Transportation agencies experiences with decision support systems for airport ground access planning

This article reports on a field investigation into the ways that transportation agencies use quantitative and qualitative information for making strategic decisions regarding airport ground access. The study analyzes the value of this information for planning airport ground access improvements at seven major international airport sites.The major finding of the research is that quantitative modeling for strategic decision support is very difficult, costly and time consuming. Modelers are confident that the models are accurate and reliable but executives generally lack confidence in the results. Transportation officials believe that the information supplied is flawed by a number of defects that minimize its value for strategic decision makers. The information defects described in this article provide an analysis of the structural difficulty of using quantitative modeling for transportation problems of strategic importance. To date, qualitative information is not frequently used, but some transportation agencies are considering its application to designing transportation services. Although this study is limited to airport ground access, the authors feel that this evidence, in conjunction with the evidence from other studies in the transportation area, dictates a need for wariness in the development of decision support systems for transportation planners. Developers of decision support systems for transportation planners must be aware of modeling costs and defects and consider how to improve the timeliness, relevance and credibility of information quantitative models provide transportation executives. Fundamentally it is important to recognize that decision makers tend, either singularly or in concert with other individuals or groups, to be the champions of a long-term vision for the community. When modeling produces inconsistent or wide ranging results that contradict their position, decision makers may not only discard modeling activities, but lose confidence in the models altogether. As a consequence, transportation planners are faced with the challenge of how to improve quantitative modeling. The most reliable and effective means for improvement is incorporation of qualitative techniques which provide greater understanding of customer perceptions and human behavior.     

精心组织 科学管理 全力打造“太长”精品工程

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关于海事行政执法自由裁量权的几点思考

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制造、维修质量的有效评估

结合减速顶的生产、维修实际，分析基于模糊综合评价理论进行减速顶的制造、维修质量评估的必要性，同时建立模糊评价的数学模型，并对实例进行模型设计和计算，为减速顶生产、运用、维修质量评估提供一条新思路。     

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决策树法在施工合同管理中的应用

决策树法是投标决策阶段常用的一种定量分析方法，但在施工阶段应用较少，此文介绍它的操作原理，并举例阐述在项目实施阶段合同管理方面的应用。     

基于模糊投影决策模型的铁路客运专线融资结构优化研究

通过对融资结构优化决策特点的分析，指出铁路客运专线融资结构优化决策实际上是复杂的多目标决策问题。在此基础上，建立铁路客运专线融资结构优化决策递阶结构模型、决策指标组合权重熵值法与专家主观意见相结合的计算模型以及模糊投影决策评价模型；并编制了决策系统和分析软件；给出了应用实例进行验证，证明此文建立的评价模型能充分利用信息，对表面上看起来复杂的方案进行优劣次序排列，且能使理论和经验结合起来。排序结果能够比较客观公正地反映我国铁路客运专线融资结构的实际情况，可为铁路客运专线的融资结构提供指标评价及方案优选服务，避免因主观原因而造成的决策偏差。     

不完全信息下的CGF主体决策行为及模型研究

着重分析了决策的风险特性,建立了CGF主体的决策行为模型。分析加入感知行为后对决策行为复杂性的影响;提出了一种不确定型决策方法用于处理不完全感知信息对决策行为的影响;最后用示例说明了该方法的有效性。     

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The price of uncertainty in pavement infrastructure management planning: An integer programming approach

Currently there is a true dichotomy in the pavement maintenance and rehabilitation (M&R) literature. On the one hand, there are integer programming-based models that assume that parameters are deterministically known. On the other extreme, there are stochastic models, with the most popular class being based on the theory of Markov decision processes that are able to account for various sources of uncertainties observed in the real-world. In this paper, we present an integer programming-based alternative to account for these uncertainties. A critical feature of the proposed models is that they provide – a priori – probabilistic guarantees that the prescribed M&R decisions would result in pavement condition scores that are above their critical service levels, using minimal assumptions regarding the sources of uncertainty. By construction of the models, we can easily determine the additional budget requirements when additional sources of uncertainty are considered, starting from a fully deterministic model. We have coined this additional budget requirement the price of uncertainty to distinguish from previous related work where additional budget requirements were studied due to parameter uncertainties in stochastic models. A numerical case study presents valuable insights into the price of uncertainty and shows that it can be large.