and policies, or the development of associated System Dynamics models.

Project Poster - Somali High Seas Piracy Resolution: An Application of Systems Engineering to International Policy

The Republic of Somalia has been in a state of perpetual civil war since 1991. There is a lack of an authoritative centralized government to prevent piracy escalation.

This systems engineering project focused on designing a systems solution to eliminate open seas piracy in the African Horn region.
- TO: Permanently eliminate piracy in the African Horn region.
- BY: Neutralizing existing threats, preventing imminent threats, sustaining safe passage (minimizing human casualties and optimizing stakeholder value)
- USING: International naval resources and Somali political and economic stabilization.

The Systems Engineering V-Model was used as an adaptable framework for use by different organizations in a wide variety of product development projects:
- System Dynamics to identify stakeholder needs, problem context, and objectives.
- Object Process Methodology (OPM) models to architect requirements.
- Quality Function Deployment (QFD) to prioritize customer requirements.
- Pugh concept selection to iteratively determine the best solution concepts.
- Cost benefit and risk analysis to determine best solution implementation.
- Verification and risk mitigation to verify solution design to requirements and maximize robustness.

Identified short-term policies included:
- Adoption of a safety corridor U.N. resolution and naval warship schemes within 1 year.
- Establishment of a U.N. coalition to provide ships and crew for schemes.
- Publication of safety corridor for commercial vessels.
- Establishment of a naval coalition active tracking system for safety corridor region.
- Reduction of naval ship coverage per square nautical mile.

Identified long-term policies included:
- Establishment of a U.N. committee to develop economic and infrastructure plan in Bosasso, Somalia.
- Passage of a U.N. resolution to instate Ground Forces in Somalia.
- Promotion of irrigation and agriculture as immediate economic alternative activities to piracy.
- Exploration of natural gas collection and distribution.
- Promote Tourism in Bosasso as a Safe Coastal City

In this report, we present an analysis and recommendations of business strategy for Sentilla Corporation, a small start-­up within the emerging Green IT domain who focuses on data center energy management. We begin with an overview and background of the company, followed by an introduction to their products and primary technology. With this background understanding established, we proceed to discuss lifecycles and transitions affecting this business and the relevant technology, followed by an overview of the innovation trajectory for the technology at the heart of Sentilla's new flagship product. This then leads into a look at diffusion and adoption patterns and what may be expected as their strategy unfolds. Next, we present an analysis of the business ecosystem within which Sentilla operates to understand its competitive and cooperative landscape, as well as trends that will shape its future. This is followed by a consideration of how Sentilla can create and then capture maximum value through strategic options. Finally, we conclude with a number of recommendations and options for Sentilla management to pursue across short, intermediate, and longer time horizons as they attempt to navigate the enterprise through the fast-­paced, high-stakes environment of a technological business.

Much of the research that goes into Big Data, and specifically on Collaborative Big Data, is focused upon questions, such as: • how to get more of it? (e.g., participatory mechanisms, social media, geo-coded data from personal electronic devices) and • how to handle it? (e.g., how to ingest, sort, store, and link up disparate data sets). A question that receives far less attention is that of Collaborative analysis of Big Data; how can a multi- disciplinary layered analysis of Big Data be used to support robust decisions, especially in a collaborative setting, and especially under time pressure? The robust Decision Engineering required can be achieved by employing an approach related to Network Science, that we call Relationship Science. In Relationship Science, our methodological framework, karassian netchain analysis (KNA), is utilized to ascertain islands of stability or positive influence dominating sets (PIDS), so that a form of annealed resiliency or latent stability is achieved, thereby mitigating against unintended consequences, elements of instability, and “perfect storm” crises lurking within the network.

Charles Atencio is a graduate of the Massachusetts Institute of Technology (MIT), earning a Master in Science in Engineering and Management, within the MIT System Design and Management (SDM) Program, sponsored jointly by the MIT School of Engineering, Engineering Systems Division (ESD) and MIT Sloan School of Management. His studies focused on System Dynamics, Corporate Strategy, Sustainability, Public Policy, and Leadership Development at both MIT Sloan School of Management and Harvard Business School (HBS). As part of MIT SDM, Charlie applied System Dynamics and Systems Engineering principles to research the socio-political-economic causal relationships leading to insurgency activity and the expansion of piracy in Somalia.

Wind farm design deals with the optimal placement of turbines in a wind farm. Past studies have focused on energymaximization, cost-minimization or revenue-maximization objectives. As land is more extensively exploited for onshore wind farms, wind farms are more likely to be in close proximity with human dwellings. Therefore governments, developers, and landowners have to be aware of wind farms' environmental impacts. After considering land constraints due to environmental features, noise generation remains the main environmental/health concern for wind farm design. Therefore, noise generation is sometimes included in optimization models as a constraint. Here we present continuous-location models for layout optimization that take noise and energy as objective functions, in order to fully characterize the design and performance spaces of the optimal wind farm layout problem. Based on Jensen's wake model and ISO-9613-2 noise calculations, we used single-and multiobjective genetic algorithms (NSGA-II) to solve the optimization problem. Preliminary results from the biobjective optimization model illustrate the trade-off between energy generation and noise production by identifying several key parts of Pareto frontiers. In addition, comparison of single-objective noise and energy optimization models show that the turbine layouts and the inter-turbine distance distributions are different when considering these objectives individually. The relevance of these results for wind farm layout designers is explored.

The wind farm layout optimization problem is concerned with the optimal location of turbines within a fixed geographical area to maximize energy capture under stochastic wind conditions. Previously it has been modelled as a maximum diversity (or p-dispersion-sum) problem, but such a formulation cannot capture the nonlinearity of aerodynamic interactions among multiple wind turbines. We present the first constraint programming (CP) and mixed integer linear programming (MIP) models that incorporate such nonlinearity. Our empirical results indicate that the relative performance between these two models reverses when the wind scenario changes from a simple to a more complex one. We also propose an improvement to the previous maximum diversity model and demonstrate that the improved model solves more problem instances.

Wind farm layout optimization (WFLO) is the design of wind turbine layout, subject to various financial and engineering objectives and constraints. The first topic of this thesis focuses on solving two variations of WFLO that have different analytical aerodynamic models, and illustrate deep integration of the wake models into mixed-integer programs and constraint programs. Formulating WFLO as MIP and CP enables more quantitative analysis than previous studies could do with heuristics, and allows the practitioners to use an off-the-shelf optimization solver to tackle the WFLO problem. The second topic focuses on another version of WFLO that has two competing objectives: minimization of noise and maximization of energy. A genetic algorithm (NSGA-II) is used. Under these two objectives, solutions are presented to illustrate the flexibility of this optimization framework in terms of supplying a spectrum of design choices with different numbers of turbines and different levels of noise and energy output. ii I am incredibly fortunate to have Professor Cristina Amon as my supervisor, who has provided unfaltering support and guidance through my MASc journey. I am grateful for her welcoming me into the ATOMS lab and allowing me to pursue my academic interests freely. Her work ethics and ability to discover important research questions are inspirational. Since my first day at ATOMS, Dr. David Romero has been a supportive and resourceful mentor. Without David's confidence in me, I would not have appreciated research so much. David's depth and breadth of knowledge, along with his effective supervision style, are instrumental for my research progress. Sitting beside him brought us many interesting discussions from research to culinary art. Professor Timothy Chan has been an academic and career mentor to me through the many courses and projects that we are both part of. Thank you for fostering my passion for operations research and re-kindling my love for mathematics. Discussions with Professor Chris Beck through various research projects and the CP&LS course honed my critical thinking skills, and brought me one step closer to being a good researcher. have taught me many things in research: work ethics, communication, and research collaboration. Michael Kim, unknowingly, inspired part of my work through his award-winning paper. He probably still does not know if he has not read this paragraph. The Hatch team, especially Michael Morgenroth and Joaquin Moran, brought insights from the industry, without which I would not have been able to write this thesis.

This paper presents a three-stage algorithm for resource-aware scheduling of computational jobs in a large-scale heterogeneous data center. The algorithm aims to allocate job classes to machine configurations to attain an efficient mapping between job resource request profiles and machine resource capacity profiles. The first stage uses a queueing model that treats the system in an aggregated manner with pooled machines and jobs represented as a fluid flow. The latter two stages use combinatorial optimization techniques to solve a shorter-term, more accurate representation of the problem using the first stage, long-term solution for heuristic guidance. In the second stage, jobs and machines are discretized. A linear programming model is used to obtain a solution to the discrete problem that maximizes the system capacity given a restriction on the job class and machine configuration pairings based on the solution of the first stage. The final stage is a scheduling policy that uses the solution from the second stage to guide the dispatching of arriving jobs to machines. We present experimental results of our algorithm on both Google workload trace data and generated data and show that it outperforms existing schedulers. These results illustrate the importance of considering heterogeneity of both job and machine configuration profiles in making effective scheduling decisions.

Improvement of business performance is an enduring subject of research across a diverse set of disciplines such as accounting, operations management, marketing, finance, economics and even psychology and sociology. In any discipline the ability to create self-awareness, a capacity to evaluate and compare their own behavior to internal standards and values, is a pre-requisite of improvement. In an organization, selfawareness is created with assessments. The results of assessments can help the organization to adapt to changing market conditions by defining more valuable objectives to direct its new businesses as well as to restructure its operations for eliminating deficiencies that limit its abilities to achieve its business goals at lower costs. For an organization with core businesses in product development (PD), assessment of PD is an essential element of all business improvement initiatives. However, in order to perform a focused PD assessment, the analysis needs to be structured...

Improvement of business performance is an enduring subject of research across a diverse set of disciplines such as accounting, operations management, marketing, finance, economics and even psychology and sociology. In any discipline the ability to create self-awareness, a capacity to evaluate and compare their own behavior to internal standards and values, is a pre-requisite of improvement. In an organization, selfawareness is created with assessments. The results of assessments can help the organization to adapt to changing market conditions by defining more valuable objectives to direct its new businesses as well as to restructure its operations for eliminating deficiencies that limit its abilities to achieve its business goals at lower costs. For an organization with core businesses in product development (PD), assessment of PD is an essential element of all business improvement initiatives. However, in order to perform a focused PD assessment, the analysis needs to be structured...

1] A previous intercomparison of atmospheric mercury models in North America has been extended to compare simulated and observed wet deposition of mercury. Three regional-scale atmospheric mercury models were tested: the Community Multiscale Air Quality (CMAQ) model, the Regional Modeling System for Aerosols and Deposition (REMSAD), and the Trace Element Analysis Model (TEAM). These models were each employed using three sets of lateral boundary conditions to test their sensitivity to intercontinental transport of mercury. The same meteorological and pollutant emission data were used in each simulation. Observations of wet deposition were obtained from the National Atmospheric Deposition Program's Mercury Deposition Network. The regional models can explain 50-70% of the site-to-site variance in annual mercury wet deposition. CMAQ was found to have slightly superior agreement with observations of annual mercury deposition flux in terms of the mean value for all monitoring sites, but REMSAD showed the best correlation when measured by the coefficient of determination (r 2 ). With the exception of one CMAQ simulation, all of the models tended to simulate more wet deposition of mercury than was observed. TEAM exceeded the observed average annual wet deposition by 50% or more in all three of its simulations. CMAQ and REMSAD were better able to reproduce the observed seasonal distribution of mercury wet deposition than was TEAM, but TEAM showed the highest correlation for weekly wet deposition samples. An analysis of model accuracy at each observation site showed no obvious geographic patterns for correlation, bias, or error. Adjusting simulated mercury deposition on the basis of the difference between observed and simulated precipitation data improved the correlation and error scores for all of the models.

The MIT Joint Program on the Science and Policy of Global Change is an organization for research, independent policy analysis, and public education in global environmental change. It seeks to provide leadership in understanding scientific, economic, and ecological aspects of this difficult issue, and combining them into policy assessments that serve the needs of ongoing national and international discussions. To this end, the Program brings together an interdisciplinary group from two established research centers at MIT: the Center for Global Change Science (CGCS) and the Center for Energy and Environmental Policy Research (CEEPR). These two centers bridge many key areas of the needed intellectual work, and additional essential areas are covered by other MIT departments, by collaboration with the Ecosystems Center of the Marine Biology Laboratory (MBL) at Woods Hole, and by short-and long-term visitors to the Program. The Program involves sponsorship and active participation by industry, government, and non-profit organizations.

We assess the human health and economic impacts of projected 2000-2050 changes in ozone pollution using the MIT Emissions Prediction and Policy Analysis-Health Effects (EPPA-HE) model, in combination with results from the GEOS-Chem global tropospheric chemistry model that simulated climate and chemistry effects of IPCC SRES emissions. We use EPPA to assess the human health damages (including acute mortality and morbidity outcomes) caused by ozone pollution and quantify their economic impacts in sixteen world regions. We compare the costs of ozone pollution under scenarios with 2000 and 2050 ozone precursor and greenhouse gas emissions (SRES A1B scenario). We estimate that health costs due to global ozone pollution above pre-industrial levels by 2050 will be $580 billion (year 2000$) and that acute mortalities will exceed 2 million. We find that previous methodologies underestimate costs of air pollution by more than a third because they do not take into account the long-term, compounding effects of health costs. The economic effects of emissions changes far exceed the influence of climate alone.

The MIT Joint Program on the Science and Policy of Global Change is an organization for research, independent policy analysis, and public education in global environmental change. It seeks to provide leadership in understanding scientific, economic, and ecological aspects of this difficult issue, and combining them into policy assessments that serve the needs of ongoing national and international discussions. To this end, the Program brings together an interdisciplinary group from two established research centers at MIT: the Center for Global Change Science (CGCS) and the Center for Energy and Environmental Policy Research (CEEPR). These two centers bridge many key areas of the needed intellectual work, and additional essential areas are covered by other MIT departments, by collaboration with the Ecosystems Center of the Marine Biology Laboratory (MBL) at Woods Hole, and by short-and long-term visitors to the Program. The Program involves sponsorship and active participation by industry, government, and non-profit organizations.