影响矩阵法在施桥大桥吊杆二次调索中的应用

在系杆拱桥施工过程中,为增大结构的安全储备并保证主梁受力安全,通常对吊杆采用两次张拉,最终达到设计成桥索力,由此提出二次调索施调索力、顺序的计算问题。以影响矩阵理论为基础,建立了二次调索最优控制的数学模型,采用惩罚函数法求解,该方法计算精确、简便、实用,也可用于成桥后索力的误差调整。     

基于最优化理论的机步师联合火力打击目标价值评估

根据信息化条件下机步师城市进攻战斗联合火力打击特点,以最优化理论为基础,对战场联合火力打击目标价值进行了分析和排序,并通过实例对该模型的有效性和实用性进行了验证,为指挥员的决策行为提供了理论依据。     

一种基于多源地面目标的相关判断方法

多源地面目标相关判断是进行地面目标融合处理的前提。在分析地面目标特征、知识库及相关处理流程的基础上,设计了依据来源级别、相关关系、目标类型和位置门限等方面进行相关判断的步骤,并重点阐述了根据目标距离进行位置门限判断的算法。力争达到能够快速准确地缩小判断范围、减少判断步骤、确定相关候选对象。     

反应系数法分析围壁空气层对舱室热环境的影响

反应系数法是空调动态负荷计算的常用方法,该文通过对四种围壁结构的反应系数进行对比分析,表明带有一定厚度空气层的情况对外扰波动影响更小,对保温有利,同时,围壁结构排列次序对反应系数也有影响。     

水泥混凝土桥面铺装状况评价及养护决策分析

高速公路桥梁的养护对保证车辆快速安全行驶具有重要意义。文章在对高速公路桥病害调研统计分析的基础上,对桥面铺装病害进行分类和定义,通过对桥面铺装状况进行评价,确定了桥面铺装养护决策阶段和流程,并结合应用实例,验证了该养护决策的可行性。     

Estimation of mean and covariance of stochastic multi-class OD demands from classified traffic counts

This paper proposes a new model to estimate the mean and covariance of stochastic multi-class (multiple vehicle classes) origin–destination (OD) demands from hourly classified traffic counts throughout the whole year. It is usually assumed in the conventional OD demand estimation models that the OD demand by vehicle class is deterministic. Little attention is given on the estimation of the statistical properties of stochastic OD demands as well as their covariance between different vehicle classes. Also, the interactions between different vehicle classes in OD demand are ignored such as the change of modes between private car and taxi during a particular hourly period over the year. To fill these two gaps, the mean and covariance matrix of stochastic multi-class OD demands for the same hourly period over the year are simultaneously estimated by a modified lasso (least absolute shrinkage and selection operator) method. The estimated covariance matrix of stochastic multi-class OD demands can be used to capture the statistical dependency of traffic demands between different vehicle classes. In this paper, the proposed model is formulated as a non-linear constrained optimization problem. An exterior penalty algorithm is adapted to solve the proposed model. Numerical examples are presented to illustrate the applications of the proposed model together with some insightful findings on the importance of covariance of OD demand between difference vehicle classes.     

Exploring public transport sustainability with neutrosophic logic

Sustainable transportation is a significant component of overall sustainable development. Increasingly, evaluation and decision-making with respect to major complex projects (for example, transportation and land use projects), a multiple attribute perspective is taken. This paper illustrates a multiple attribute decision-making approach for selecting sustainable public transportation systems under uncertainty, that is, with partial or incomplete information represented by single-valued neutrosophic sets (SVNSs). A SVNS is a generalization of a classical set, a fuzzy set, and an intuitionistic fuzzy set. Here, SVNSs and SVNS connectives are illustrated in the context of a ‘Public Transit Sustainable Mobility Analysis Tool’ (PTSMAT) which involves a composite (multiple attribute) sustainability index. A case study of PTSMAT is provided for the UBC Corridor study in Vancouver, Canada. As expected, similar results are obtained to the original study, though the neutrosophic formalism opens a wide range of possibilities for recognition of uncertainty in sustainability assessment.     

A methodology to control urban traffic noise under the constraint of environmental capacity: A case study of a double-decision optimization model

This paper presents a novel methodology to control urban traffic noise under the constraint of environmental capacity. Considering the upper limits of noise control zones as the major bottleneck to control the maximum traffic flow is a new idea. The urban road network traffic is the mutual or joint behavior of public self-selection and management decisions, so is a typical double decision optimization problem.The proposed methodology incorporates theoretically model specifications. Traffic noise calculation model and traffic assignment model for O–D matrix are integrated based on bi-level programming method which follows an iterated process to obtain the optimal solution. The upper level resolves the question of how to sustain the maximum traffic flow with noise capacity threshold in a feasible road network. The user equilibrium method is adopted in the lower layer to resolve the O–D traffic assignment.The methodology has been applied to study area of QingDao, China. In this illustrative case, the noise pollution level values of optimal solution could satisfy the urban environmental noise capacity constraints. Moreover, the optimal solution was intelligently adjusted rather than simply reducing the value below a certain threshold. The results indicate that the proposed methodology is feasible and effective, and it can provide a reference for a sustainable development and noise control management of the urban traffic.     

指挥决策问题的可拓模型研究

针对指挥决策中含有的大量矛盾、不相容问题,寻求可能的解决途径。引入可拓学理论和方法,采取物元变换思想,对决策系统进行相容度分析,将其中的矛盾、不相容问题转换为相容问题进行处理,最后根据评价准则对方案进行损耗和效果评价。如果决策结果满足决策的目标与要求,就作为决策方案。编队防空仿真实验进一步验证了模型的可行性和合理性,为解决指挥决策中的对立问题提供了依据。     

A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul

This paper provides a hierarchical customer satisfaction framework to measure rail transit lines’ performances in Istanbul. The problems related to rail transit line systems are addressed via customer satisfaction surveys. Then, a framework is proposed combining statistical analysis, fuzzy analytic hierarchy process, trapezoidal fuzzy sets and Choquet integral to evaluate customer satisfaction levels. Next, the criteria need to be improved are determined and specific recommendations to enhance the operation for specific lines are suggested. The proposed framework provides directions for the future investments and it also can be used at a more macroscopic level to determine the operational deficiencies. Furthermore, it can be generalized and applied to complex decision making problems that include uncertain and subjective data or vague information.