Ashuri, Baabak

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Now showing 1 - 4 of 4
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    Decision Analytics in Design and Construction
    (Georgia Institute of Technology, 2019-09-26) Aflatoony, Leila ; Ashuri, Baabak ; Bartlett, Chris M. ; Rakha, Tarek
    Decision analytics stands to have a profound impact on how design and construction disciplines are woven together to solve today's most complex problems. Rigorous data collection and analysis are core to design and construction decision making. The nature of analysis is to study complexity and deduce a reasonable summary that will then inform design and construction decisions. Decision analytics is distinguished from analysis by the emphasis on causality and prediction. The proliferation of computing power and access to rich data sets has driven innovation in the analytics tools market, lowering the barrier for entry to powerful analytics tools for designers and constructors. This means that decision-makers can more accurately identify causality and leverage the predictive power of analytics to inform design and construction decisions that anticipate and solve for problems much further into the future. Opportunities are growing to align decision analytics across multiple disciplines to minimize economic waste, maximize energy efficiencies, and enhance the lives of individuals and communities. An intuitive example of this opportunity lies in new building design and construction. Construction Analytics is a distinctive discipline, bridging the fields of building construction, civil and environmental engineering, economics, and operations research. Designers and decision-makers use descriptive analytics to identify indicators to cost overruns, diagnostic analytics to predict construction market resiliency after natural disasters, predictive analytics to identify future building trends, and prescriptive analytics to optimize resource allocation during construction projects. Building performance analytics explores various performance measures linked to building energy investigations, including measuring existing building performance through detailed audits to achieve substantial energy savings in deteriorating infrastructures, as well as simulating and visualizing new building and urban energy-flows to formulate informed design decisions empowered by data analytics for a sustainable and energy efficient future. In the example of new hospital construction, human-centered analytics can produce powerful insights and unlock empathy for the people (pediatric doctors, nurses, patients) who actively use the hospital space. Merging and visualizing several sources of quantitative and qualitative data draws out causality and enables predictive decision making aimed at improving the experience and performance of the people using the space.
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    GT Roundtable 2: Food-Energy-Water Nexus and Social Sustainability
    ( 2016-06-09) Ashuri, Baabak ; Bolling, Bill ; Webb Girard, Amy ; King, Carey W. ; Realff, Matthew J. ; Smith, Joseph D.
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    Pricing Flexibility in Solar Ready Homes
    (Georgia Institute of Technology, 2011-11-17) Ashuri, Baabak ; Irizarry, Javier ; Riether, Gernot
    Solar energy technologies, i.e., Photovoltaic (PV) panels, have promising features for renewable energy generation (i.e., energy savings) and greenhouse gas (GHG) emission reduction in the housing sector. Nevertheless, adopting these PV technologies requires substantial initial investments. The market for these technologies is often vibrant from the technological and economic standpoints. Therefore, investors typically find it more attractive to delay investment in PV technologies. Alternatively "Solar Ready Homes" are proposed. These flexible homes can easily adopt PV technologies later in future when the price of PV panels is lower, electricity energy price is higher, and stricter environmental regulations are in place. The investors need proper financial valuation models in order to avoid over- and under-investment in solar technologies. We apply Real Options Theory to evaluate investments in Solar Ready Homes. Our proposed investment analysis framework uses a probability distribution model to empirically characterize uncertainty about the performance of PV panels. Uncertainty about future retail price of energy is characterized with a stochastic model. Our framework uses the experience curve to model changes in price and efficiency of PV technologies over time. Our investment valuation model identifies the optimal time to install PV panels in Solar Ready Homes. Our valuation model characterizes the investor's financial risk profile in two investments: "fixed" Solar Home and "flexible" Solar Ready Home. The optimal time for installing PV panels in solar ready home is identified. Our framework determines the price of flexibility embedded in solar ready home. In other words, it calculates the difference between the expected value of investment in solar ready home and the expected value of investment in solar home. (Ashuri)
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    A Real Options Approach to Modeling Investments in Competitive, Dynamic Retail Markets
    (Georgia Institute of Technology, 2008-06-12) Ashuri, Baabak
    The retail industry is considered to be a very competitive industry in the United States since there are so many players in the almost saturated retail markets that provide similar products and services at similar price levels to customers. Market selection has been identified as an important strategy to differentiate a retailer in this competitive market. Therefore in this thesis, we describe a conceptual framework to evaluate retailers investment opportunities in dynamic, competitive retail markets. The objective is to describe a conceptual investment analysis framework to address the strategic aspects of a retailer s investment opportunity as well as the dynamic uncertainty of a retail market in a single framework. This conceptual framework outlines a strategic view towards retail stores as flexible assets of a retail enterprise. This conceptual framework is general and can be adjusted and applied to investments options in other services. In addition, we develop an integrated investment analysis approach based on dynamic programming to explore retailers investment behaviors in dynamic markets. The objective is to determine retailers optimal investment thresholds in noncompetitive and competitive markets. We consider two retailers to illustrate our approach and use a simple game theory treatment to address competition in retail markets. We use our integrated investment analysis model based on a real options methodology to evaluate the apparent tendency for the small discount retailer invests earlier in a new developing market due to the competition effect from the large discount retailer. This early entry gives the small retail a first-mover advantage and delays the big retailer s entry into the competitive market. In addition, we conduct sensitivity analysis to characterize how significantly the values of our model parameters impact the retailers investment decisions. We also develop an integrated investment analysis approach based on contingent claims analysis to explore retailers investment behaviors in dynamic markets. The objective is to determine retailers optimal investment thresholds in noncompetitive and competitive markets. The equivalent risk neutral evaluation approach is presented in this thesis as an extended version of the contingent claims analysis approach, which facilitates the market-oriented valuation of the retailer s investment option in dynamic markets. Sensitivity analysis is conducted to study how retailers optimal investment thresholds change as the values of parameters in this equivalent risk neutral evaluation approach change. The relationship between the dynamic programming and the equivalent risk neutral evaluation approach is also summarized in this thesis to identify the similarities and the differences between these two investment analysis approaches. One of the most important objectives of this comparison is to determine in what market conditions the choice of investment analysis approach is critical and dramatically changes the retailer s optimal investment threshold. Finally, we empirically examine an important aspect of our theoretical work that the big retailer invests and opens a store relatively later in markets with a small retailer compared to markets without a small retailer. In addition, the big retailer opens a store at relatively higher retail market potential in markets with a small retailer compared to markets without a small retailer. In this thesis, we discuss some empirical evidence to support these theoretical results. We chose Wal-Mart and Dollar General as the big and small retailers, respectively, in our empirical study. Our empirical results do not validate the theory and just provide supporting evidence for our theoretical works.