Adaptive Spatial Sampling of Contaminated Soil

Suppose that a residential neighborhood may have been contaminated by a nearby abandoned hazardous waste site. The suspected contamination consists of elevated soil concentrations of chemicals that are also found in the absence of site-related contamination. How should a risk manager decide which residential properties to sample and which ones to clean? This paper introduces an adaptive spatial sampling approach which uses initial observations to guide subsequent search. Unlike some recent model-based spatial data analysis methods, it does not require any specific statistical model for the spatial distribution of hazards, but instead constructs an increasingly accurate nonparametric approximation to it as sampling proceeds. Possible cost-effective sampling and cleanup decision rules are described by decision parameters such as the number of randomly selected locations used to initialize the process, the number of highest-concentration locations searched around, the number of samples taken at each location, a stopping rule, and a remediation action threshold. These decision parameters are optimized by simulating the performance of each decision rule. The simulation is performed using the data collected so far to impute multiple probable values of unknown soil concentration distributions during each simulation run. This optimized adaptive spatial sampling technique has been applied to real data using error probabilities for wrongly cleaning or wrongly failing to clean each location (compared to the action that would be taken if perfect information were available) as evaluation criteria. It provides a practical approach for quantifying trade-offs between these different types of errors and expected cost. It also identifies strategies that are undominated with respect to all of these criteria.

     

Selecting a Portfolio of Technologies: An Application of Decision Analysis

The Department of Energy is faced with the critical and complex decision of selecting technologies for waste site remediation. This research focuses on developing a decision support tool which assists the decision maker to find an optimal portfolio. A portfolio consists of selecting the appropriate processes for a remediation site and, subsequently, selecting a technology for each process so that the decision maker's objectives are achieved. The measures supporting the objectives are risk, life-cycle cost, and time required for remediation. The model uses exponential attribute utility functions with an additive objective function. The model provides the decision maker with estimates of the cost and time distributions, and their associated utility. Cumulative frequency distributions illustrate the dominance of technology choices and the variance in the results. The model permits sensitivity analysis in the form of rainbow and tornado diagrams to display the effects of changes in the values of the input variables. Overall, the model provides a generic technology selection tool that can be used to make better informed decisions and may be easily manipulated to reflect changes in the remediation process.     

A Strategic,Area-wide Contingency Planning Model for Oil Spill Cleanup Operations with Application Demonstrated to the Galveston Bay Area*

This paper develops a general integer programming model for strategic, area-wide contingency planning of oil spill cleanup operations. Model inputs include the set of risk points and the likely spill scenarios and response requirements for each, the sites of existing storage locations and the inventory of components at each, and potential sites for new storage locations. The model prescribes a minimum total cost plan to either build new storage locations, expand existing ones, or both, to purchase new components and pre-position them, and a contingency plan that determines which response systems should be composed to enable an effective time-phased response for each likely spill scenario. A family of heuristics based on linear programming (LP) is devised to resolve this strategic problem, providing an area-wide contingency plan. The heuristics are evaluated on a set of 10 test problems that involve 1869 general integer variables and 3264 constraints. Computational tests indicate that four heuristics are quite effective, prescribing solutions for each of 10 test cases within 1.41% of optimum and within a few minutes runtime. This study focused on modeling the Galveston Bay Area, and the test problems represent application in that area. A sensitivity analysis is demonstrated by assessing the impacts of component availability and the degradation of cleanup capability over time. Use of the model as a decision support aid by responsible parties, contractors, governmental organizations and others is described.     

Applying Decision Analysis to Determine the Effect of Smoke Detector Laws on Fire Loss in the United States

Decision analysis is used to examine whether residential smoke detectors should be required by law. Statistics pertaining to fire incidence, fire death, smoke detector efficacy, and the consequences of fire are examined for accuracy and availability and combined in a decision model. A sensitivity analysis is performed on the model inputs. Included in the analysis is the idea that homes with detectors may have lower death rates due to the characteristics of those who purchase detectors rather than the detectors themselves. Results from the base case estimate show an expected cost of $65,000 per life saved when a law is enacted. This value lies well below the value implied by spending on other public safety programs.     

When Costs Are Unequal and Unknown: A Subtree Grafting Approach for Unbalanced Data Classification*

In binary classifications, a decision tree learned from unbalanced data typically creates an important challenge related to the high misclassification rate of the minority class. Assigning different misclassification costs can address this problem, though usually at the cost of accuracy for the majority class. This effect can be particularly hazardous if the costs cannot be specified precisely. When the costs are unknown or difficult to determine, decision makers may prefer a classifier with more balanced accuracy for both classes rather than a standard or cost‐sensitively learned one. In the context of learning trees, this research therefore proposes a new tree induction approach called subtree grafting (STG). On the basis of a real bank data set and several other data sets, we test the proposed STG method and find that our proposed approach provides a successful compromise between standard and cost‐sensitive trees.     

Risk‐Based Decision Making for Reoccupation of Contaminated Areas Following a Wide‐Area Anthrax Release

This article presents an analysis of postattack response strategies to mitigate the risks of reoccupying contaminated areas following a release of Bacillus anthracis spores (the bacterium responsible for causing anthrax) in an urban setting. The analysis is based on a hypothetical attack scenario in which individuals are exposed to B. anthracis spores during an initial aerosol release and then placed on prophylactic antibiotics that successfully protect them against the initial aerosol exposure. The risk from reoccupying buildings contaminated with spores due to their reaerosolization and inhalation is then evaluated. The response options considered include: decontamination of the buildings, vaccination of individuals reoccupying the buildings, extended evacuation of individuals from the contaminated buildings, and combinations of these options. The study uses a decision tree to estimate the costs and benefits of alternative response strategies across a range of exposure risks. Results for best estimates of model inputs suggest that the most cost‐effective response for high‐risk scenarios (individual chance of infection exceeding 11%) consists of evacuation and building decontamination. For infection risks between 4% and 11%, the preferred option is to evacuate for a short period, vaccinate, and then reoccupy once the vaccine has taken effect. For risks between 0.003% and 4%, the preferred option is to vaccinate only. For risks below 0.003%, none of the mitigation actions have positive expected monetary benefits. A sensitivity analysis indicates that for high‐infection‐likelihood scenarios, vaccination is recommended in the case where decontamination efficacy is less than 99.99%.     

A multicriteria decision making model for reverse logistics using analytical hierarchy process

Product recovery activities such as recycling, refurbishing and direct reuse are becoming integral to manufacturing supply chains. This study presents a multicriteria decision making model for reverse logistics using analytical hierarchy process (AHP). The AHP model evaluates a hierarchy of criteria and subcriteria, including costs and business relations, for critical decisions regarding network design. Using sensitivity analysis with AHP, the work provides insights into the preference ordering among eight alternative network configurations. For instance, the choice of test sites is largely dependent on the potential for cost savings on testing procedures and transportation of scrap, and this decision is not sensitive to the importance of business relations. By contrast, the choice of collection sites is largely determined by the relative importance of business relations considerations vs. cost considerations. As well, the processing location decision favors a third-party reprocessor if there is little need to protect proprietary product knowledge and cost savings is very important. The model is demonstrated using three case studies of real-world applications.     

Where Deer Roam: Chronic Yet Acute Site Exposures Preclude Ecological Risk Assessment

Global positioning system (GPS) technology has made possible the detailed tracking of the spatial movements of wildlife. Using GPS tracking collars placed on female white‐tailed deer (Odocoileus virginianus) over a protracted period, we illustrate that this species is spatially irrelevant for consideration in ecological risk assessments (ERAs) for commonly assessed contaminated sites. Specifically, deer movements do not allow for a sufficiency of chemical exposures to occur such that toxicological endpoints would be triggered. Deer are spatially irrelevant not only because their home ranges and overall utilized areas dwarf prototypical hazardous waste sites. They are also inappropriate for assessment because they only minimally contact reasonably sized preferred locations, this while demonstrating a confounding high degree of site affinity for them. Our spatial movements analysis suggests that deer introduce the ERA novelty of a species displaying elements of both chronic and acute site exposure. We further suggest that other large commonly assessed high‐profile mammals could also be found to be spatially irrelevant for ERAs were they to submit to the GPS tracking and subsequent data analysis we performed for the deer. Recognizing that certain receptors need not be considered in ERAs can help to simplify the ERA process.     

Production and Sales Planning in Capacitated New Product Introductions

How should a firm with limited capacity introduce a new product? Should it introduce the product as soon as possible or delay introduction to build up inventory? How do the product and market characteristics affect the firm's decisions? To answer such questions, we analyze new product introductions under capacity restrictions using a two‐period model with diffusion‐type demand. Combining marketing and operations management decisions in a stylized model, we optimize the production and sales plans of the firm for a single product. We identify four different introduction policies and show that when the holding cost is low and the capacity is low to moderate, a (partial) build‐up policy is indeed optimal if consumers are sensitive to delay. Under such a policy, the firm (partially) delays the introduction of its product and incurs short‐term backlog costs to manage its future demand and total costs more effectively. However, as either the holding cost or the capacity increases, or consumer sensitivity to delay decreases, the build‐up policy starts to lose its appeal, and instead, the firm prefers an immediate product introduction. We extend our analysis by studying the optimal capacity decision of the firm and show that capacity shortages may be intentional.     

基于碳排放的废弃产品回收站点分布研究

基于碳税价格对废弃产品回收运营决策的影响考虑，本文从整个社会的视角，把顾客、回收站点、回收总站作为一个整体进行考虑，并分析碳税价格对回收站点数量和成本的影响。首先，将顾客、回收站点和回收处理中心作为整体，考虑能源成本、运营成本和碳排放费用，建立废弃产品回收站点分布的社会总成本模型。接着，对碳税价格为零和碳税价格无穷大两种极端情况，分析了回收站点的数量、碳税价格、社会总成本等因素之间的关系。最后对模型进行数值计算。结果表明：当前碳税价格下，同时考虑碳税成本和运营费用或仅考虑运营费用这两种情况计算出来的回收站点数量相等，单位总成本区别不显著，即碳税价格对运营影响不大；若仅考虑碳税成本，则回收站点数量变化较大，即合理的碳税价格对运营影响显著；碳税价格理想的定价应该在40.03至66.46之间；回收站点数量为66时，碳排放量最优。     

网络环境下消费者感知效用模型的构建与研究

商品效用的大小是消费者购买决策的出发点,论文以感知效用为视角,分析了网络消费者的非补偿性决策和补偿性决策,构建了网络消费者感知效用模型,并以此为基础,深入分析了信息收集成本、质量评估成本、商品价格以及购物风险等成本变量对网络购物环境下消费者感知效用的影响。研究结果表明,无论是风险规避型网络消费者,还是风险型网络消费者,商品质量的提高均可以增大消费者感知效用,而商品价格、购物风险的增大将导致消费者感知效用减小,但对于风险型网络消费者,由于承受风险能力强,商品价格的升高并没有增大消费者心理风险成本,而对于风险规避型网络消费者,随着商品价格的升高,消费者心理风险成本相应增大。     

Ecological Risks to Fossorial Vertebrates from Volatile Organic Compounds in Soil

The past several years has seen an increased awareness of the need to conduct ecological risk assessments (ERAs) for hazardous waste sites. One technique used in ERAs involves estimating contaminant exposure to individual animals of selected species, which is then compared to a reference dose derived from the literature. Exposure estimates are conducted on those species which are representative of the different trophic levels found at the site. In many terrestrial systems, fossorial (burrowing) vertebrates are found in both lower and upper trophic levels. As part of the ERA conducted for Site 300, Lawrence Livermore National Laboratory's high-explosive test facility, contaminant exposures were estimated for fossorial and nonfossorial vertebrates spanning two trophic levels. The results of the evaluation revealed that a significant pathway by which fossorial vertebrates could be exposed to trichloroethylene in soil was through the inhalation of contaminated subsurface burrow air. This was the first time that the importance of this ecological exposure pathway has been recognized. The results of this analysis suggest that further research into the ecological significance of subsurface burrow air contaminated with volatile organic compounds is warranted.     

Strategies for Addressing the Nursing Shortage: Coordinated Decision Making and Workforce Flexibility

In this article, we present strategies to help combat the U.S. nursing shortage. Key considerations include providing an attractive work schedule and work environment—critical issues for retaining existing nurses and attracting new nurses to the profession—while at the same time using the set of available nurses as effectively as possible. Based on these ideas, we develop a model that takes advantage of coordinated decision making when managing a flexible workforce. The model coordinates scheduling, schedule adjustment, and agency nurse decisions across various nurse labor pools, each of differing flexibility levels, capabilities, and costs, allowing a much more desirable schedule to be constructed. Our primary findings regarding coordinated decision making and how it can be used to help address the nursing shortage include (i) labor costs can be reduced substantially because, without coordination, labor costs on average are 16.3% higher based on an actual hospital setting, leading to the availability of additional funds for retaining and attracting nurses, (ii) simultaneous to this reduction in costs, more attractive schedules can be provided to the nurses in terms of less overtime and fewer undesirable shifts, and (iii) the use of agency nurses can help avoid overtime for permanent staff with only a 0.7% increase in staffing costs. In addition, we estimate the cost of the shortage for a typical U.S. hospital from a labor cost perspective and show how that cost can be reduced when managers coordinate.     

Effectiveness and Efficiency of Controlling Campylobacter on Broiler Chicken Meat

Campylobacter bacteria are an important cause of foodborne infections. We estimated the potential costs and benefits of a large number of possible interventions to decrease human exposure to Campylobacter by consumption of chicken meat, which accounts for 20-40% of all cases of human campylobacteriosis in the Netherlands. For this purpose, a farm-to-fork risk assessment model was combined with economic analysis and epidemiological data. Reduction of contamination at broiler farms could be efficient in theory. However, it is unclear which hygienic measures need to be taken and the costs can be very high. The experimental treatment of colonized broiler flocks with bacteriophages has proven to be effective and could also be cost efficient, if confirmed in practice. Since a major decrease of infections at the broiler farm is not expected in the short term, additional measures in the processing plant were also considered. At this moment, guaranteed Campylobacter-free chicken meat at the retail level is not realistic. The most promising interventions in the processing plant are limiting fecal leakage during processing and separation of contaminated and noncontaminated flocks (scheduling), followed by decontamination of the contaminated flock. New (faster and more sensitive) test methods to detect Campylobacter colonization in broilers flocks are a prerequisite for successful scheduling scenarios. Other methods to decrease the contamination of meat of colonized flocks such as freezing and heat treatment are more expensive and/or less effective than chemical decontamination.     

An Analysis of the Portfolio of Sites to Characterize for Selecting a Nuclear Repository

The U.S. Department of Energy has selected three sites, from five nominated, to characterize for a nuclear repository to permanently dispose of nuclear waste. This decision was made without the benefit of an analysis of this "portfolio" problem. This paper analyzes different portfolios of three sites for simultaneous characterization and strategies for sequential characterization. Characterization of each site, which involves significant subsurface excavation, is now estimated to cost $1 billion. Mainly because of the high characterization costs, sequential characterization strategies are identified which are the equivalent of $1.7-2.0 billion less expensive than the selected DOE simultaneous characterization of the three sites. If three sites are simultaneously characterized, one portfolio is estimated to be the equivalent of $100-400 million better than the selected DOE portfolio. Because of these potential savings and several other complicating factors that may influence the relative desirability of characterization strategies, a thorough analysis of characterization strategies that addresses the likelihood of finding disqualifying conditions during site characterization, uncertainties, and dependencies in forecast site repository costs, preclosure and postclosure health and safety impacts, potential delays of both sequential and simultaneous characterization strategies, and the environmental, socioeconomic, and health and safety impacts of characterization activities is recommended.     

Assessing the Benefits and Costs of Homeland Security Research: A Risk-Informed Methodology with Applications for the U.S. Coast Guard

This article describes a methodology for risk-informed benefit–cost analyses of homeland security research products. The methodology is field-tested with 10 research products developed for the U.S. Coast Guard. Risk-informed benefit–cost analysis is a tool for risk management that integrates elements of risk analysis, decision analysis, and benefit–cost analysis. The cost analysis methodology includes a full-cost accounting of research projects, starting with initial fundamental research costs and extending to the costs of implementation of the research products and, where applicable, training, maintenance, and upgrade costs. The benefits analysis methodology is driven by changes in costs and risks leading to five alternative models: cost savings at the same level of security, increased security at the same cost, signal detection improvements, risk reduction by deterrence, and value of information. The U.S. Coast Guard staff selected 10 research projects to test and generalize the methodology. Examples include tools to improve the detection of explosives, reduce the costs of harbor patrols, and provide better predictions of hurricane wind speeds and floods. Benefits models and estimates varied by research project and many input parameters of the benefit estimates were highly uncertain, so risk analysis for sensitivity testing and simulation was important. Aggregating across the 10 research products, we found an overall median net present value of about $385 million, with a range from $54 million (5th percentile) to $877 million (95th percentile). Lessons learned are provided for future applications.     

逆向供应链中电子废弃产品回收定价和补贴策略研究

随着废弃电子产品的大量涌现和社会环保意识的增强,废弃电子产品的回收处理成为社会关注的热点问题之一。在现有的文献研究中,更多的是从逆向供应链系统化的视角关注价格或税收政策对回收商或处理商的运营效率、积极性的影响,逆向供应链激励的关键问题即如何确定回收价格和补贴方面的研究却较少见。本文基于社会福利最大化的视角,建立斯塔克伯格博弈模型（社会福利模型）来确定逆向供应链分散管理中的回收价格和社会最优补贴费,该模型可以检验分散管理系统（环保局作为领导者、MIS和回收商作为追随者）中回收逆向供应链的回收价格和补贴的激励效应。通过社会福利模型和资金平衡模型的比较,以及比较案例的数值演算,结果发现：达到市场最优销售数量和向顾客提供回收废弃电子产品的最优奖金的状况下,销售商和回收商处于平衡状态;社会福利模型中窒息价格、零奖金对应的基准收集数量、相对于奖金的收集量敏感度等因素对社会福利的影响均优于资金平衡模型。     

The Lost Value of Groundwater and its Influence on Environmental Decision Making

A critical aspect in establishing environmental policies lies in the proper assessment of the value of the resource being affected. Standard risk assessment analyses calculate the cost of pollution as consisting, solely, of the cost to remediate a site. This traditional definition is extended here to include the lost value of groundwater. These concepts and their impact on decision-making analyses are illustrated through the case of municipal waste landfills. Based on data from existing polluting sites, a simple cost-benefit probabilistic analysis is conducted first, which equates, as is the practice, the cost of pollution to that of remediation. This leads rationally to selection of the lowest-protection technology. Using plausible arguments the reduction in value of groundwater from potable high-quality water to irrigation water, which is what is returned after remediation, is argued. The arguments consist of: (a) the ratio of the subsidized prices of drinking to irrigation water reflects the relative value of the use of water; (b) the amount paid for remediation, in each case, represents, at a minimum, the value of the water recovered; and (c) the lost value of groundwater equals the value of drinking water minus the value of irrigation water. Incorporation of this lost value of groundwater is sufficient to drastically alter the conclusions of the decision-making analysis and make the highest level technology the most rational and profitable alternative. The broader point of this article lies in that proper accounting of environmental costs is necessary in order to alter environmental policies and practices.     

The Tradeoffs Associated with Rerouting Highway Shipments of Hazardous Materials to Minimize Risk

Recent legislative and regulatory activities at the federal level have focused attention on the highway routing of hazardous materials. The question is whether routes that minimize the risk of release accidents (i.e., the expected number of persons impacted by releases of hazardous materials) should be used in lieu of the routes that have the lowest operating costs. This policy issue is addressed for interstate shipments by using a national network model to determine the practical route and minimum risk route between each of 100 different origin-destination pairs (state capitals). The resulting cost-risk tradeoffs are then used to estimate the average cost of rerouting per fatality averted, the value of which turns out to be within the range of values for a number of familiar existing regulations.     

A Simulation of Partial Information Use in Decision Making: Implications for DSS Design

Information matrices are often the output produced by a decision support system. These matrices are a common method for expressing a decision situation under different decision-making scenarios. The decisions involved in designing a decision support system to generate the information matrix are important and involve several cost and benefit components. A designer needs guidance in making effective design decisions in this context. Such guidance can be provided by considering the relationships among specific design decisions, costs, and benefits. The general objective of this study is to provide a comprehensive framework for this purpose. This study is the first to develop and present a comprehensive cost-benefit framework for evaluating design decisions for a variety of scenarios. The specific objective of this research is to provide guidance regarding the number of available information dimensions to incorporate in a computer-based decision aid. Simulation experiments are conducted with a completely specified model based on the cost-benefit framework (including needed assumptions) to evaluate how many information dimensions to include for a specific information matrix size to achieve a balance between information use costs and decision quality. Based upon extensive simulation analyses for a hypothetical decision maker, the practical guideline found for designers is to include only the top half of the relevant information dimensions in any specific decision support system. Over a large number of repeating choice decisions, the savings in cognitive effort and information gathering costs clearly offset relatively minor losses in decision quality.