南京大胜关长江大桥CPⅢ点位随温度变化的模型研究

以京沪高铁南京大胜关长江大桥CPⅢ高精密测量为工程背景,经过坐标变换后计算得出各个点位的膨胀值,利用概率与统计理论分析处理,得到可用于建模的点对数据(ΔS,ΔT)。根据一定的假设条件,建立了测量点位的位移随环境温度变化的线性回归模型,从而得出大桥CPⅢ位置POS与温度T之间的关系。利用该模型修正并评价测量数据,结果表明其较差完全符合要求,区间预测模型可用。并编程实现了CPⅢ点位位置随环境温度变化的自动化预测,能够方便地为大桥的变形观测、定期复测和实际胀缩效果分析提供一定的参考。     

科学发展模式对经济效益影响的几点建议

落实科学发展观,采用科学发展模式,关键在于结合实际、统筹兼顾、协调发展,将信心和动力通过实际工作转化为经济效益。此文以施工管理和节约能耗为例,阐述大型工程建设应优化资源配置,按施工组织设计配备各类人员,各类材料要按规领取,节约使用和有序囤放。工程管理中应根据建设规模合理选定现场检测点,以节约路途时间和能耗,增加经济效益。     

多货物品种集装化运输的优化调度问题

用数学模型描述了供应商根据某一用户在各个不同时间段内对不同货物的需求量情况,利用各种不同型号的箱体进行分时段货物发送的问题。其最终目的是为了求得能够使运输成本和存储成本达到最小的货物发送方案,该问题是一个由批量问题和拼箱问题所组成的复合问题。就此问题,提出了一种三阶段算法。通过理论分析,该算法可以有效地解决此类问题。     

轿车前梁有限元分析

利用轿车前梁的CATIA模型建立其有限元模型。通过前悬架的ADAMS模型确定前梁的受力。应用这些方法将CAD、CAE有机的结合在一起，提高了有限元分析的效率和准确性。为了检验模型的精确程度，对前梁进行了静态加载试验。结果表明计算和试验结果有很好的一致性。     

埋地燃气管道综合检验检测技术研究

随着技术的进步以及国家能源政策的调整，埋地管道的安全运行日益得到了管道使用单位和政府相关职能部门的重视。综述了目前国内外常用的埋地钢质管道检验检测方法的原理及其优缺点，并结合作者的研究成果，提出了埋地管道综合检验检测技术组合方法。     

江苏省交通事故时间分布分析

根据江苏省有关交通事故数据,利用统计方法对交通事故发生的时间特征进行分析。通过分析交通事故年时间分布规律的波峰曲线,得出江苏省交通事故发生数量开始振荡变化,逐年上升的势头有望于近期遏制。通过分析交通事故、月、周、小时时间分布曲线,得出交通事故高峰小时出现在交通量高峰小时之后的9~12h、14~16h,两者不重合。死亡高峰时段(危险时段)多发生在18~21h的3h内。研究结论对不同时间内如何采取不同的安全对策来降低江苏省交通事故具有指导作用。     

Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach

Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.     

基于多旅行商问题的应急设施服务区划分模型

基于多旅行商问题，增设集散中心需求及应急服务设施资源容量约束条件，以最小化遍历区域内全部集散中心的综合旅行时间成本为优化目标，构建一种应急设施服务区划分模型，确定各应急设施的服务区范围.设计一种复合算法求解模型，首先基于P-中值选址模型的优化理念，形成初始方案；继而加入禁忌搜索算法，结合LKH求解器对模型进行迭代优化求得最优解.基于宁波市北仑区实际拓扑网络进行案例分析，验证了模型和求解方法的有效性.     

Solving the gate assignment problem through the Fuzzy Bee Colony Optimization

In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers’ total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.     

An optimal data-splitting algorithm for aircraft scheduling on a single runway to maximize throughput

In this research, we present a data-splitting algorithm to optimally solve the aircraft sequencing problem (ASP) on a single runway under both segregated and mixed-mode of operation. This problem is formulated as a 0–1 mixed-integer program (MIP), taking into account several realistic constraints, including safety separation standards, wide time-windows, and constrained position shifting, with the objective of maximizing the total throughput. Varied scenarios of large scale realistic instances of this problem, which is NP-hard in general, are computationally difficult to solve with the direct use of commercial solver as well as existing state-of-the-art dynamic programming method. The design of the algorithm is based on a recently introduced data-splitting algorithm which uses the divide-and-conquer paradigm, wherein the given set of flights is divided into several disjoint subsets, each of which is optimized using 0–1 MIP while ensuring the optimality of the entire set. Computational results show that the difficult instances can be solved in real-time and the solution is efficient in comparison to the commercial solver and dynamic programming, using both sequential, as well as parallel, implementation of this pleasingly parallel algorithm.