Modeling using dynamic variables – An approach for the design of loss prevention programs

Records from The National Safety Council [National Safety Council, 2007. Safety Intervention Evaluation: A Systematic Approach. <http://www.acgih.org/events/ControlBand/Thomas_SafetyIntervention.pdf> (accessed 10.01.07)] have shown that in 2004 alone, on-the-job injuries to workers constituted 35% of total recorded injuries in the United States. This generated an associated cost of about $142.2 billion. Unfortunately, the safety intervention programs enforced at work places to mitigate such losses are driven mainly by intuition and experience of involved safety personnel. This paper details implementing a computer program to furnish safety personnel with an empirical basis for designing loss prevention programs based on historical safety data. The computer tool is driven by a dynamic mathematical model which adapts itself to variations in data patterns and explains the correlation between historical incident rates and corresponding resources committed to interventions. This study empowers the industry with a tool that is capable of forming the core of optimizing valuable human resource allocation in safety program designs.     

Minimization of socioeconomic disruption for displaced populations following disasters

In the aftermath of catastrophic natural disasters such as hurricanes, tsunamis and earthquakes, emergency management agencies come under intense pressure to provide temporary housing to address the large‐scale displacement of the vulnerable population. Temporary housing is essential to enable displaced families to reestablish their normal daily activities until permanent housing solutions can be provided. Temporary housing decisions, however, have often been criticized for their failure to fulfil the socioeconomic needs of the displaced families within acceptable budgets. This paper presents the development of (1) socioeconomic disruption metrics that are capable of quantifying the socioeconomic impacts of temporary housing decisions on displaced populations; and (2) a robust multi‐objective optimization model for temporary housing that is capable of simultaneously minimizing socioeconomic disruptions and public expenditures in an effective and efficient manner. A large‐scale application example is optimized to illustrate the use of the model and demonstrate its capabilities ingenerating optimal plans for realistic temporary housing problems.     

基于微粒群算法的供水管网抗震优化设计

以管网年费用折算值为优化目标、管网拓扑结构与管径为优化参数、管网节点最低可靠度为约束条件,建立了供水管网抗震优化设计模型。利用微粒群算法对这一模型进行了求解,该算法以管网作为微粒个体,通过不断地更新微粒的位置来搜索最优的管网结构,直到最后给出优化的管网结构。利用上述方法对一典型供水管网进行了抗震优化设计分析,给出了3种不同节点最低可靠度约束条件下的优化改造方案。     

基于贝叶斯随机评价方法的小城镇灾害易损性分析

为了准确评价小城镇灾害易损性所处的状态,进而为制定小城镇防灾减灾规划提供科学依据,提出了基于贝叶斯公式的小城镇灾害易损性随机评价方法。该方法通过计算小城镇灾害易损性单个指标属于某个评价级别的概率,由最大似然分类原则确定单个易损性指标的评价级别,进而采用最大加权概率原则推求其综合评价级别。通过实例计算分析,并与模糊评估方法、可拓物元评价方法进行比较验证,说明了该方法的可行性及其简单、实用的特点。     

基于基础设施性能的资产管理策略(英文)

基础设施的可持续发展需要系统地研究包括资产管理在内的基础设施管理的各个相关方面。本文构建了基于基础设施性能的资产管理策略的框架,并详细讨论了其主要原则与关键内容。     

基于概率分析的应急交通救援需求预测模型

在开展应急救援需求特征分析的基础上,将需求分布问题归为管控条件下的运输优化问题,借鉴运筹学思想,建立基于运输问题的应急救援需求分布模型。其次,引入应急期限要求、通行能力约束、路径阻抗动态时变等影响因素,对运输问题模型进行改进。为便于求解,进一步将模型转化为基于概率分析的救援需求分阶段优化模型,该模型将车辆出行时间视为服从正态分布的随机变量,利用出行时间方差的调整反映不同阶段路径阻抗的变化,能够在满足救援响应时间的前提下,解得运输成本最小的需求矩阵。案例研究证明这个模型能够满足应急期限要求并刻画路径阻抗的动态时变特征,达到了预期效果。     

三峡工程开县移民安置环境系统的多目标优化决策

着重探讨运用多目标优化决策方法处理三峡工程开县移民问题。根据各目标在优化中不同的重要性,采用线性加权构造评价函数的方法取代通用的单一约束规划方法,并选用人均口粮、农业产值、水土保持和森林面积作为决策目标,从而提出了一个在多个目标之间协调优化、综合决策的估算模式;对模型的有效性进行了分析,井测算出开县农业生态环境对移民安置的承载能力,为寻求移民安置的优化方案提供科学依据,以使移民得以长治久安。     

环境保护投资择优标准及其辅助决策模型研究

指出了环境保护投资的优化是区域内相对最优而非点最优。从阐明环保投资与环境质量和环境经济效益之间的关系特征出发,提出新的环保投资择优标准,并给出了依据这一标准的辅助决策模型用于分析环保投资相对优化的问题。      

离散型多准则最优化决策模型在NEQDSS中的应用

离散型多准则最优化决策模型(简称DMODM)是解决由有限决策变量离散取值生成的、评价准则为多个的一类决策优化问题的模型。与目前常见的决策模型或方法相比,该模型具有适应性强、交互式过程、吸收决策者的参与以及离散性和求解速度快等特点。本文简要地介绍了该模型的原理及其两种求解方法,并具体描述了国家环境质量决策支持系统(简称NEQDSS)选择该模型的依据、决策过程、具体数学模型的选择与匹配、决策控制变量确定与取值,以及决策目标确定和交互式过程等应用情况。最后对该模型在NEQDSS中的应用效果作了总结评价。     

Bayesian spatial prediction

This paper presents a complete Bayesian methodology for analyzing spatial data, one which employs proper priors and features diagnostic methods in the Bayesian spatial setting. The spatial covariance structure is modeled using a rich class of covariance functions for Gaussian random fields. A general class of priors for trend, scale, and structural covariance parameters is considered. In particular, we obtain analytic results that allow easy computation of the predictive distribution for an arbitrary prior on the parameters of the covariance function using importance sampling. The computations, as well as model diagnostics and sensitivity analysis, are illustrated with a set of precipitation data.