基于遗传算法的内河船舶路径优化问题研究

将遗传算法应用到内河船舶的路径优化研究中，建立了以运送成本最低为目标的无时间窗约束的内河船舶路径优化模型，并对模型的遗传算法进行了设计。通过实例的计算验证了该模型在内河船舶路径优化中的有效性。     

基于遗传算法的物流配送路径选择系统的仿真设计

本文以物流配送路径选择为研究内容，分析物流配送路径选择系统的功能需求，提出仿真系统的架构设计方案，进行实验论证提出一种可行性算法并进行模拟仿真，构建物流配送路径选择系统的仿真模型。     

现代物流中车辆路径问题的研究

本文阐述了车辆路径问题(VRP)的几种典型的模型和常见算法，分析了这些算法用于计算车辆路径问题的优缺点，并指出了VRP的研究范围还需向范围更广的供应链方向扩展。     

O2O模式下外卖合单配送路径优化

在了解外卖行业发展背景的基础上，从商家、顾客、配送员、平台四方角度探讨了对外卖配送路径进行优化的目的和意义。针对外卖配送的特点和相关文献综述，从平台角度出发，以总配送路径最短为目标函数，在规定的时间窗、承载量内，建立了O2O模式下的外卖配送路径优化模型，对配送路径进行优化，为配送员制定有效的配送方案。同时，收集某高校周边的外卖配送实例，利用最近邻点法和遗传算法对实例进行求解，验证了模型的有效性。最后比较两种求解方法，得出采用遗传算法对O2O模式的外卖配送路径进行优化所达到的效果更佳。     

基于雾计算的实时交通诱导规划机制研究

实时交通流量以及道路状况的变化,对于用户出行路径规划起着关键性的作用。文章在大数据环境中以雾计算数据处理机制为依托,从时间和空间多维角度实现路径的动态诱导规划方案。首先,完成实时交通道路数据网络收集模型的创建,同时,生成与之相对应的数据及数据关联分析;然后,基于实时数据分析结果,对现有的路径规划算法提出动态的路径诱导规划机制。     

结构转换层的设计与施工

结合某工程实践，详细地说明某高层住宅综合楼结构转换层的设计与施工特点，通过对结构转换层多模型计算结果的对比和分析．提出了一种最经济合理的计算模型；通过对结构转换层多施工方案的检算，选择了一种安全便捷的施工方案。     

基于不确定运输时间的军械紧急调运模型研究

针对运输时间不确定的军械紧急调运问题，将运输时间处理为模糊随机变量，建立在任务时间限制期下的最大可能性路径选择模型以确定调运路径。而后，引入超期风险概念，建立以超期风险最小为首要优化目标的军械紧急调运多层觇划模型，实现了对调运方案的优选。最后，给出一个具体军械调运实例来验证模型的适用性。     

基于AHP的多式联运路径选择研究

公、铁、海多式联运中的路径选择是企业物流路径中经常遇到的一个决策问题，本文综合考虑运输的各种目标要求．根据多式联运运输路径的评价指标，应用层次分析法（AHP）对多式联运运输路径方案进行优选。文中根据多式联运系统的特点和基本原则，采取定性分析与定量分析相结合．建立起层次结构模型．通过层次总排序得到运输方案的优劣排序．使评标过程更加全面、科学、公正、准确。     

蚁群算法解决有时间窗的车辆优化调度问题研究

有时间窗的车辆路径问题（Vehicle Routing Problem With Time Windows，VRPTW）属于NP-Hard问题，严格的时间约束使VRFTW非常复杂。应用蚁群算法（Ant Colony Algorithm,ACA）思想来解决VRPTW。对已有蚁群算法解决车辆路径优化问题（VRP）的模型进行改进，对算法中相应的转移规则和轨迹更新规则进行了重新设定，改进了算法转移策略和信息素更新策略。给出了算法的实现步骤。通过算例分析，将计算结果与遗传算法（GA）和粒子群算法（PSO）作了比较，对模型和算法的正确性、高效性、适用性进行了验证。实验结果表明，ACA可以快速、有效求得VRFTW的优化解，是求解VRFTW的一个较好方案。     

基于有限元的预应力混凝土梁桥的优化分析

介绍了结构优化设计的基本原理和在Ansys软件中的实现过程，在有限元分析中钢筋混凝土模型和预应力模型的实现方案，及如何在Ansys软件中建立钢筋混凝土单元模型和预应力单元模型。并讨论在优化过程中各参数选取时要考虑的因素。最后提供了一个预应力混凝土简支T型梁的优化计算模型，从而提供了基于有限元的预应力混凝土梁桥优化分析的具体实现方法，为更复杂的优化计算提供了计算基础。     

基于两阶段选址法的单点区域型配送中心选址研究

以单点区域型配送中心选址为研究对象,以系统理论、组合理论等为基础,运用定量、定性方法对区域型配送中心选址进行研究,提出了新的选址方法——两阶段单点选址法。借助Matlab等软件的强大功能,进行计算求解,并通过实例验证了该方法的有效性。     

Common value auctions with independent types

In this paper I consider a common value model, with independent types, where the ex-post value of the good is influenced by the bidders' information at the auction stage. The seller cannot fully extract the surplus. In a symmetric model the optimal mechanism can be implemented through first price, second price, or English auctions; but not through a Dutch auction. Other properties of the optimal auctions are that the seller's reservation price is endogenous, and that in sealed bid auctions the price may exceed the value of the object (winner's curse).     

Unveiling the endogeneity between social-welfare and labor efficiency: Two-stage NDEA neural network approach

This paper estimates intersectoral labor efficiencies and its covariances in Brazil from 2003 to 2016 using a two-stage neural network model. It investigates whether there is a positive relationship between education and productivity. At the first stage, a static Markov chain regression provides labor quantitative efficiency (volume) and labor value efficiency (value added). The second stage runs a dynamic regression model between each estimated efficiency and the social contextual variables to unveil endogeneities from covariances among the variables set. Strong covariances are found between the efficiencies and fertility rate, suggesting that there is a relevant gap between the productive sector and worker qualification, leading to lower levels of efficiency in Brazil, as shown in the theoretical model. The economy is unable to allocate efficiently the stock of qualified workers.     

Measuring the performance of Internet companies using a two-stage data envelopment analysis model

In exploring the business operation of Internet companies, few researchers have used data envelopment analysis (DEA) to evaluate their performance. Since the Internet companies have a two-stage production process: marketability and profitability, this study employs a relational two-stage DEA model to assess the efficiency of the 40 dot com firms. The results show that our model performs better in measuring efficiency, and is able to discriminate the causes of inefficiency, thus helping business management to be more effective through providing more guidance to business performance improvement.     

A two-stage segment and prediction model for mortgage prepayment prediction and management

Predicting the risk of mortgage prepayments has been the focus of many studies over the past three decades. Most of these works have used single prediction models, such as logistic regressions and survival models, to seek the key influencing factors. From the point of view of customer relationship management (CRM), a two-stage model (i.e., the segment and prediction model) is proposed for analyzing the risk of mortgage prepayment in this research. In the first stage, random forests are used to segment mortgagors into different groups; then, a proportional hazard model is constructed to predict the prepayment time of the mortgagors in the second stage. The results indicate that the two-stage model predicts mortgage prepayment more accurately than the single-stage model (non-segmentation model).     

A note on Lee's model of intraurban employment location

By using the two-stage approach, this note provides an alternative derivation of Lee's theoretical model. In the process, we will demonstrate that the two-stage approach provides a compact way to define the bid-rent function rigorously and to avoid the weakness contained in Lee.     

中国省际高技术产业效率测度与分解——基于共享投入关联DEA模型

本文基于高技术产业科技成果产出和科技成果转化为生产力两阶段视角,考虑"中间产品产出再投入"和"初始投入在两个子系统间的分配结构",构建共享投入关联DEA模型,利用1999～2010年我国29个省份的面板数据,测算高技术产业系统效率和子系统的纯技术效率,并与关联DEA模型、BCC模型的结果进行比较。结果表明,共享投入关联DEA模型在计算效率水平的同时,还可得到中间产品的转化信息和初始投入的配置信息。     

利用GARCH-M类模型和极值理论对上证综指的研究

金融时间序列具有分布的厚尾性、波动的集聚性等特征,传统的方法难以准确的度量其风险。文中运用一种新的估计VaR和ES的方法,即采取两阶段法。首先用GARCH-M类模型(GARCH-M、EGARCH-M和TGARCH-M)拟和原始收益率数据,得到残差序列;第二步用极值分析的方法分析的尾部,最后得到收益率序列的动态VaR和ES。最后对三个模型的计算结果进行比较。     

Two-stage stochastic integer programming: a survey

Stochastic integer programming is more complicated than stochastic linear programming, as will be explained for the case of the two-stage stochastic programming model. A survey of the results accomplished in this recent field of research is given.     

Toward a coordination of inventory and distribution schedules for blood in disasters

The need for efficient blood supply is of more significance in the event of disasters, when there is a lack of coordination between distribution and inventory management. The recent earthquake in Kermanshah province in Iran is among such cases that confirmed the need for coordinating such schedules. In this respect, a two-stage stochastic programming (SP) approach is presented for planning supply of blood after disasters that can assist in inventory decisions under hybrid uncertainty, minimizing the shortage and wastages. The uncertainty stems from imprecise parameters and scenario variability, and a robust-fuzzy-stochastic programming (RFSP) approach is devised to hedge against the uncertainty. The perishability of blood, the substitutability of blood groups, and the age-based characteristic of blood are taken into account to make the model more reliable. The compromise programming is applied to solve the multi-objective model. The results illustrate that the RFSP model can make a reasonable trade-off between mean value, feasibility robustness, and optimality robustness, which results in a robust and reliable solution under disastrous conditions.