Optimization of emergency service operations

Master'sMASTER OF ENGINEERIN

Sustainable Biofuel Supply Chain Planning and Management under Uncertainty

In this study, the concept of “environmental thinking” is integrated in sustainable biofuel supply chain planning and management and a multi-objective modeling framework is developed. It is aimed to seek best-compromise solutions in achieving economic and environmental sustainability in supplying biofuels from cellulosic biomasses and simultaneously satisfying demand, resource, and technology constraints. In addition to the least-cost objective, greenhouse gas (GHG) emission reduction as another objective is integrated into the modeling framework and carbon footprints will be assessed by using GREET model. This ensures that GHG mitigation strategies are factored in the system planning and management. The biomass-to-biofuel conversion efficiency can be highly uncertain due to the uncertainties inherent in the conversion processes. In this study, the uncertainty will be considered in the integrated modeling framework. A multi-objective, mix-integer stochastic programming model is proposed and solved by the compromise method, a subclass of the goal programming method. The model is used to evaluate the economic potentials and environmental impacts for establishing a biowastes based cellulosic ethanol supply chain in California as a case study. It is found that significant trade-offs exist between economic and environmental benefits, and the uncertainty in biomass-to-biofuel conversion technology has substantial impacts on multi-criteria decision making

A mean-risk mixed integer nonlinear program for transportation network protection

This paper focuses on transportation network protection to hedge against extreme events such as earthquakes. Traditional two-stage stochastic programming has been widely adopted to obtain solutions under a risk-neutral preference through the use of expectations in the recourse function. In reality, decision makers hold different risk preferences. We develop a mean-risk two-stage stochastic programming model that allows for greater flexibility in handling risk preferences when allocating limited resources. In particular, the first stage minimizes the retrofitting cost by making strategic retrofit decisions whereas the second stage minimizes the travel cost. The conditional value-at-risk (CVaR) is included as the risk measure for the total system cost. The two-stage model is equivalent to a nonconvex mixed integer nonlinear program (MINLP). To solve this model using the Generalized Benders Decomposition (GBD) method, we derive a convex reformulation of the second-stage problem to overcome algorithmic challenges embedded in the non-convexity, nonlinearity, and non-separability of first- and second-stage variables. The model is used for developing retrofit strategies for networked highway bridges, which is one of the research areas that can significantly benefit from mean-risk models. We first justify the model using a hypothetical nine-node network. Then we evaluate our decomposition algorithm by applying the model to the Sioux Falls network, which is a large-scale benchmark network in the transportation research community. The effects of the chosen risk measure and critical parameters on optimal solutions are empirically explored

Multistage Infrastructure System Design: An Integrated Biofuel Supply Chain against Feedstock Seasonality and Uncertainty

A biofuel supply chain consists of various interdependent components from feedstock resources all the way to energy demand sites. This study focuses on the design of an efficient biofuel supply chain system against seasonal variations and uncertainties of feedstock supply in an integrative manner. By integrating planning and operational decisions in a stochastic programming framework, we aim at finding an effective design strategy for biofuel supply chain that is economically viable and hedges well against a wide range of future uncertainties. A solution algorithm based on scenario decomposition is designed to overcome computational challenges involved in large-scale applications. A California case study is implemented to demonstrate the applicability of the proposed methods in evaluating the economic potential, the infrastructure needs, and the risk of wastes-based bioethanol production

Enhanced Light Utilization in Semitransparent Organic Photovoltaics Using an Optical Outcoupling Architecture

Buildingâ integrated photovoltaics employing transparent photovoltaic cells on window panes provide an opportunity to convert solar energy to electricity rather than generating waste heat. Semitransparent organic photovoltaic cells (STâ OPVs) that utilize a nonfullerene acceptorâ based nearâ infrared (NIR) absorbing ternary cell combined with a thin, semitransparent, high conductivity Cuâ Ag alloy electrode are demonstrated. A combination of optical outcoupling and antireflection coatings leads to enhanced visible transmission, while reflecting the NIR back into the cell where it is absorbed. This combination of coatings results in doubling of the light utilization efficiency (LUE), which is equal to the product of the power conversion efficiency (PCE) and the average photopic transparency, compared with a conventional semitransparent cell lacking these coatings. A maximum LUE = 3.56 ± 0.11% is achieved for an STâ OPV with a PCE = 8.0 ± 0.2% at 1 sun, reference AM1.5G spectrum. Moreover, neutral colored STâ OPVs are also demonstrated, with LUE = 2.56 ± 0.2%, along with Commission Internationale d’Eclairage chromaticity coordinates of CIE = (0.337, 0.349) and a color rendering index of CRI = 87.An efficient and neutral colored semitransparent organic photovoltaic cell (STâ OPV) is realized by utilizing a nearâ infrared (NIR) absorbing ternary cell combined with a thin, semitransparent, highâ conductivity Cuâ Ag alloy electrode. A combination of optical outcoupling and antireflection coatings leads to enhanced visible transmission, while reflecting the NIR back into the cell where it is absorbed.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151812/1/adma201903173.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151812/2/adma201903173_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151812/3/adma201903173-sup-0001-S1.pd

ZrTe2/CrTe2: an epitaxial van der Waals platform for spintronics

The rapid discovery of two-dimensional (2D) van der Waals (vdW) quantum materials has led to heterostructures that integrate diverse quantum functionalities such as topological phases, magnetism, and superconductivity. In this context, the epitaxial synthesis of vdW heterostructures with well-controlled interfaces is an attractive route towards wafer-scale platforms for systematically exploring fundamental properties and fashioning proof-of-concept devices. Here, we use molecular beam epitaxy to synthesize a vdW heterostructure that interfaces two material systems of contemporary interest: a 2D ferromagnet (1T-CrTe2) and a topological semimetal (ZrTe2). We find that one unit-cell (u.c.) thick 1T-CrTe2 grown epitaxially on ZrTe2 is a 2D ferromagnet with a clear anomalous Hall effect. In thicker samples (12 u.c. thick CrTe2), the anomalous Hall effect has characteristics that may arise from real-space Berry curvature. Finally, in ultrathin CrTe2 (3 u.c. thickness), we demonstrate current-driven magnetization switching in a full vdW topological semimetal/2D ferromagnet heterostructure device.Comment: Includes new data (ST-FMR) and calculations (spin Hall conductivity

Optimal Retrofit Scheme for Highway Network under Seismic Hazards

Many older highway bridges in the United States (US) are inadequate for seismic loads and could be severely damaged or collapsed in a relatively small earthquake. According to the most recent American Society of Civil Engineers’ infrastructure report card, one-third of the bridges in the US are rated as structurally deficient and many of these structurally deficient bridges are located in seismic zones. To improve this situation, at-risk bridges must be identified and evaluated and effective retrofitting programs should be in place to reduce their seismic vulnerabilities. In this study, a new retrofit strategy decision scheme for highway bridges under seismic hazards is developed and seamlessly integrate the scenario-based seismic analysis of bridges and the traffic network into the proposed optimization modeling framework. A full spectrum of bridge retrofit strategies is considered based on explicit structural assessment for each seismic damage state. As an empirical case study, the proposed retrofit strategy decision scheme is utilized to evaluate the bridge network in one of the active seismic zones in the US, Charleston, South Carolina. The developed modeling framework, on average, will help increase network throughput traffic capacity by 45% with a cost increase of only $15million for the Mw 5.5 event and increase the capacity fourfold with a cost of only$32m for the Mw 7.0 event

Multistage Infrastructure System Design: An Integrated Biofuel Supply Chain against Feedstock Seasonality and Uncertainty

A biofuel supply chain consists of various interdependent components from feedstock resources all the way to energy demand sites. This study focuses on the design of an efficient biofuel supply chain system against seasonal variations and uncertainties of feedstock supply in an integrative manner. By integrating planning and operational decisions in a stochastic programming framework, we aim at finding an effective design strategy for biofuel supply chain that is economically viable and hedges well against a wide range of future uncertainties. A solution algorithm based on scenario decomposition is designed to overcome computational challenges involved in large-scale applications. A California case study is implemented to demonstrate the applicability of the proposed methods in evaluating the economic potential, the infrastructure needs, and the risk of wastes-based bioethanol production