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• This paper proposed a novel wind speed forecasting algorithm. • A new dataset division mechanism based on the moving window is used in this paper. • In order to improve the accuracy and stability simultaneously, the multi-objective particle swarm optimization is applied. • We compared the proposed MOPSO-Combined method with the combined model that published before in the paper. Accurate wind speed forecasting is important in power grid security, power system management, operation and market economics. However, most research has focused only on improving either accuracy or stability, with few studies addressing the two issues, simultaneously. Therefore, we proposed a novel combined model based on multi-objective particle swarm optimization, which is applied to optimize the key parameters of the echo state network. Most combined wind speed forecasting methods just use the combination theory to combine individual methods, this paper uses echo state network to combine the intermediate wind speed forecasting results of three artificial neural networks. Moreover, a new dataset division mechanism based on the moving window is applied in this paper. Firstly, the length of the input data is changed from 5 to 15 for 1-step, 2-step and 3-step wind speed forecasting, after that, the optimal length of the input vector can be got. And then we apply this optimal length of the input vector to another dataset for further verifying the proposed method. In order to verify the forecasting effectiveness of the proposed forecasting model, the 80/min wind speed data of M2 tower of the National Wind Power Technology Center of the United States were taken as an example. The experimental results indicate that the proposed algorithm is superior to the other ten comparative models in prediction accuracy and stability, and it also performs better than the combined model that we have proposed before. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread, including metrics such as infection cases, deaths, hospitalizations, and ICU usage. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. We outlined several case studies to demonstrate the model's state- and local-level projection capabilities. We further demonstrated how model outcomes could be used to evaluate perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. The model's two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended. Additionally, the prediction error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats. [ABSTRACT FROM AUTHOR]

Tight oil and shale oil are the important unconventional resources in shale systems, in which the tight oil is the most realistic and profitable resources. Due to special geological environments, lacustrine tight oil is different from marine tight oil in North America. Sweet-spotting is critical to develop these abundant resources, in which three aspects including geology, engineering and economy have been taken into consideration. In this paper, we proposed the definition of sweet spot zone and sweet spot interval in shale system, and discuss the geological significance & related technologies. Tight oil and shale oil should be assessed as a whole system during the future geological evaluation and technology development. The productivity of tight oil and shale oil will be increased by 30–50 million tons, making the economic development of oil in continental shale systems in China. • The world-level lacustrine tight oil accumulations in China. • Geological aspects of tight oil in shale systems. • Critical evaluation and development technologies for sweet spotting. Crude oil in unconventional shale systems, present as tight oil and shale oil, accumulates inside an oil kitchen in formations with coexistent sources and reservoirs. Organic matter present in oil shale is not yet mature and requires heating to convert it into crude oil. Oil exploration in shale systems involves the exploration of shale oil retained in source rocks and tight oil rich zones located near source rocks. Tight oil is a type of realistic unconventional oil resources in China. The marked increase in potential shale oil reserves, and exploration of these reserves, will result in a shale oil revolution similar to that experienced for shale gas. Based on a systematic comparison of geologic features of shale systems in the US and China, the geologic significance of the sweet spots in shale systems is proposed. This zone contains an abundance of unconventional oil in shale systems that can be explored and developed under current economic and technical conditions. The sweet spot zone refers to the zone in the tight oil rich zone which has industrial value within the scope of matured high-quality source rocks on the plane. The sweet spot interval refers to the high-productivity interval of tight oil which has industrial value through artificial stimulation. The main aim of oil exploration in shale formations is to identify the sweet spots. The distribution of the economic sweet spots in shale systems is evaluated by overlapping the geologic, engineering and economic sweet spots. Resource assessment techniques, the identification of logging data properties, high-resolution 3D seismic surveys, horizontal well production from well pads, and artificial reservoir development of sweet spots in oil-bearing shale formations can assist efficient development of oil. Globally, shale formations contain a significant volume of oil reserves. Currently, stimulated reservoir volume (SRV) techniques in horizontal wells in marine shale gas formations in the United States have average peak-productivity cycles of 10–15 years. To achieve commecial oil production in lacustrine shale systems in China, it is important to utilize large formation thicknesses and the high abundance of organic matter. In addition, the development of practical and economic techniques will result in an increase of productivity of tight oil and shale oil by 30–50 million tons, as well as the economic development of oil in lacustrine shale systems in China. [ABSTRACT FROM AUTHOR]

The paper presents some optimization dynamic mathematical models of the military aggressive subordinating interactions of two States in favor of one of them, created on the base of the optimization dynamic mathematical model of a State earlier constructed by the Author. [ABSTRACT FROM AUTHOR]

Crew costs are a significant portion of rail operating expenses, and so effective deployment of train crews is an important priority for freight railroads. This paper proposes an optimization model and develops an effective solution approach to assign crews to trains and decide when to deadhead crew members in double-ended crew districts in U.S. freight railways. To solve this model quickly, we develop several modeling and methodological enhancements including methods to reduce the problem size, strengthen the model, and generate good heuristic solutions. Computational testing and validation using 140 real-life problem instances demonstrate that our algorithm can find optimal and implementable solutions very quickly (typically within one minute), making it well-suited for practical use to provide real-time decision support to crew planners. [ABSTRACT FROM AUTHOR]

The United States is affected by an average of almost seven severe weather events a year, often resulting in billions of dollars in physical and economic damages, a subset of which are related to grid outages. There is a need for power and energy system stakeholders to better understand and implement the strategies that help reduce net-economic and societal consequences associated with grid outages by improving the resilience of their systems. In addition, there are incentives to reduce emissions and meet climate goals, several pathways of which include resilient technologies. Including resilience constraints and metrics in energy system planning models may help inform the design of more resilient systems that are also more renewable and sustainable. This paper reviews qualitative definitions of resilience, quantitative approaches to resilience, recent examples of the inclusion of resilience in energy system models with respect to acute climatological threats, and the gaps in fully articulating resilience in current modeling tools. We then outline steps to effectively improve resilience considerations against such threats into energy sector modeling tools. Based on the findings, the authors propose a novel framework for energy system resilience assessment and future areas of research to bridge the current modeling gaps. • To date, resilience concerns have been lacking in energy systems optimization models. • Research is needed to better understand how renewables can contribute to resilience. • Resilience concerns should include uncertainty and translate to economic costs. • Future modeling efforts of co-optimizing renewable and resilience goals should be applied to common and validated models. [ABSTRACT FROM AUTHOR]

Inaccessible transit stops prevent people with disabilities from using fixed-route transit services, thereby limiting their mobility. The Americans with Disabilities Act (ADA) prescribes the minimum accessibility requirements for transit stops for riders with disabilities. In addition, transit agencies may also choose to implement the 'universal-design' paradigm, which involves higher design standards than current ADA requirements and includes amenities that are useful for all riders, such as shelters and lighting. Because of budget limitations, however, transit agencies can select only a limited number of transit stops for ADA improvements each year. To increase the impact of these improvements, it is desirable that transit stops be selected such that they will maximize the overall benefits to patrons with disabilities. This paper describes a multiobjective binary nonlinear programming model for selecting, within a limited annual budget, a priority set of transit stops for improvements. The model aims to achieve two objectives, namely, meeting the minimum ADA standards and the universal-design standards. Compared with the traditional approach of selecting stops for improvements, which are often based on either staff experience or requests from elected officials, the optimization approach described in this paper provides a more objective platform to identify better candidate stops for ADA improvements. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an optimal control strategy suitable for investigating the mission performance of a spacecraft equipped with a NASAʼs Evolutionary Xenon Thruster. Using an accurate mathematical model of the propulsion system, based on both experimental data and a semi-empirical schematization of the power generation system, the minimum-time rendezvous problem with various celestial bodies is investigated through an indirect approach. Unlike a conventional approach, in which the thruster power is varied with continuity within a given range, here the solar electric propulsion system is described using a finite number of operation points, characterized by different pairs of thruster input power and propellant mass flow rate. The model includes the possibility of obtaining coasting phases along the spacecraft trajectory. The optimal control strategy is found for a three-dimensional problem in which the actual shape and relative orientation of the initial and final orbits are taken into account. The paper presents the simulation results for a number of minimum-time trajectories towards inner planets (Mercury, Venus, and Mars) and three Near Earth Asteroids (NEAs) of particular interest for the scientific community: 1998 KY-26, 99942 Apophis, and 2007 VK-184. [Copyright &y& Elsevier]

Abstract: This paper deals with a fair ramp metering problem which takes into account average travel delay distribution among on-ramps for an expressway system comprising expressways, on-ramps and off-ramps. A novel spatial equity index is defined to measure the evenness of travel delay distribution among on-ramps within the predefined on-ramp groups. An ideal fair ramp metering problem therefore aims to find an optimal dynamic ramp metering rate solution that not only minimizes the total system delay, but also maximizes the equity indexes associated to the groups. Some of these objectives, however, contradict with each other, and their Pareto-optimality is explored. The fair ramp metering problem proposed in this paper is formulated as a multiobjective optimization model incorporating a modified cell-transmission model (MCTM) that captures dynamic traffic flow pattern with ramp metering operations. The MCTM then is embedded in the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve the multiobjective optimization model. Finally, the Interstate I-210W expressway-ramp network in the United States is adopted to assess the methodology proposed in this paper. [Copyright &y& Elsevier]

This paper is the second of a two-part paper and describes the application of a multiperiod generalized network flow model that is used to analyze the economic efficiencies of the energy movements from the coal and natural gas suppliers to the electric load centers, providing a simulation tool that captures dynamics of and interdependencies between the coal, natural gas, and electricity networks. The modeling approach, mathematical formulation, and modeling assumptions are presented in part I. This sequel presents and discusses numerical results to illustrate the application of the model. The model enables public and private decision makers to carry out comprehensive analyses of a wide range of issues related to the energy sector, such as strategic planning, economic impact assessment, and different regulatory regimes. [ABSTRACT FROM AUTHOR]

This paper is the first of a two-part paper presenting a multiperiod generalized network flow model of the integrated energy system in the United States. Part I describes the modeling approach used to evaluate the economic efficiencies of the systemwide energy flows, from the coal and natural gas suppliers to the electric load centers. Under the proposed problem formulation, fuel supply and electricity demand nodes are connected via a transportation network, and the model is solved for the most efficient allocation of quantities and corresponding prices. The methodology includes physical, economic, and environmental aspects that characterize the different networks. Part II of this paper provides numerical results that demonstrate the application of the model. [ABSTRACT FROM AUTHOR]

In a recent issue of Water International, three authors discuss their views of the virtual water metaphor that is used to describe the water embedded in crops that are traded in international markets. The term 'virtual water' was created originally to gain the attention of public officials responsible for choosing policies that influence water use in arid regions. Over time, both the terminology and the scope of the virtual water metaphor have been extended beyond the original purpose. This paper supports the view that the virtual water metaphor is helpful in encouraging public officials to consider the scarcity value (opportunity cost) of water when designing policies that influence water use. The paper proposes, also, that the policy relevance of the virtual water metaphor is found primarily in its application to issues regarding the supply of agricultural products, rather than demand. The distinction between supply and demand issues and the potential value of the virtual water metaphor in policy discussions regarding supply are demonstrated using three examples of water allocation decisions, described within the framework of economic optimization. The examples include wheat production in Saudi Arabia and the production of sudangrass and ethanol in the United States. [ABSTRACT FROM AUTHOR]

Energy system optimization models (ESOMs) should be used in an interactive way to uncover knife-edge solutions, explore alternative system configurations, and suggest different ways to achieve policy objectives under conditions of deep uncertainty. In this paper, we do so by employing an existing optimization technique called modeling to generate alternatives (MGA), which involves a change in the model structure in order to systematically explore the near-optimal decision space. The MGA capability is incorporated into Tools for Energy Model Optimization and Analysis (Temoa), an open source framework that also includes a technology rich, bottom up ESOM. In this analysis, Temoa is used to explore alternative energy futures in a simplified single region energy system that represents the U.S. electric sector and a portion of the light duty transport sector. Given the dataset limitations, we place greater emphasis on the methodological approach rather than specific results. [ABSTRACT FROM AUTHOR]

This paper presents a long-term investment planning model that co-optimizes infrastructure investments and operations across transportation and electric infrastructure systems for meeting the energy and transportation needs in the United States. The developed passenger transportation model is integrated within the modeling framework of a National Long-term Energy and Transportation Planning (NETPLAN) software, and the model is applied to investigate the impact of high-speed rail (HSR) investments on interstate passenger transportation portfolio, fuel and electricity consumption, and 40-year cost and carbon dioxide (CO 2 ) emissions. The results show that there are feasible scenarios under which significant HSR penetration can be achieved, leading to reasonable decrease in national long-term CO 2 emissions and costs. At higher HSR penetration of approximately 30% relative to no HSR in the portfolio promises a 40-year cost savings of up to $0.63 T, gasoline and jet fuel consumption reduction of up to 34% for interstate passenger trips, CO 2 emissions reduction by about 0.8 billion short tons, and increased resilience against petroleum price shocks. Additionally, sensitivity studies with respect to light-duty vehicle mode share reveal that in order to realize such long-term cost and emission benefits, a change in the passenger mode choice is essential to ensure higher ridership for HSR. [ABSTRACT FROM AUTHOR]

The widespread destruction that follows large-scale natural disasters, such as Hurricane Katrina in August 2005, challenges the efficacy of traditional temporary housing methods in providing adequate solutions to housing needs. Recognising these housing challenges, the Congress of the United States allocated, in 2006, USD 400 million to the Department of Homeland Security to support Alternative Housing Pilot Programs, which are intended to explore the possibilities of providing permanent and affordable housing to displaced families instead of traditional temporary housing. This paper presents a new methodology and optimisation model to identify the optimal configurations of post-shelter housing arrangements to maximise the overall net socioeconomic benefit. The model is capable of quantifying and optimising the impacts of substituting temporary housing with alternative housing on the social and economic welfare of displaced families as well as the required additional costs of doing so. An application example is presented to illustrate the use of the model and its capabilities. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a comprehensive three-stage multiple criteria decision making framework for portfolio selection based upon financial and ethical criteria simultaneously. It may be noted that the ethical investment movement that began from the USA in 1960s has gained tremendous momentum the world over recently. The growing instances of corporate scams and scandals have made it incumbent upon the investors to consider the quality of governance of corporations and ethicality of their conduct. Indeed, there has been a spate of reforms relating to corporate laws and capital markets all over the world. Also, the investors are becoming conscious of the desirability of ethical evaluation of the assets. The growing influence of institutional investors has reinforced this consciousness. Hence, the research in the area must take cognizance of these developments to construct models that accord due consideration to ethical criteria besides the financial criteria. We use multiple methodologies toward the purpose. Analytical hierarchy process technique is used to obtain the ethical performance score of each asset based upon investor-preferences. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor-ratings on the financial criteria. Three hybrid portfolio optimization models are developed to obtain well diversified financially and ethically viable portfolios. These models have maximization of financial goal as main objective and differ in the way the ethical goal is pursued by the investor. Numerical illustrations are included to demonstrate the suitability of the models for obtaining diversified portfolios that meet the investor’s financial and ethical goals. [Copyright &y& Elsevier]

Abstract: Essential air service (EAS) is a federally funded program that helps provide commercial air transport service from smaller, geographically remote communities in the United States. While critics of this program frequently cite the underutilization of EAS connections as being an indicator of wasteful public spending, recent studies suggest that the spatial configuration of EAS subsidized airports may also contribute to systemic inefficiencies. The purpose of this paper is to explore the prospects for reducing EAS allocations while meeting existing geographic service needs. The analysis of this public sector service is structured using classic location coverage problems to examine whether there are any system inefficiencies. This enables an objective assessment to be carried out, using spatial optimization modeling approaches. The subsequent analysis provides the basis for a number of public policy insights, including the evaluation of service redundancies, the impact of geographic proximity guidelines and the potential for expanding coverage of the EAS program. [Copyright &y& Elsevier]

Abstract: One of the major barriers to the deployment of hydrogen as a transportation fuel is the lack of an infrastructure for supplying the fuel to consumers. Consequently, models are needed to evaluate the cost and design of various infrastructure deployment strategies. The best strategy will likely differ between regions based on the spatial distribution of H2 demand and variations in regional feedstock costs. Although several spatially-explicit infrastructure models have been developed, none of the published models are capable of optimizing interconnected regional pipeline networks for linking multiple production facilities and demand locations. This paper describes the Hydrogen Production and Transmission (HyPAT) model, which is a network optimization tool for identifying the lowest cost centralized production and pipeline transmission infrastructure within real geographic regions. A case study in the southwestern United States demonstrates the capabilities and outputs of the model. [Copyright &y& Elsevier]

Abstract: This paper provides a solution to address one of the requirements identified in the US Northwest 6th Power Plan, specifically, meeting an increase in the forecasted Pacific Northwest demand with renewable generation. A model is presented which evaluates one source of renewable generation, off-shore wind energy. The model analyzes different wind turbine design characteristics, assigns significance to potential synergistic effects of one design feature on another, and provides an optimized design relative to the cost, environment, robustness, and overall efficiency of an off-shore wind turbine. [Copyright &y& Elsevier]

Abstract: Infrastructure systems in the US are in urgent need of maintenance and rehabilitation. According to the most recent factsheet published by the American Society of Civil Engineers, one of the top five infrastructure concerns of today in the US is the transportation system. The major challenge facing maintenance managers in state Departments of Transportation (DOTs) today is to preserve the road networks at an acceptable level of serviceability subject to the stringent yearly maintenance and rehabilitation (M&R) budgets. Maintenance managers must allocate such limited budgets among competing alternatives. Absence of simpler decision-making tools exacerbates the matter. This paper presents the development and implementation of a simpler, yet useful, network-level pavement maintenance optimization model, which is a Linear Program (LP) subject to budget constraints and the agencies’ pavement performance goals in terms of total lane-miles in each pavement condition state. A decision-making tool is developed using Frontline Systems’ Risk Solver Platform add-in for Microsoft Office Excel. This decision-making tool can compute the optimal amount of investment for each pavement treatment type in a given funding period. Pavement condition data pertaining to one of the Districts within a state DOT is used to test the model presented herein. Within this context, nine treatment types along with their corresponding unit prices ($/Lane-Mile), five pavement condition states, pavement deterioration rates, network-level pavement performance targets, and available annual maintenance budget for a 15-year planning horizon are defined. The results presented show how an annual highway maintenance budget needs to be allocated or determined to achieve the District’s value proposition for various scenarios. Comparing the results of these varying scenarios provides insight on long-term strategies and the impact of target constraints on budget expenditures. [Copyright &y& Elsevier]

This paper describes the application of a novel multiobjective self-adaptive differential evolution (MOSADE) algorithm for the simultaneous optimization of component sizing and control strategy in parallel hybrid electric vehicles (HEVs). Based on an electric assist control strategy, the HEV optimal design problem is formulated as a nonlinear constrained multiobjective problem with competing and noncommensurable objectives of fuel consumption and emissions. The driving performance requirements are considered constraints. The proposed MOSADE approach adopts an external elitist archive to retain nondominated solutions that are found during the evolutionary process. To preserve the diversity of Pareto optimal solutions, a progressive comparison truncation operator based on the normalized nearest neighbor distance is proposed. Moreover, a fuzzy set theory is employed to extract the best compromise solution. Finally, the optimization is performed over the following three typical driving cycles that are currently used in the U.S. and European communities: 1) the file transfer protocol; 2) ECE +EUDC; and 3) Urban Dynamometer Driving Schedule. The results demonstrate the capability of the proposed approach to generate well-distributed Pareto optimal solutions of the HEV multiobjective optimization design problem. The comparison with the reported results of genetic-algorithm-based weighting sum approaches and Nondominated Sorting Genetic Algorithm II reveals the superiority of the proposed approach and confirms its potential for optimal HEV design. [ABSTRACT FROM PUBLISHER]

Abstract: In response to the scientific interest in Jupiter''s Galilean moons, NASA and ESA have plans to send orbiting missions to Europa and Ganymede, respectively. The inter-moon transfers of the Jovian system offer obvious advantages in terms of scientific return, but are also challenging to design and optimize due in part to the large, often chaotic, sensitivities associated with repeated close encounters of the planetary moons. The approach outlined in this paper confronts this shortcoming by exploiting the multi-body dynamics with a patched three-body model to enable multiple “resonant-hopping” gravity assists. Initial conditions of unstable resonant orbits are pre-computed and provide starting points for the elusive initial guess associated with the highly nonlinear optimization problem. The core of the optimization algorithm relies on a fast and robust multiple-shooting technique to provide better controllability and reduce the sensitivities associated with the close approach trajectories. The complexity of the optimization problem is also reduced with the help of the Tisserand–Poincaré (T–P) graph that provides a simple way to target trajectories in the patched three-body problem. Preliminary numerical results of inter-moon transfers in the Jovian system are presented. For example, using only 59m/s and 158 days, a spacecraft can transfer between a close resonant orbit of Ganymede and a close resonant orbit of Europa. [Copyright &y& Elsevier]

Human life expectancy has risen in most developed countries over several decades, causing observed demographic shifts. Many researchers have developed models to determine the expectancy of life at birth. Yet due to the complexity of the real-world mortality data, and recent global economy impacts, there is a great demand to search for new models to accurately predict the life expectancy, especially in the United States. In this paper, we focus on the analysis of mortality rate in the United States over a period of six decades (data from 1946 to 2005) with considerations of the six most common distribution functions in the mortality area. These functions are: Gompertz, Gompertz-Makeham, logistic, log logistic, loglog, and Weibull. Given complex mortality data, we develop models including algorithms to compute the mortality measures such as mortality rate, and then select the best function for predicting the life expectancy in the United States. The modeling results include life expectancy at birth, life expectancy at each age, mortality rate, and forecasting models. We also discuss a new risk-cost function with respect to life expectancy, and determine the optimal threshold lifetime level that maximizes the expected risk cost. Numerical examples are given to illustrate the expected risk-cost results. The proposed risk-cost optimization model, and the optimal lifetime threshold value can help the policy-decision makers of related healthcare organizations including insurance companies to carefully perform the tradeoff between the cost and benefits. This approach can affect both short term and long term related healthcare policies and plans. The results show that the logistic distribution uniquely out performs other distributions based on the mean square error criterion. We find that on average the life expectancy at birth in the United States in 2005 for both sexes, males, and females are 84.0, 81.9, and 84.6 years, respectively. This new result shows that the true life expectancy on average in the U.S. is significantly larger than in existing reports. Life expectancy obviously changes as one will get older. By late adulthood, a person's chances of living longer increase as is reflected by the life expectancy function. For example, although the life expectancy from birth in the United States in 2005 is 84.0 years, those who live to age 65 will have an average of 20.4 additional years left to live, making their life expectancy almost 85.4 years. Similarly, those who live to age 75 will gain an additional few years, making their life expectancy almost 87.6 years. On average, a child who will be born in the US in 2015 can expect to live 85.5 years, and in 2025 a child born can expect to live nearly 87 years. [ABSTRACT FROM AUTHOR]

Abstract: Highway bridges are subjected to strength degradation processes. Under budget constraints, it is important to determine the best maintenance strategies. Optimized strategies, based on prediction models, are already considered for the maintenance and operation of highway bridges. Prediction models are updated both in space and time by using non-destructive testing methods. Nevertheless, there is an urgent need for the efficient inclusion of structural health monitoring (SHM) data in structural assessment and prediction models. Indeed, SHM allows keeping strength degradation processes under control and should be included in life-cycle cost models. The lifetime reliability of structures is characterized by survivor functions. The SHM data enable to update the probability density function of time to failure through a Bayesian process. The aim of this paper is threefold: (a) to include SHM data in a bridge life-cycle cost analysis, (b) to determine optimal maintenance strategies based on monitoring information, and (c) to show the benefits of SHM. Optimal strategies are determined considering the cases without and with including monitoring results; the benefit of monitoring is then highlighted. The proposed concepts are applied to the I-39 Northbound Bridge over the Wisconsin River in Wisconsin, USA. A monitoring program of that bridge was performed by the ATLSS Engineering Research Center at Lehigh University. [ABSTRACT FROM AUTHOR]

Abstract: This paper analyzes the intermodal transportation network of containerized imports to the United States. An optimization model is developed that integrates international and North America inland transport networks. The results indicate optimal ship size, route, port, and interior shipping corridor and are highly reflective of observed shipments. Sensitivity analyzes were performed to evaluate impacts of congestion on capacity constraints as well as the prospective impacts of the alternative routes that are emerging. Results indicate that Prince Rupert is highly competitive for shipments to some markets, but the expansion of the Panama Canal would have lesser impacts on container flows. [Copyright &y& Elsevier]

The large-scale and catastrophic impacts of Hurricanes Katrina and Rita in 2005 challenged the efficacy of traditional postdisaster temporary housing methods. To address these challenges, the U.S. Congress appropriated $400 million to the Department of Homeland Security to support alternative housing pilot programs, which encourage innovative housing solutions that will facilitate sustainable and permanent affordable housing in addition to serving as temporary housing. Facilitating and maximizing the sustainability of postdisaster alternative housing is an important objective that has significant social, economic, and environmental impacts. This paper presents the development of a novel optimization model that is capable of (1) evaluating the sustainability of integrated housing recovery efforts under the alternative housing pilot program and (2) identifying the housing projects that maximize sustainability. An application example is analyzed to demonstrate the use of the developed model and its unique capabilities in maximizing the sustainability of integrated housing recovery efforts after natural disasters. [ABSTRACT FROM AUTHOR]

Abstract: Wind is one of the most promising sources of alternative energy. The construction of wind farms is destined to grow in the U.S., possibly twenty-fold by the year 2030. To maximize the wind energy capture, this paper presents a model for wind turbine placement based on the wind distribution. The model considers wake loss, which can be calculated based on wind turbine locations, and wind direction. Since the turbine layout design is a constrained optimization problem, for ease of solving it, the constraints are transformed into a second objective function. Then a multi-objective evolutionary strategy algorithm is developed to solve the transformed bi-criteria optimization problem, which maximizes the expected energy output, as well as minimizes the constraint violations. The presented model is illustrated with examples as well as an industrial application. [Copyright &y& Elsevier]

This paper employs an economic-engineering optimization model to explore water supply options for environmental restoration of the Colorado River Delta, Mexico. Potential water sources include reductions in local agricultural and urban water use through water markets, wastewater reuse, and additional Colorado River flows from the United States. For these alternatives, the optimization model estimates operating and water scarcity costs, water scarcity volumes, and marginal economic costs of environmental flows and values of additional Colorado River flows from the United States over a range of required delta environmental flows. Economic values for agricultural and urban water uses were estimated by two ancillary models. The results provide insights into economically promising water supplies for restoration activities. Quantifying the trade-off between agricultural and urban economic valuation and environmental flows provides a framework for decision makers to quantify their valuation of environmental flows. The model also provides a framework for integrating additional knowledge of the system as information becomes available. [ABSTRACT FROM AUTHOR]

This paper explores space-filling experimental designs for use with local quadratic approximations. A Mean Squared Distance (MSD) criterion is offered for optimization of the uniformity of designs. The same criterion is also called the Mean Square Error (MSE) criterion by other authors. The authors have proposed a method of optimization of designs in unit cube and spherical regions, which can be used for designs with constrained or unconstrained level values. Also, other criteria such as Entropy, Discrepancy, MaxMin and Eglaj`s criterion are compared by approximation of the test function, using several weight functions for local approximation. It is shown that the Latin Hypercube type designs, optimized according to the MSD criteria, give the best results by approximation of test function both without statistical error and with statistical error with normal distribution. The design optimization method is used also for the well-known NASA High Speed Civil Transport aircraft (HSCT) approximation challenge. For this case, the choice of a fixed number of experiments (from the given 2490) according to the MSD criteria and following local linear approximation gives good results, more accurate than with solutions proposed by other authors, using the Response surface and the Kriging methods. [ABSTRACT FROM AUTHOR]

This paper presents results of a large-scale economic-engineering optimization model of California’s water supply system. The results of this 4-year effort illustrate the value of optimization modeling for providing integrated information needed to manage a complex multipurpose water system. This information includes economic benefits of flexible operations, economic valuation of capacity expansion opportunities, estimating user willingness to pay for additional water, economic opportunity costs of environmental flows, and identification of promising conjunctive use and water transfer opportunities. The limitations of such modeling also are discussed. Overall, the results suggest improvements to system operation and water allocations with a statewide expected value potentially as high as $1.3 billion/year. Significant improvements in performance appear possible through water transfers and exchanges, conjunctive use, and various operational changes to increase flexibility. These changes also greatly reduce costs to agricultural and urban users of accommodating environmental requirements. Model results also suggest benefits for expanding selected conveyance and storage facilities. [ABSTRACT FROM AUTHOR]

Coordinating the distribution of ammunition and scheduling strategic transportation resources during military contingency operations is a complex process. This paper presents a large-scale optimization-based planning method that uses column generation to schedule the movement of ammunition and transportation resources through a time-space network representation of the distribution system. The optimization-based planner is initialized using a feasible solution generated by a heuristic planning method. Both the optimization-based planner and the heuristic planner generate plans with improved ship utilization and delivery tardiness values as compared to plans generated using current planning techniques. In addition, the heuristic planner is implemented within a closed-loop planning and control framework, and is used to generate plans on a rolling horizon basis. [Copyright &y& Elsevier]

Aviation security protects vital national interests, as well as passengers and aircraft. Key components of an aviation security system include baggage and passenger screening devices and operations. Determining how and where to assign (deploy) such devices can be quite challenging. Moreover, even after such systems are in place, it can be difficult to measure their effectiveness. This paper describes how discrete optimization models can be used to address these questions, based on three performance measures that quantify the effectiveness of airport baggage screening security device systems. These models are used to solve for optimal airport baggage screening security device deployments considering the number of passengers on a set of flights who have not been cleared using a security risk assessment system in use by the Federal Aviation Administration (i.e., passengers whose baggage is subjected to screening), the number of flights in this set, and the size of the aircraft for such flights. Several examples are provided to illustrate these results, including an example that uses data available from the Official Airline Guide. [ABSTRACT FROM AUTHOR]

Delivering automobiles, vans, and trucks to auto dealerships is a unique problem among the routing and scheduling problem class. A subproblem is that of loading the vehicles onto an auto-carrier (equipment specially designed for this purpose). Allocation of the vehicles to this equipment has typically been done by means of trial and error and heuristic methods. This paper shows how the loading aspect of the delivery problem can be solved using optimizing methods. The approach has the potential of replacing the empirical methods currently used in practice. [ABSTRACT FROM AUTHOR]

The equilibrium network design problem can be formulated as a mathematical program with variational inequality constraints. We know this problem is nonconvex; hence, it is difficult to solve for a globally optimal solution. In this paper we propose a simulated annealing algorithm for the equilibrium network design problem. We demonstrate the ability of this algorithm to determine a globally optimal solution for two different networks. One of these describes an actual city in the midwestern United States. [ABSTRACT FROM AUTHOR]