基于D-S证据理论的舰船技术状态评估方法

在简要叙述船舶技术状态评估现状的基础上,提出了基于D-S证据理论的舰船技术状态评估方法。介绍了船舶技术状态评估的重要性和发展现状,以及可处理由不知道所引起的不确定性的D-S证据理论和基于D-S证据理论的决策方法;通过实例详细阐述如何用D-S证据理论进行舰船技术状态评估。实例证明,通过应用D-S证据理论,提高了舰船技术状态评估的准确性和可靠性,降低了评估的不确定性。     

考虑需求预测不确定性的交通网络设计

为分析需求预测不确定性对交通网络设计的影响,根据需求预测结果的不同类型,建立了适用于连续型需求的基于灵敏度分析的交通网络设计模型和适用于离散型需求的基于未来情形预测的交通网络设计模型。模型均由两项组成,前一项表示网络效益,后一项表示网络稳定性。给出了两类模型的构造过程,探讨了模型的求解方法与适用范围。计算结果表明:随着设计参数变小,网络效益变小,网络稳定性变大,进而得到不同的网络设计结果。可见,考虑需求不确定性的模型能准确地反映实际的交通网络设计目标。     

不确定网络设计模型研究综述

传统的交通规划以确定性假设和决策准则为依据,导致规划方案存在不合理因素。在不确定性理论的基本前提下,以双层优化模型为基础,以建立不确定性交通网络设计理论为研究目标,系统综述了在供给需求不确定、决策者和用户风险态度以及时间维度方面的拓展。最后,给出了不确定网络设计模型的一般框架。     

一种可用于鱼雷导引的高冲突数据融合处理方法

目标释放诱饵等对抗装备对鱼雷武器实施干扰、诱骗等可增加其逃生的机会,精确制导及智能识别技术仍将是鱼雷制导技术发展的方向之一。鱼雷的精确制导及智能识别技术需要以传感器获取目标不同的特征数据或同性周期数据为判断,并将这些数据融合。论文针对处理多信息源数据高度冲突时的问题,提出了一种改进的数据合成方法。该方法可应用于鱼雷目标识别时数据高冲突的处理,减小识别结果的不确定性。     

Pick‐up locations and bus allocation for transit‐based evacuation planning with demand uncertainty

This paper develops a decision‐support model for transit‐based evacuation planning under demand uncertainty. Demand uncertainty refers to the uncertainty associated with the number of transit‐dependent evacuees. A robust optimization model is proposed to determine the optimal pick‐up points for evacuees to assemble, and allocate available buses to transport the assembled evacuees between the pick‐up locations and different public shelters. The model is formulated as a mixed‐integer linear program and is solved via a cutting plane scheme. The numerical example based on the Sioux Falls network demonstrates that the robust plan yields lower total evacuation time and is reliable in serving the realized evacuee demand. Copyright © 2012 John Wiley & Sons, Ltd.     

A multi‐class mean‐excess traffic equilibrium model with elastic demand

Recent empirical studies have revealed that travel time variability plays an important role in travelers' route choice decisions. To simultaneously account for both reliability and unreliability aspects of travel time variability, the concept of mean‐excess travel time (METT) was recently proposed as a new risk‐averse route choice criterion. In this paper, we extend the mean‐excess traffic equilibrium model to include heterogeneous risk‐aversion attitudes and elastic demand. Specifically, this model explicitly considers (1) multiple user classes with different risk‐aversions toward travel time variability when making route choice decisions under uncertainty and (2) the elasticity of travel demand as a function of METT when making travel choice decisions under uncertainty. This model is thus capable of modeling travelers' heterogeneous risk‐averse behaviors with both travel choice and route choice considerations. The proposed model is formulated as a variational inequality problem and solved via a route‐based algorithm using the modified alternating direction method. Numerical analyses are also provided to illustrate the features of the proposed model and the applicability of the solution algorithm. Copyright © 2012 John Wiley & Sons, Ltd.     

需求不确定下的网约车司机最优工资水平研究

在网约车出行系统中,乘客出行需求的不确定性会影响网约车平台制定合理的工资水平, 进而影响平台利润。本文研究出行需求不确定下的网约车司机工资水平。根据问题的数学描述,假设实际出行需求服从随机分布,司机数量供给服从劳动力供给理论,采用网约车研究领域广泛采纳的精准乘客-司机匹配函数描述乘客与司机间的匹配关系。在此基础上,建立求解网约车平台利润最大化的模型框架。案例研究发现：存在最优的工资水平,使得网约车平台利润最大化;最优工资水平、司机人数与乘客最大需求正相关,最优工资水平与平台影响力负相关;本模型给出期望利润最大化下的最优解,比平均值模型具有一定的鲁棒性,有效保障网约车平台利润。 结果表明,本文建立的最优工资水平模型可以有效研究需求不确定性下的网约车出行系统。     

网联车辆编队的鲁棒模型预测控制方法

为研究一类带有结构不确定干扰的网联车辆编队控制问题,考虑结构扰动服从盒式不确定集约束情况,提出一种基于鲁棒对等变换的模型预测编队控制方法。首先,基于车辆运动学状态方程,对传统车辆编队控制模型进行描述;其次,在状态方程中引入仿射结构不确定矩阵,构建最小最大化范式下的网联车辆编队控制模型;然后,在盒式不确定集下,基于鲁棒对等理论将该模型转化为计算上易于处理的上图优化模型;最后,采用序列二次规划算法获得最优编队控制方案,并通过仿真实验将该控制方案与传统车辆编队控制方案进行对比,以此验证模型的有效性。 结果表明：本文提出的最优编队控制方法能够抵御编队系统结构不确定干扰,在实现稳定编队目标的同时保障车辆的编队安全性。     

An adaptive neural network-fuzzy linear regression approach for improved car ownership estimation and forecasting in complex and uncertain environments: the case of Iran

This paper applies a novel adaptive approach consisting of Artificial Neural Network (ANN) and Fuzzy Linear Regression (FLR) to improve car ownership forecasting in complex, ambiguous, and uncertain environments. This integrated approach is applied to forecast car ownership in Iran from 1930 to 2007. In this study, the level of car ownership is viewed as the result of demographic, politico-social, and urban structure factors including average family size, total population density, urban population density, urbanization rate, gross national product per capita, gasoline price, and total road length. To capture the potential complexity, uncertainty, and linearity relation between the car ownership function and its determinants, ANN and FLR (including eight well-known FLR) approaches are applied to the collected data. Next, the preferred ANN is selected based on sensitivity analysis results for the test data while the preferred FLR is identified with regard to ANOVA and MAPE results. The results obtained from the performance comparison demonstrate the considerable superiority of the preferred ANN over the preferred FLR regarding the nonlinear and complex nature of the car ownership function in Iran. This is the first study that presents an ANN-FLR approach for car ownership forecasting capable of handling complexity and non-linearity, uncertainty, pre-processing, and post-processing.     

基于ANSYS的少片变截面钢板弹簧可靠性分析

在ANSYS软件中建立某少片变截面铜板弹簧的有限元模型,通过分析结果与实验结果的对比,验证了模型的正确性。考虑多种不确定性因素的影响,应用ANSYS中提供的概率分析系统,采用蒙特卡罗法模拟抽样,对该钢板弹簧进行概率有限元可靠性分析。