数字签名技术的研究与探讨

通过对对称加密体制、非对称加密体制以及Hash算法的分析,根据数字签名的要求,给出了一种具有防伪、防赖及保密的数字签名技术．     

On Inverse Problems of Optimum Perfect Matching

As far as we know, for most polynomially solvable network optimization problems, their inverse problems under l ₁ or l norm have been studied, except the inverse maximum-weight matching problem in non-bipartite networks. In this paper we discuss the inverse problem of maximum-weight perfect matching in a non-bipartite network under l ₁ and l norms. It has been proved that the inverse maximum-weight perfect matching under l norm can be formulated as a maximum-mean alternating cycle problem of an undirected network, and can be solved in polynomial time by a binary search algorithm and in strongly polynomial time by an ascending algorithm, and under l ₁ norm it can be solved by the ellipsoid method. Therefore, inverse problems of maximum-weight perfect matching under l ₁ and l norms are solvable in polynomial time.     

利用和声分散搜索算法解决动态共乘的乘客选择研究

动态共乘作为一种配合解决城市交通出行难题的新模式近年来引起了人们越来越多的关注，然而在较大范围内选择合适的乘客，以便获得最佳的综合服务效果却具有相当大的挑战性。本文正是针对这一问题，建立了以乘客效用最大化和司机总行程最短为目标函数，以满足司机与乘客的时间要求和司机参与约束为限制条件的多目标0-1规划共乘模型，用于帮助司机选择最合适的乘客。根据该模型的特点，构造了加入了分散搜索机制的新的和声搜索算法。在仿真实验时，针对司机和乘客效用的两种产生方式，在较大规模的路网环境下利用该算法分别对模型进行了求解，得到了Pareto最优解集。仿真结果不仅表明了模型的合理性和算法的可行性，而且还指出基于效用函数可以发现更多合适的潜在乘客。最后，通过与文献中其它算法的对比进一步展示了本文算法的有效性。     

考虑通行约束和运力限制的灾后应急物资联合调度优化研究

考虑到灾后路网受损难以运输应急物资，本文研究了应急响应中车辆-直升机联合调度的路径优化问题。针对受灾地区的实时路况，通往灾区的救援工具受到数量以及装载量的约束，本文将受灾点等待救援的平均时间最短以及应急网络总费用最低设为目标，构建运力受限条件下带通行约束的救援物资联合运输多目标优化模型，然后根据随机邻域搜索变异和分级交叉的思想构建出一种带精英策略的非支配排序混合进化算法（NSHEA-II）得到模型的解，并利用算例分析对该算法进行可行性检验。结果发现，本文构建的NSHEA-II算法相对NSGA-II算法能够得到较好的结果且波动性较小，这为决策者制定救援物资的配送方案提供有效的技术支撑。     

基于求解TSP问题的双向扩展差额算法

旅行商(TSP)问题是典型的组合优化中的NP-hard难题.本文在最近城市搜索法和两端延伸最近城市搜索法基础上提出了双向扩展差额求解算法,并分析了算法的复杂度.采用以上三种算法求解了TSPLIB标准库中多个算例,比较结果表明本算法能够更快的找到更优的方案,具有更好的综合性能.     

基于差分的数量折扣条件下订货策略优化模型

通过采用合适的数量折扣策略,供需双方都可以提高自己的利润。用双层规划模型描 述了供应链管理中供应商与订货商合作情况下确定数量折扣及订货量问题,并用基于差分的 算法进行了求解. 实例分析说明双方合作的双层规划模型能正确表示实际的供需关系,在平衡 点可以使双方利益均达到最大     

基于可信性理论的Mean-CVaR投资组合优化

选取CVaR作为风险度量指标,在可信性理论的基础上构建Mean-CVaR投资组合模型,采用Markov过程预测作为模糊变量的预期投资收益率,并设计基于模糊模拟和遗传算法的混合智能算法以求解;选取上证50成份股2013—2014年的日度历史交易数据,将该模型应用到中国证券市场,结果发现该投资组合模型与中国证券市场的环境具有一定的适应性,能够为投资者的投资决策提供依据。     

基于适度财务指标和遗传算法的财务危机预测模型研究

以往进行财务危机预测主要使用统计方法建模,迄今尚未见到因考虑财务指标的适度性而建立遗传算法模型的中外文献。适度财务指标是指该指标在某个区间内公司才是健康的,过大或过小则公司发生财务危机的可能性都较大。故以中国上市公司数据为样本,建立了基于适度财务指标和遗传算法的创新型财务危机预测模型,结果显示该类模型具有较高的预测准确率。     

Fast retraction of evidence in a probabilistic expert system

We present and justify a propagation algorithm to facilitate the simultaneous calculation, for every node in a probabilistic exper system of the distribution of the associated random quantity, conditional on all the evidence obtained about the remaining nodes.     

Factor analysis for ranked data with application to a job selection attitude survey

Summary.  Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation–maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs.