基于多阶段模糊规模效应量化的TMC模式下供应链调度优化研究

旅游大规模定制(Tourism Mass Customization, TMC)模式实施的关键是通过对旅游供应链的调度优化处理旅游活动的“规模效应”与游客“个性化需求”之间的矛盾问题。运用经济学及模糊数学的理论方法分析并实现了TMC模式下存在的多阶段模糊规模效应量化处理。构建了引入规模效应量化的服务成本最小化、引入模糊时间窗的顾客满意度最大化及供应链协同度最大化为优化目标的TMC模式下多目标供应链调度优化模型。最后,通过蚁群算法实现TMC模式下多调度优化目标的求解并对优化效果进行对比研究。研究结果表明,TMC模式下供应链调度中旅游活动存在多阶段模糊规模效应并且可以量化处理;TMC模式中的规模效应具有合理的区间范围,旅游企业应注重规模效应与其他目标的均衡;蚂蚁算法在求解TMC模式下多目标优化问题方面不仅收敛速度快,而且通过对多调度目标优化效果的对比检验表明,性能稳健优良。     

基于多属性群决策的软件可靠性模型选择方案

基于模型评价准则的属性值和专家经验的偏好信息进行软件可靠性增长模型的选择,可归结为多属性群决策问题.模型评价准则的主观专家偏好可利用层次分析方法将其量化得到其主观排序权重;基于失效数据的模型评价准则的计算数据可利用熵权法获得客观排序权重.综合两种权重可得到既能反映专家经验又能综合实际软件可靠性测试数据的模型评价方案.实例分析表明该方案具有合理性和可行性.     

基于关键链方法的多项目共享人力资源均衡问题及其遗传算法

通过对多项目管理瓶颈的分析,结合关键链方法,设置了瓶颈缓冲,建立了基于关键链方法的多项目管理,为多项目管理提供了一种新的管理方法.将基于关键链方法的多项目管理方法应用于并行多项目共享人力资源冲突管理,建立了基于遗传算法的多项目共享人力资源均衡模型,为缓解多项目共享人力资源供求矛盾提供了一种具体的量化管理方法,并进行了算例分析,证明了模型的有效性、实用性.     

决策支持下的不完全偏好信息多目标对策

针对多目标对策的不完全偏好信息,将理性策略概念和偏好规划理论引入到多目标对策研究中,把两人的多目标对策考虑为两个局中人同时面临的两个多目标决策问题,偏好信息用可行权重限定集合进行建模.多目标对策模型分为对策模型和决策模型两部分.在对策模型中,利用理性和偏好的共同知识,获得理性策略集.在决策模型中,局中人将策略选择作为使用私人偏好信息的多目标决策问题.最后,通过实例验算表明所提方法的有效性.     

不确定环境下基于交叉评价的模糊综合评价方法

由于环境的不确定性,多属性决策中客观数据和主观因素并存,决策者很难做出精准的评判.借鉴DEA交叉评价的思想,将量化数据用交叉评价方法进行处理,得出平均交叉效率值作为模糊综合评价的指标进行二次评价.建立了不确定环境下基于交叉评价的模糊综合评价模型,并通过评价实例验证了模型的客观、全面性.     

基于圆形邻域孤立点挖掘算法的企业信用风险失真度研究

在考虑企业可能对外发布一些虚假信息这一前提下,借鉴DBSCAN聚类算法的思想,研究了圆形邻域的孤立点挖掘算法,并将其应用于企业虚假信息的识别,在此基础上提出并定义了企业信用风险失真度的概念及其量化结构,以此分析虚假信息对企业信用风险造成的影响,为投资者决策提供理论依据.     

带遗忘因子的递推GM(1,1)预测模型

针对有新信息逐步引入的灰色预测模型,为区分新旧数据对模型的贡献度,提出一种带遗忘因子的递推灰色预测模型.通过在目标函数中增加遗忘因子,实现新旧建模数据的动态加权,达到逐步遗忘旧信息的目的.给出了灰色预测模型参数的递推估计算法,将新获信息实时加入,以提升模型预测精度.网络流量的递推GM(1,1)预测模型建立及数值仿真,验证了改进算法的有效性.     

模糊计划评审技术(F-PERT)中关键路径的规划解法

研究了模糊计划评审技术中关键路径的求解方法 .首先建立了模糊计划网络图中寻找关键路径的规划模型 ,指出该规划模型在模糊排序规则下可以转化为多目标线性规划问题 ,并给出了该类多目标线性规划问题的求解步骤 .最后的算例表明 ,文中提出的方法是切实可行的 .     

基于文本挖掘和AHP-Fuzzy模型的机坪管制运行状况评估

国内大部分机场已实行了机坪管制移交工作.目前尚无一套较为完善的评估体系,对机坪管制运行效果进行量化评估.采用文本挖掘方法,提取文本数据的高频词作为评价指标的来源,构建了机坪管制运行状况评估指标体系;建立了AHP-Fuzzy模型并评估A机场的机坪管制运行现状,评估结果显示良好;通过对比专家权重的结果,发现与本模型评估结果...     

Lotka-VolterraMCGP模型在协同产品组合设计中的应用

产品组合设计是一个典型的多目标优化问题.多目标规划方法是解决多目标决策问题的一种常用方法.然而,经典的多选择目标规划方法孤立地处理产品组合优化问题,没有考虑组合产品之间的相互影响.研究人员应该考虑产品组合优化中产品之间的相互作用,以便它们能够适应“现实世界”的问题.产品之间的相互作用可以用种群动力学来解释.Lotka-Volterra模型是分析产品种群相互作用的经典方法.Lotka-Volterra模型均衡状态能够反映产品种群协同发展的理想情境.LotkaVolterra均衡分析方法与多选择目标规划方法相结合是分析产品组合交互作用的有效方法.提出了一种新的求解多目标问题的方法,并用一个算例说明了该方法的有效性.模型优化结果的对比分析表明,Lotka-Volterra多选择目标规划模型能够同时考虑资源约束、产品协同和产出最大化要求.     

可拓数据挖掘研究进展

可拓学研究用形式化模型解决矛盾问题的理论与方法,可拓数据挖掘是可拓学和数据挖掘结合的产物,它探讨利用可拓学方法和数据挖掘技术,去挖掘数据库中与可拓变换有关的知识,包括可拓分类知识、传导知识等可拓知识.随着经济全球化的推进,环境的多变促使了信息和知识的更新周期缩短,创新和解决矛盾问题越来越成为各行各业的重要工作.因此,如何挖掘可拓知识就成为数据挖掘研究的重要任务.研究表明,可拓数据挖掘将具有广阔的应用前景.将介绍可拓数据挖掘的集合论基础、基本知识和目前研究的主要内容,并提出今后需要进一步探讨的问题及其发展前景.     

Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models

In the past, several authors have found evidence for the existence of a priority pattern of acquisition for durable goods, as well as for financial services. Its usefulness lies in the fact that if the position of a particular customer in this acquisition sequence is known, one can predict what service will be acquired next by that customer. In this paper, we analyse purchase sequences of financial services to identify cross-buying patterns, which might be used to discover cross-selling opportunities as part of customer relationship management (CRM). Hereby, special attention is paid to transitions, which might encourage bank-only or insurance-only customers to become financial-services customers. We introduce the mixture transition distribution (MTD) model as a parsimonious alternative to the Markov model for use in the analysis of marketing problems. An interesting extension on the MTD model is the MTDg model, which is able to represent situations where the relationship between each lag and the current state differs. We illustrate the MTD and MTDg model on acquisition sequences of customers of a major financial-services company and compare the fit of these models with that of the corresponding Markov model. Our results are in favor of the MTD and MTDg models. Therefore, the MTD as well as the MTDg transition matrices are investigated to reveal cross-buying patterns. The results are valuable to product managers as they clarify the customer flows among product groups. In some cases, the lag-specific transition matrices of the MTDg model give better insight into the acquisition patterns than the general transition matrix of the MTD model.     

基于结构分解方法的采煤机械化对煤炭高效开发的贡献研究

采用完全结构分解方法,将采煤劳动强度变化分解为结构份额和效率份额,并基于1985~2006年的统计数据进行了分析.主要结论有:我国采煤劳动强度的下降,也即煤炭资源开发效率的提高是采煤工艺结构变动和各采煤工艺工效提高综合作用的结果,相对效率份额,结构份额的贡献更大一些;从结构份额来看,非机采和高档普采结构的变动导致了整体劳动强度的下降;而从效率份额来看,综采、高档普采和非机采工效的提高则是整体劳动强度下降的主要原因.     

Developing an efficient knowledge discovering model for mining fuzzy multi-level sequential patterns in sequence databases

Sequential pattern mining from sequence databases has been recognized as an important data mining problem with various applications. Items in a sequence database can be organized into a concept hierarchy according to taxonomy. Based on the hierarchy, sequential patterns can be found not only at the leaf nodes (individual items) of the hierarchy, but also at higher levels of the hierarchy; this is called multiple-level sequential pattern mining. In previous research, taxonomies based on crisp relationships between any two disjointed levels, however, cannot handle the uncertainties and fuzziness in real life. For example, Tomatoes could be classified into the Fruit category, but could be also regarded as the Vegetable category. To deal with the fuzzy nature of taxonomy, Chen and Huang developed a novel knowledge discovering model to mine fuzzy multi-level sequential patterns, where the relationships from one level to another can be represented by a value between 0 and 1. In their work, a generalized sequential patterns (GSP)-like algorithm was developed to find fuzzy multi-level sequential patterns. This algorithm, however, faces a difficult problem since the mining process may have to generate and examine a huge set of combinatorial subsequences and requires multiple scans of the database. In this paper, we propose a new efficient algorithm to mine this type of pattern based on the divide-and-conquer strategy. In addition, another efficient algorithm is developed to discover fuzzy cross-level sequential patterns. Since the proposed algorithm greatly reduces the candidate subsequence generation efforts, the performance is improved significantly. Experiments show that the proposed algorithm is much more efficient and scalable than the previous one. In mining real-life databases, our works enhance the model's practicability and could promote more applications in business.     

项目网络中考虑支配与扩散关系的组合决策

单纯侧重项目自身属性而不考虑项目关联性以及由项目衍生而来的技术、经验/信息扩散对项目组合决策时的影响,易导致决策偏差,低估具有潜在技术先导性项目的价值。对此,引用复杂网络理论,以项目关联性的视角,将项目间支配和扩散关系分别抽象为有向加权网络,运用K-shell分解方法构建项目组合网络中基于支配关系的项目影响力模型以及技术、经验/信息在项目间扩散传播的模型。然后,基于PageRank算法,综合考虑项目间支配与扩散关系,建立了项目优先级排序决策模型。最后,通过算例分析说明了该模型与算法的可行性与有效性,为企业项目组合决策提供了有益的参考。     

基于贝叶斯网络的工程项目进度完工概率分析

针对传统计划评审技术(Program Evaluation and Review Technique,PERT)在计算完工概率时假设条件的局限性(假设条件与工程实际存在偏差,导致完工概率偏大),提出了基于贝叶斯网络的施工进度完工概率分析方法.首先,分析了贝叶斯网络与进度计划网络之间的相似性,将两者结合起来构建了贝叶斯进度网络;在此基础上,综合考虑贝叶斯网络在节点取值及概率计算方面的优越性,并结合工程项目的不确定性及复杂性特点,建立了基于贝叶斯网络的施工进度完工概率分析模型.最后,将该模型应用于具体工程进行实例分析,验证了模型的可行性与有效性.研究结果表明:基于贝叶斯网络的进度完工概率模型充分考虑了工程施工中的风险因素,其结果能更客观地反映工程实际,可为工程项目决策者提供可靠的依据.     

A formula for multiple classifiers in data mining based on Brandt semigroups

A general approach to designing multiple classifiers represents them as a combination of several binary classifiers in order to enable correction of classification errors and increase reliability. This method is explained, for example, in Witten and Frank (Data Mining: Practical Machine Learning Tools and Techniques, 2005, Sect. 7.5). The aim of this paper is to investigate representations of this sort based on Brandt semigroups. We give a formula for the maximum number of errors of binary classifiers, which can be corrected by a multiple classifier of this type. Examples show that our formula does not carry over to larger classes of semigroups.     

Selective association rule generation

Mining association rules is a popular and well researched method for discovering interesting relations between variables in large databases. A practical problem is that at medium to low support values often a large number of frequent itemsets and an even larger number of association rules are found in a database. A widely used approach is to gradually increase minimum support and minimum confidence or to filter the found rules using increasingly strict constraints on additional measures of interestingness until the set of rules found is reduced to a manageable size. In this paper we describe a different approach which is based on the idea to first define a set of “interesting” itemsets (e.g., by a mixture of mining and expert knowledge) and then, in a second step to selectively generate rules for only these itemsets. The main advantage of this approach over increasing thresholds or filtering rules is that the number of rules found is significantly reduced while at the same time it is not necessary to increase the support and confidence thresholds which might lead to missing important information in the database.     

Numerical simulation of pollutant dispersion in street canyons: Geometric and thermal effects

Numerical investigations on pollutant dispersion in street canyons with emission sources located near the ground level are performed in the present work. Pollutant dispersion problems in urban areas are usually studied considering the street canyon model, which consists of long streets laterally confined by buildings. Significant changes can be observed in wind flow patterns and pollutant concentration fields when thermal and geometric effects are considered. Thus, the objective of this study is to investigate numerically the wind flow and pollutant dispersion for the following cases: (a) a two-dimensional street canyon model considering three different aspect ratios and four different wall heating configurations; (b) a flow domain with two immersed buildings arranged in two distinct configurations; (c) a three-dimensional urban area model composed of a building set and street intersections. Expected flow structures were obtained inside the canyon when different aspect ratios and wall heating configurations were considered. Flow phenomena such as separation/reattachment were observed when two-buildings models were analyzed. Finally, three-dimensional flow structures, with some characteristic that are not observed in two-dimensional models, affecting the pollutant removal, were simulated in the last case, highlighting the relevance of model dimensionality. The wind flow and pollutant dispersion are investigated using a numerical model based on the finite element formulation utilized by some of the authors of this work, which is extended here to deal with problems of heat and mass transport in the urban micro-scale. Turbulence is reproduced using Large Eddy Simulation (LES) and thermal effects on the momentum equations are considered as a buoyancy force, according to Boussinesq approximation.     

Interpretation of association rules in multi-tier structures

Dealing with the large amount of data resulting from association rule mining is a big challenge. The essential issue is how to provide efficient methods for summarizing and representing meaningful discovered knowledge from databases. This paper presents a new approach called multi-tier granule mining to improve the performance of association rule mining. Rather than using patterns, it uses granules to represent knowledge that is implicitly contained in relational databases. This approach also uses multi-tier structures and association mappings to interpret association rules in terms of granules. Consequently, association rules can be quickly assessed and meaningless association rules can be justified according to these association mappings. The experimental results indicate that the proposed approach is promising.