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Purpose: The purpose of this paper is to provide a decision support model for optimizing the composition of portfolios of market-driven academic programs, primarily in schools offering market-driven academic programs. This model seeks to maximize financial performance during a desired planning time period while also achieving targets for other non-financial dimensions of the portfolio (e.g. mission alignment, student demographics and faculty characteristics) by deciding the types of programs to be added, redesigned and/or removed for each year of the planning period. Design/methodology/approach: This paper introduces an integer linear program (i.e. mathematical optimization) to describe the portfolio optimization problem. Integer linear programs are widely used for optimizing portfolios of financial and non-financial products and services in non-educational settings. Additionally, in order to use an integer linear program for the model, qualitative data must be incorporated into the quantitative model. To do so, this paper first discusses two methods of quantifying qualitative information related to market-driven program dimensions in the following section. Findings: The paper provides empirical insights related to the impact of this model through an illustrative case from a school offering market-driven academic programs at a prestigious private university in the USA. The results of the case highlight the potential positive impact of utilizing a similar model for planning purposes. Financially, the model results in almost double financial surplus than without the model while also achieving higher scores for all non-financial dimensions measured for the portfolio analyzed. Originality/value: This paper provides a unique and impactful model for decision support in strategic planning for market-driven academic programs, an area of intense discussion and focus in higher education today. [ABSTRACT FROM AUTHOR]

For homothetic time and state separable preferences, the coefficient of relative risk aversion (CRRA) is equal to the reciprocal of the elasticity of intertemporal substitution (EIS). This paper shows that when the growth rate of consumption is i.i.d., asset pricing models based upon preferences in which the CHRA and the EIS are no longer linked do not have more explanatory power. Further, in these stochastic environments, estimates of the CHRA in the standard preferences are measures of the true CHRA and not the EIS. These results are fairly accurate descriptions of economies calibrated using United States annual data. [ABSTRACT FROM AUTHOR]

A central question of many previous monetary studies has been the determination of the effects of various forces upon the actual supply of money in the economy. One of a number of competing monetary supply hypotheses (Brunner and Meltzer) has focused on the relationship between bank reserves and deposit creation. This theoretical structure was first suggested in earlier works by Phillips and Crick. Recent studies of similar nature, including those of Mellon and Orr, Morrison, and Brown and Lloyd, have employed the techniques of inventory theory to examine the factors influencing bank credit expansion. Similar inventory treatments of other cash balance and portfolio adjustment models are presented in the works of Baumol, Eppen and Fama, Lloyd, and Chitre. In the present paper, the effects of various forces on the optimal expansion of credit and the holding of reserves are investigated using the techniques of dynamic programming (see, for example, Bellman and Dreyfus). Discussed in the paper are the effects on bank credit expansion of uncertainty about reserve losses, various types of penalty costs, costs of adjustment, uncertain future interest rates, and of various institutional structures under which the bank must operate. This latter topic is important because of the Federal Reserve policy change of September 1968. Prior to that time, required reserves and actual reserves were computed over the same reserve period. Since that time, however, the required reserves in any period are known with certainty at the beginning of the period and depend upon deposit levels two periods earlier. For purposes of mathematical convenience, this latter structure is treated here as a one-period lag. [ABSTRACT FROM AUTHOR]

One of the important uses of an EOQ item's distribution of lead time demand is to set its reorder point. This paper shows that a realistic model of observed demand patterns can be chosen from the compound Poisson family of distributions. Actual historical data from several U.S. Air Force bases are analyzed using the geometric-Poisson and constant-Poisson distributions. The control discipline is order quantity, reorder point with continuous review. The service level is based on percent of demand supplied during lead time and is consistent with current USAF methodology. The reorder point is based on independent calculations and no attempt is made to jointly optimize the order quantity and reorder point. Lead time is assumed to be known and constant. [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]

The United States Coast Guard (USCG), a part of the U.S. Department of Homeland Security, is the nation's leading agency in maritime security, safety, and stewardship. One of the primary USCG resources is a fleet of boats (maritime vessels less than 65 feet in length) of various types that must be allocated to USCG stations nationwide. This paper describes the academic-industry collaboration between the authors and the USCG, which resulted in the development of an integer linear programming model that optimally matches supplies of various types of boats to station demands. The paper also introduces a model for the optimal sharing of scarce boat resources. In addition, we generalize our model, using value-at-risk and robust optimization ideas, to manage the risk of boat shortages. The paper reports on the USCG implementation process and discusses internal resistance issues and eventual adoption. We describe USCG modifications to the model recommendations due to practicalities not captured by our model. Finally, we present the significant improvements to USCG quantitative performance metrics that resulted from our model's recommendations. These include a considerable reduction of excess capacity and boat shortages at the stations, a decrease in the overall fleet size with a simultaneous increase in boat utilization, and overall reduction of the fleet operating cost. We also discuss in depth how our model effected these improvements. [ABSTRACT FROM AUTHOR]

Summary. The fingerprint has, with considerable justification, come to be regarded as the acme of forensic identification. Over the last century, millions of cases have been resolved world wide because of marks left at crime scenes. The comparison methodology has not evolved greatly during its history and it is universal practice to present fingerprint evidence to a court as a categoric opinion of identification or exclusion, or to classify the evidence as inconclusive and not to report it. There has been a growing movement to supplement the fingerprint examination process by one that has a statistical model, supported by appropriate databases for calculating numerical measures of weight of evidence. The movement calls for the establishment of a logical framework for informing conclusions, based on explicit assumptions and data and open to revision and improvement. The aim is to enable the numerical evaluation of evidence that would currently be reported as a categorical identification and also of evidence that would currently be classified as inconclusive. The paper presents the results of a project carried out by the Forensic Science Service that aims to attain this goal. After a historical review, we describe a formal model for assigning numerical values to configurations of minutiae in fingerprints. We describe how the parameters of the model have been optimized to take account of interoperator variability and distortion of the finger pad, and we present the results of a substantial validation experiment that was based on searches that have been carried out on the US national fingerprint database of approximately 600 million fingerprints. [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]

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]

Bridge maintenance and replacement optimization methods use deterioration models to predict the future condition of bridge components. The purpose of this paper is to develop a framework for bridge maintenance optimization using a deterioration model that takes into account aspects of the history of the bridge condition and maintenance, while allowing the use of efficient optimization techniques. Markovian models are widely used to represent bridge component deterioration. In existing Markovian models, the state is the bridge component condition, and the history of the condition is not taken into account, which is seen as a limitation. This paper describes a method to formulate a realistic history-dependent model of bridge deck deterioration as a Markov chain, while retaining aspects of the history of deterioration and maintenance as part of the model. This model is then used to formulate and solve a reliability-based bridge maintenance optimization problem as a Markov decision process. A parametric study is conducted to compare the policies obtained in this research with policies derived using a simpler Markovian model. [ABSTRACT FROM AUTHOR]

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]

Earlier management systems were typically developed to manage individual components of the transportation system. More recently, these systems have been evolving into integrated infrastructure management systems (IIMSs) that simultaneously consider the needs of the different components of the entire transportation system. This paper describes the preliminary experience of a small urban area, located in northwestern Vermont, with the development of a framework for an IIMS. The framework, which is designed to manage the needs of six different components of the transportation system, is based on the use of available off-the-shelf tools whenever possible, and the use of geographic information systems (GIS) software to serve as an integration platform, as well as a visual interface. Within this framework, budget allocation among the competing needs of the different transportation components is determined using a mathematical programming approach. The paper concludes with a case study that describes how the developed framework is applied to the city of South Burlington, Vermont. Important lessons learned from this case study are also summarized. [ABSTRACT FROM AUTHOR]

Existing approaches to testing for the efficiency of a given portfolio make strong parametric assumptions about investor preferences and return distributions. Stochastic dominance-based procedures promise a useful nonparametric alternative. However, these procedures have been limited to considering binary choices. In this paper we take a new approach that considers all diversified portfolios and thereby introduce a new concept of first-order stochastic dominance (FSD) optimality of a given portfolio relative to all possible portfolios. Using our new test, we show that the U.S. stock market portfolio is significantly FSD nonoptimal relative to benchmark portfolios formed on market capitalization and book-to-market equity ratios. Without appealing to parametric assumptions about the return distribution, we conclude that no nonsatiable investor would hold the market portfolio in the face of the attractive premia of small caps and value stocks. [ABSTRACT FROM AUTHOR]

The model described in this paper was developed to provide guidance for price and promotion decisions in a consumer-goods division doing business in the United States marketplace. Demand is a dynamic function of price and promotion; the total market is divided into M interacting market segments that often have different needs and wants for the product and that can be sent different promotional messages; the segments usually have different price and promotion elasticities. For a given planning horizon, the model determines the optimal price to be charged in the M segments, the optimal promotional budget, and its allocation over the M segments. A convex programming formulation is utilized. The paper discusses the reality of the environment to be modeled, develops the model in relation to this reality, and then provides an empirical illustration of the model. [ABSTRACT FROM AUTHOR]

This paper is concerned with formulating, exploring and interpreting the uses and constructs which may be derived from a mathematical model of programming type which expresses more realistically than past efforts the actual conditions of current operations. It is an attempt to provide a means of attaining thereby an objective understanding of the implications of actual Federal Reserve liquidity policy on the actions and opportunities of banking institutions. The model presented corresponds to the problem of determining an optimal portfolio for an individual bank over several time periods in accordance with requirements laid down by bank examiners which are interpreted as defining limits within which the level of risk associated with the return on the portfolio is an acceptable one. The problem is transformed into an equivalent one offering advantages of analysis and of computation. Some methods of effective computation are explored. [ABSTRACT FROM AUTHOR]

With the development of mobile internet and multi-media service, advanced techniques need to be applied in wireless network to improve user experience. Long term evolution (LTE) systems, which can offer up to 100Mbps downlink date rates, have been deployed in USA and Korea. However, because plenty of complex physical layer algorithms are utilized, network planning and optimization become heavy burdens for LTE network operators. Self-organizing network (SON) is a promising method to overcome this problem by automatically selecting and adjusting key parameters in LTE systems. In this paper, we present a dynamic adjusting algorithm to improve both handover and load balancing performance by introducing a weighted co-satisfaction factor (CSF). Analysis and system level simulation are conducted to exhibit the performance improvement of the proposed scheme. Results show that the proposed method outperforms the conventional solutions in terms of the network handover success ratio and load balancing gains significantly. [ABSTRACT FROM AUTHOR]

A target is hidden in one of several possible locations, and the objective is to find the target as fast as possible. One common measure of effectiveness for the search process is the expected time of the search. This type of search optimization problem has been addressed and solved in the literature for the case where the searcher has imperfect sensitivity (possible false negative results), but perfect specificity (no false positive detections). In this paper, which is motivated by recent military and homeland security search situations, we extend the results to the case where the search is subject to false positive detections. [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]

In this paper, we re-examine the question of identification of source areas using optimization methods for two Iowa watersheds. The question of nonuniqueness is addressed first, with the result that only eight sediment contributing regions are allowed to be active simultaneously as in the companion study. Tukey's procedure is utilized to examine if the differences in erodibility of the different regions are statistically significant. Results were presented in the form of maps to obtain a spatial picture of the potential of sediment generating areas. Comparisons with physical characteristics of the watersheds showed good agreement between erosion potential and properties associated with erosion. [ABSTRACT FROM AUTHOR]

Bidding for offshore U.S. oil and gas leases is a major corporate resource allocation problem involving enormous uncertainties and very high stakes. This paper presents two new, operationally useful decision analysis models to aid in bidding for oil and gas leases. They are unique in that they consider risk aversion and probabilistic dependence among the values of the leases, with both bid levels and partnership shares as (continuous) decision variables. They are suitable for use in evaluating proposed bidding policies or as objective functions in optimization formulations. Practicality of their data requirements is evidenced by use of one of the models for several years in a major oil company. Comparison of optimal solutions to these models on a small example, using actual oil-company data, demonstrates the importance of taking risk aversion and probabilistic dependence into account, and provides insight into the adequacy of independence and conditional dependence as approximations for dependence. These results are pertinent to other real-world allocation problems that share many of the characteristics of bidding problems, such as R&D funds allocation. [ABSTRACT FROM AUTHOR]

As the United States has become a more litigious society, executives are finding more of their time and their firms' resources consumed by litigation. This paper attempts to assist their decision making with regard to legal matters by looking at the following questions:(1) Under what conditions should a potential plaintiff litigate a specific issue? (2) Under what conditions should a defendant faced with a lawsuit respond forcefully to it? (3) How can legal expenses be budgeted and controlled by both sides? (4) What are the most likely conditions under which both plaintiff and defendant would be willing to seek out of court settlement? The research determines the economic requisites of both a plaintiff's decision to litigate an issue and the corresponding defendant's decision on how to respond to the suit. These decisions are viewed as both commensurate and competitive with other managerial operating decisions, e.g. capital budgeting and market expansion, that make demands upon the firm's resources. Models from economics and learning theory are employed both to illustrate the relationship between expenditures on a civil suit and the probability of success and to show how litigation costs should be budgeted and controlled. The research also shows both the conditions under which it is advantageous for both sides to seek an out of court settlement and the means to estimate the probability of out of court settlements in civil litigation in general. This latter is shown under the most general conditions to be a minimum of 0.66 and to rise as the participants in the litigation become more risk averse. [ABSTRACT FROM AUTHOR]

In 1970, abortion on demand was legalized in several sections of the United States. Due to increased demand for abortions, free standing abortion clinics came into existence. This paper develops a methodology for determining the optimal design of a free standing abortion clinic, in order to maximize its profits. An example of a clinic which was actually designed employing the methodology is presented, and the results suggest that the methodology is relevant and useful. [ABSTRACT FROM AUTHOR]

This paper considers a class of network optimization problems in which certain directed arcs must be covered by a set of cycles. Our study was motivated by a distribution planning problem of a commercial firm that had to make deliveries over several origin-destination pairs (directed arcs) and that could service any demand arc by using a vehicle in its own fleet or by paying a common carrier. The problem is to determine an optimal fleet size and the resulting vehicle routes while satisfying maximum route-time restrictions. We formulate the problem, describe some approximate solution strategies, and discuss important implementation issues. [ABSTRACT FROM AUTHOR]

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The article has a twofold goal: bringing to light both the advantages and the disadvantages of individual and group decision in juridical cases (the situation of the jurors is considered in the sociological literature as a quasi-natural experiment. The paper analyzes various dimensions and aspects of this topic, including a short history of each model, in order to better understand the reasons why both systems are operating today. The research started from the question: which of these two juridical systems (the American system and the Romanian one) are regarded as more efficient by those who practice law. A series of semi-structured interviews applied to experienced lawyers from both countries focused on: differences perceived between decision-making processes involving a court of jury and a judge vs. judge alone; advantages and disadvantages of each decision making system; weak points of each system; possible changes for the optimization of the decision-making process; and an evaluation of the reaction of the public opinion. The analysis of the data showed that some of the hypotheses were partially valid. In the final part, I discussed the perspectives of the implementation of the decision-making process based on a court of jury in Romania. [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: The purpose of this paper is to describe refinements to the recently developed GA-ACO method and to show its application to a real world engineering design problem. The GA-ACO method is a genetic algorithm with a new operator, called an ACO operator. ACO stands for Ant Colony Optimization. The ACO operator uses pheromone trails, a method from Ant Colony Optimization to influence the genetic algorithm. The GA-ACO method is used to optimize an engineering design. Engineers produce a preliminary design for a system using a CAD tool. The output of the CAD tool is then translated into a design graph. Many additional characteristics of the design can be represented by labels on the design graph. The GA-ACO method is then used to optimize these labels. This technique can be applied widely to many design optimization problems. The application considered in this paper concerns optimization of designs for efficient assembly. It uses problems in engineering design encountered at Newport News Shipbuilding, the largest shipyard in the United States. We present a comparison of variations of the GA-ACO method with a standard genetic algorithm for this type of problem. [Copyright &y& Elsevier]

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]

This paper links campaign resources and voter calculus through a microeconomic optimization framework. I assume that candidate policy positions are fixed while personal valence scores and the salience of issue dimensions are malleable. Low valence candidates with many proximate competitors in the policy space will focus on building valence. High valence candidates who are relatively 'unclustered' in the policy space will focus on manipulating issue salience. Resources devoted to diminishing others' valence scores will increase as the number of viable candidates decreases. The model's results are tested, where feasible, using data from the Democratic Party primary of 2004. [ABSTRACT FROM AUTHOR]

Optimization of air traffic flow in the National Airspace System is, by nature, a large-scale problem, the solving of which usually requires intensive computation. This paper presents a mixed integer linear programming method to solve a nationwide air traffic flow management problem based on an aggregate air traffic model. A dual decomposition method is applied to solve the large-scale traffic management problem, where the nationwide problem is decomposed path by path into 1000s of subproblems that are solved independently. It is shown in this paper that the solution with the dual decomposition method converges to the global optimum. The major contribution of the proposed modeling and optimization framework is that it is about 6 times faster than a previously developed large-capacity cell transmission model. It is also shown that the proposed optimization method obtains an equally optimal solution as the large-capacity cell transmission model. In the meantime, the proposed model is capable of including both ground delay and airborne delay, while the large-capacity cell transmission model includes airborne delay only. The traffic forecasting capability of the new model is also examined statistically at the sector level and the national level. It is shown that the sector counts are predicted with relative errors of less than 20 % for over 80 % of the high- altitude sectors in the United States. Further analysis also suggests that the accuracy of the proposed model is comparable with the other two aggregate models in the literature. [ABSTRACT FROM AUTHOR]

The Division of HIV/AIDS Prevention (DHAP) at the Centers for Disease Control and Prevention has an annual budget of approximately $325 million for funding HIV prevention programs in the U.S. The purpose of this paper is to thoroughly describe the methods used to develop a national HIV resource allocation model intended to inform DHAP on allocation strategies that might improve the overall effectiveness of HIV prevention efforts. The HIV prevention resource allocation problem consists of choosing how to apportion prevention resources among interventions and populations so that HIV incidence is minimized, given a budget constraint. We developed an epidemic model that projects HIV infections over time given a specific allocation scenario. The epidemic model is then embedded in a nonlinear mathematical optimization program to determine the allocation scenario that minimizes HIV incidence over a 5-year horizon. In our model, we consider the general U.S. population and specific at-risk populations. The at-risk populations include 15 subgroups structured by gender, race/ethnicity and HIV transmission risk group. HIV transmission risk groups include high-risk heterosexuals, men who have sex with men and injection drug users. We consider HIV screening interventions and interventions to reduce HIV-related risk behaviors. The output of the model is the optimal funding scenario indicating the amounts to be allocated to all combinations of populations and interventions. For illustrative purposes only, we provide a sample application of the model. In this example, the optimal allocation scenario is compared to the current baseline funding scenario to highlight how the current allocation of funds could be improved. In the baseline allocation, 29% of the annual budget is aimed at the general population, while the model recommends targeting 100% of the budget to the at-risk populations with no allocation targeted to the general population. Within the allocation to behavioral interventions the model recommends an increase in targeting diagnosed positives. Also, the model allocation suggests a greater focus on MSM and IDUs with a 72% of the annual budget allocated to them, while the baseline allocation for MSM and IDUs totals 37%. Incorporating future epidemic trends in the decision-making process informs the selection of populations and interventions that should be targeted. Improving the use of funds by targeting the interventions and population subgroups at greatest risk may lead to improved HIV outcomes. These models can also direct research by pointing to areas where the development of cost-effective interventions can have the most impact on the epidemic. [ABSTRACT FROM AUTHOR]

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]

This paper presents a new integer programming (IP) model for large-scale instances of the air traffic flow management (ATFM) problem. The model covers all the phases of each flight--i.e., takeoff, en route cruising, and landing--and solves for an optimal combination of flow management actions, including ground-holding, rerouting, speed control, and airborne holding on a flight-by-flight basis. A distinguishing feature of the model is that it allows for rerouting decisions. This is achieved through the imposition of sets of "local" conditions that make it possible to represent rerouting options in a compact way by only introducing some new constraints. Moreover, three classes of valid inequalities are incorporated into the model to strengthen the polyhedral structure of the underlying relaxation. Computational times are short and reasonable for practical application on problem instances of size comparable to that of the entire U.S. air traffic management system. Thus, the proposed model has the potential of serving as the main engine for the preliminary identification, on a daily basis, of promising air traffic flow management interventions on a national scale in the United States or on a continental scale in Europe. [ABSTRACT FROM AUTHOR]

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]

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 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]

Systems analysis has potential for many applications. But it has pitfalls in use as well. This paper discusses the operational context for the application of systems analysis to modernizing nations of the world. It also suggests some limitations of systems analysis in its indiscriminate use for this range of problems. [ABSTRACT FROM AUTHOR]

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]

Aviation security is an important concern of national interest. Baggage screening security devices and operations at airport stations throughout the United States address this concern. 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. Uncovered flight segment (UFS) and uncovered passenger segment (UPS) performance measures provide a useful framework for measuring the effectiveness of a baggage screening security device deployment to a given station. However, the optimization models associated with these measures do not consider baggage screening strategies that permit partial screening of flights. To allow for such strategies, as well as to identify baggage screening security system models where the decision to screen each individual selectee bag is made individually (rather than collectively by flight), this paper introduces performance measures in which each selectee bag is assigned an individual value. In particular, the flight segment baggage value (FSBV) assigns a value to each selectee bag based upon the proportion of the flight segment that the bag covers. The passenger segment baggage value (PSBV) assigns a value to each selectee bag based on the proportion of the passenger segments that the bag covers. For each of these measures, an associated decision problem and integer programming model are introduced. In addition, several results are presented detailing both optimization techniques for the models associated with each measure and the relationships between the baggage value measures and other baggage screening security system measures. A real‐world example using actual flight data from the official airline guide is presented to illustrate an application of these models and results. [ABSTRACT FROM PUBLISHER]

This paper applies the test of operational meaningfulness to the many detection laws that have been formulated and used in the mathematical optimization of search, and given as generalizations of those first developed in the United States Navy during and immediately following World War II. The word "operational" is used both in the sense of practical O.R. and in that of P. W. Bridgman's well-known requirement: that in applying mathematics to the material world, the physical preconditions implied in the quantities used, the operations for measuring them, and their laws of consistency must be set forth explicitly. It is submitted that this criterion will orient O.R. work toward useful developments in the search theory and may serve to identify as such those theories that are of chief interest as developments in pure mathematics. Finally, we note that the requirement that every numerical assumption of probability distributions in a study of the outside world must be based on objective evidence is perfectly consistent with the "subjective" conception of probability, viz., that its meaning is apprehended intuitively--the former measures what the latter defines. [ABSTRACT FROM AUTHOR]

In recent years serious attention has been given to the potential of imprisonment to reduce crime by "general deterrence," which discourages criminal behavior in the general population through the threat of punishment and by "incapacitation," which involves physically separating criminals from the rest of society. Also of fundamental importance to the use of prison is evidence that suggests that the imprisonment rates (prisoners per capita) in the U.S. fluctuates only within very narrow limits. This apparent constraint suggests the need for an overall framework for reducing crime that best uses the limited resource, man-years of imprisonment. In this paper we develop a model that estimates total crime rate as a function of imprisonment policies, incorporating estimates of deterrent and incapacitative effects. Using imprisonment rate as a constraint, we develop an optimization framework to minimize crime rate and use it to explore implications of alternative incarceration policies. An illustrative example, using data from 1970, is provided. [ABSTRACT FROM AUTHOR]

An operations research study of fire company location in Denver, Colorado, has been conducted, resulting in recommendations that may reduce annual costs by more than one million dollars without a substantial change in the level of fire-suppression capability. One development resulting from this study is a hierarchical objective function for the set-covering problem; this objective function permits the simultaneous minimization of the number of fire stations and the maximization of the number of existing fire stations within the minimum total number of stations. This paper includes discussions of the use of mathematical programming, station configuration information models, the synergism of judgment and analysis, and the action that resulted from the recommendations. [ABSTRACT FROM AUTHOR]

In this paper we present a Nash dynamic game model of interactions among oil inventory and tariff policies for oil importing countries. The inventory policies consist of the rates for building up and drawing down strategic oil reserves, while the tariff policies consist of setting tariffs as a function of the state of the world oil market. The model represents a stochastic game in which countries are grouped into two players, where each player in the game chooses its individual policies to maximize expected social welfare. We use the model to present empirical results about the sizes of the stockpiles as a function of disruption probabilities, tariff policies and cooperation versus noncooperation between nations. In addition to results regarding the buildup and draw down rates as a function of the amount of disruption or probability of future disruption our central conclusion is that build-up and drawdown policies are essentially the same with or without international cooperation, due to the current inventory limits on U.S. and Foreign Strategic Petroleum Reserves. [ABSTRACT FROM AUTHOR]

In a recent conference, the economist Leland Johnson criticized the asymmetry between the voluminous theoretical literature and the absence of empirical work on the Averch-Johnson-Wellisz (A-J-W) hypothesis. This criticism has engendered a body of empirical research that is characterized by other imbalances. With one notable exception, the empirical A-J-W literature concentrates on the electrical generating industry disregarding the potential benefits from intellectual diversification. Also, much effort is expended testing for the overcapitalization phenomenon, but the attendant welfare implications are virtually ignored. This paper tries to redress some of these imbalances by (i) investigating the A-J-W hypothesis in the U.S. interstate natural gas transmission industry, and (ii) analyzing the social welfare impact of rate of return regulation on this industry. Section 1 of this paper, formulates the optimization models which are used to simulate the input-output decision of a natural gas transmission company. Section II compares the simulated solutions with data from a comprehensive sample of natural gas transmission companies. Section III analyzes the social welfare implications of regulating the industry by comparing the benefits of rate of return regulation to those obtainable from marginal-cost pricing.

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 problem of selecting nature reserves has received increased attention in the literature during the past decade, and a variety of approaches have been promoted for selecting those sites to include in a reserve network. One set of techniques employs heuristic algorithms and thus provides possibly sub-optimal solutions. Another set of models and accompanying algorithms uses an integer programming formulation of the problem, resulting in an optimization problem known as the Maximal Covering Problem, or MCP. Solution of the MCP provides an optimal solution to the reserve site selection problem, and while various algorithms can be employed for solving the MCP they all suffer from the disadvantage of providing a single optimal solution dictating the selection of areas for conservation. In order to provide complete information to decision makers, the determination of all alternate optimal solutions is necessary. This paper explores two procedures for finding all such solutions. We describe the formulation and motivation of each method. A computational analysis on a data set describing native terrestrial vertebrates in the state of Oregon illustrates the effectiveness of each approach. [ABSTRACT FROM AUTHOR]

This paper describes the implementation of a novel optimization approach for reducing in large data base for the Office of Tax Analysis. US Treasury Department. The model minimizes the loss of information which results from using a subset of the data base. rather than the entire file. The specific application involves the 1977 US Statistics of Income File for Individuals. This file was reduced in size from 155.212 weighted records to 74.762 weighted records by employing a subgradient optimization method that was specialized for extremely large-scale problems. Differences between the original and reduced data bases are presented. [ABSTRACT FROM AUTHOR]