CME, meanwhile, has developed a scenario-based model designed to take into account systemic and idiosyncratic risk factors.
与此同时，芝加哥商品交易所开发了一种基于场景的模型，设计它的目的时计算系统风险和异常风险。
The sample scenario is based on the connected personal health model, which extends healthcare treatment beyond traditional clinical Settings into the patient's home.
样例场景基于连接的个人健康模型，后者对医疗治理进行了扩展，超越了传统的临床模式，进入到患者的家里。
This scenario is a more traditional model to replace Windows - with a direct competitor, instead of creating a web-based replacement.
这一设想是取代Windows的比较传统的模式——作为一个直接的竞争者，而不是创建一个基于Web的替代品。
Uncertain demands are described as a scenario set with certain probability, the supply chain operation model is constructed by using the robust linear programming method based on scenario analysis.
采用具有已知概率的离散情景描述顾客需求的不确定性，利用基于情景分析的鲁棒线性优化方法建立供应链的运作模型。
This article presents attack tree model to describe the attacking scenario. Based on the principle of attack tree model, an attack specification language is constructed.
该文采用攻击树模型来模拟攻击行为，并提出了一个基于攻击树模型原理的攻击规范语言。
This paper proposes the typical scenario three network integration capability plan model based on the capability estimate and the indoor audio model.
本文根据容量估算和室内分布系统话务模型提出了典型场景下的三网融合容量规划模型。
The HTA model was used to compose the story around the event of a phlebotomist drawing a patient's blood that supports the goal-based structure of the scenario.
HTA模型被用来围绕医师抽取一个病人血样的事件组合故事，来支撑典型场景的基于目标的结构。
Based on the integrated total cost of both buyer and supplier, a lot-splitting model with cooperative game in a simple single buyer, single supplier, single product scenario is developed.
从供需双方的综合总成本出发，以一个供应商一个订货商单一产品为背景，开发了一个供应链中供需双方合作博弈下的批量分解模型，确定了最优订货批量、发运次数和发运数量。
We formulated scenario based multi-objective stochastic programming model to describe the problem of capacity planning under uncertainty and applied improved Multi-Objective PSO (MOPSO) to solve it.
提出了基于场景的多目标随机规划模型来构建不确定市场需求环境下的能力计划问题模型，并用改进的多目标粒子群优化算法求解。
We formulated scenario based multi-objective stochastic programming model to describe the problem of capacity planning under uncertainty and applied improved Multi-Objective PSO (MOPSO) to solve it.
提出了基于场景的多目标随机规划模型来构建不确定市场需求环境下的能力计划问题模型，并用改进的多目标粒子群优化算法求解。
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