一种基于粗糙集理论的规则获取算法

获取规则是数据挖掘中的一项重要技术 ,根据粗糙集理论及决策值归纳函数的概念 ,可以把不相容的决策系统转化为相容的决策系统 ,并提出规则参数的合并方法 .在此基础上 ,利用决策矩阵和决策函数 ,提出了一种在决策系统中获取规则的算法 .同传统的算法相比 ,该算法得出的规则集没有信息丢失的现象发生 .最后以例子作了说明 .     

二分群体决策中领导的影响

本文研究了具有领导的二分群体决策中领导对决策收益的影响,得到的一个重要结论是:如果二分群体决策过程应用了独立投票过程下的最优规则,则领导对其他决策者施加影响不可能提高群体决策收益。然而当群体未能应用最优决策规则时,领导的影响有可能发挥积极作用,这主要是由于此时领导的决策能力高于其他决策成员,而决策规则分配给领导的权重相对过低而导致的。     

等待时间受限的流水车间调度问题的启发式算法

针对等待时间受限的流水车间调度问题,分析了等待时间上限与可行解的解析关系以及目标函数的特殊性质,以此为基础,提出了一种启发式算法.算法采用贪婪与插入相结合的启发式规则构造工件加工序列,通过递归回溯解消其等待时间受限约束.仿真实验表明,该启发式工件排序规则在等待时间约束较紧或问题规模较大时,较其他几种常用排序规则具有更好的效果.     

基于可识别信息的集体决策机制有效性的研究——策略性投票、审议与表决规则

投票一直以来是集体决策理论的重要研究对象。本文从博弈论的角度出发，分析一个有二元备选项的集体决策问题，并允许集体成员在投票前互相交流其可能获得的关于备选项优劣的信息，这一交流过程被称为审议。本文假定信息是可识别的，集体成员至多只能选择隐瞒而不能撒谎。本文通过考察不同投票规则下集体决策的结果以研究不同决策机制的效率。理论结果表明，多数表决的投票规则可以引导信息被充分披露的均衡存在从而使得集体决策的结果有效，而一致表决的投票规则不能做到，因此多数表决比一致表决在引导成员说真话的意义上表现得更好。此外，当信息被部分披露时，本文通过构造一类偏见驱动的审议均衡证明了集体决策的结果在一致表决下有时反而更好，因此多数表决并不总是优于一致表决。本文为一致表决的实际存在提供了理论解释，并指出集体决策的结果依赖于表决规则与具体的均衡路径，盲目地选择少数服从多数往往会导致低效的选择结果。     

模糊规则插值法在约简模糊系统中的应用

本文讨论模糊规则插值法及其在简化模糊决策系统中的应用.在稀疏模糊规则库中,可以利用模糊规则插值法实现模糊推理.而利用模糊插值法进行模糊规则库的约简,则是在规则库中发现一些规则,它们能由其它规则通过插值机制近似表示,这些规则是冗余的规则,可以从规则中去除.     

应用Benford法则和Apriori算法对海量数据的审计分析

随着信息时代的飞速发展,被审计单位财务和业务数据量爆炸式增长,审计技术必须不断发展和创新以适应信息时代审计的新要求.本文在这一背景下,对审计技术的创新作了探索性的研究,主要通过Benford法则和Apriori算法的关联数据挖掘的技术手段在审计中的应用,探讨了如何在被审计单位海量数据中进行挖掘分析,从而发现有业务意义的强规则,通过这些强规则解释数位发生偏离的原因,从而快速发现审计疑点.     

中国存在财政规则吗?

本文在动态随机一般均衡框架下,利用贝叶斯和方差分解的方法对我国财政规则的存在性进行检验,并分析其效果,得到的主要结论为:(1)完全规则或完全相机决策的财政政策调整方式在我国并不存在,但规则性确实可以对我国财政政策起到重要解释作用,规则性解释了政府投资变动的大部分,间接税有效税率变动的约50%,以及政府一般性支出和直接税有效税率变动的一部分。(2)国外假设财政规则为财政政策依据产出缺口和政府债务规模调整的情形不能最好地切合我国实际,本文发现财政政策依据产出缺口和政府赤字调整的财政规则更切合我国实际情况。(3)财政规则具有政府界别特征,温家宝为政府总理时,政府投资和税收政策的规则性都较强。(4)依据规则调整的财政政策使得经济系统面临技术冲击时波动性加大。(5)提高财政政策的规则性有利于提高社会福利。     

基于粗糙集理论的格序选择方法

在粗糙集理论中,为了识别由偏好属性导致的不相容性,已提出基于优势关系的粗糙集理论,然而这一理论在实际应用中往往导致多个决策规则的出现,如何辨别由偏好多属性决策表中获取的多个决策规则从而选取最优方案,尚未见到有关的研究文献。本文针对这一问题,利用格序理论,给出属性约简的贴近度,比较不同约简所得决策规则贴近于原知识库的程度,从而得到一种选择最优决策方案的方法。     

探讨从规则主义转为人本主义的刑法解释观

刑法条文本身具有一定的抽象性和稳定性,加上当今社会情况的复杂多变,因此在进行刑法解释时,必须正确领会立法意图、准确适用法律。在刑法解释的过程中,各国的学者存在着不同的观念。我国在刑法解释的过程中,一直坚持规则主义的解释观,不过现在也在逐渐向人本主义转变。我国从规则主义转为人本主义的刑法解释观的过程中,逐渐显现出了人本主义解释观的优势。本文认为,刑法解释观念应坚持从规则主义转为人本主义。     

基于变精度粗集的战略石油储备规模决策方法

我国的战略石油储备(Strategic Petroleun Reserve,SPR)政策刚刚起步,对SPR规模进行决策,使其保持在合理范围,是SPR管理的重要内容.本文基于变精度粗集(variable precisioz rough set,VPRS)理论,提出了一种简单易行的SPR规模决策方法,通过设定一个变精度因子口,允许SPR决策信息系统中有一定的错误分类率,减少了数据噪声,提高了决策规则的鲁棒性.     

R&D项目中止决策的小波网络模式识别

如何科学、合理、有效地识别和中止那些行将失败或没有前途的 R&D项目 ,对于企业成功地进行技术创新、建立和保持竞争优势 ,显然具有十分重要的理论价值和实践意义 .本文根据中止决策的实质及模式识别的基本原理 ,探讨了定量地中止 R&D项目的一般模式识别程序 .同时 ,基于小波基具有很强的自适应数据和函数变化的能力 ,进一步研究给出了一种基于小波网络的 R&D项目中止决策分析模式识别新方法 ,并在最小均方能量准则下 ,采用相应的共轭梯度学习算法求解子波函数线性组合的尺度和时延参数 ,以及神经网络的权值 .本研究不仅克服了现有 R&D项目中止决策分析技术在构造与求解模型方面的某些局限性 ,并且为R&D项目的跟踪管理提供了一种新的理论依据 ,为具体决策单位和政府有关部门对正在实施的 R&D项目作中止决策提供了一种可操作的决策方法和程序     

多标准决策表中发现概率规则的变精度粗糙集方法

用优势关系代替不可分辨关系,本文提出了一种可以从多标准决策表中获取概率规则的扩展变精度粗糙集模型,该模型能够处理多标准决策表中可能的不一相容性,获取由偏好对象组成的概率决策规则集,并导出事例决策系统的偏好模型。研究结果表明:基于优势关系,从多标准决策表中获取的最小概率规则集,使用的条件数量较少,且导出规则的数量较少、较强。     

Approximate weighting method for multiattribute decision problems with imprecise parameters

A multiattribute decision problem with imprecise parameters refers to one in which at least one of the parameters such as attribute weights and value scores is not represented by precise numerical values. Some well-known types of incomplete attribute weights are chosen and analyzed to find their extreme points. In doing so, we show that their coefficients matrix, by itself or by the change of variables, belongs to a class of M-matrix which enables us to find its extreme points readily due to the inverse-positive property.The knowledge of extreme points not only helps us to prioritize alternatives but also supports iterative exploration of decision-maker’s preference by investigating modified extreme points caused by additional preference information. A wide range of eligible attribute weights, however, often fail to result in the best alternative or a complete ranking of alternatives. To address this situation, we consider an approximate weighting method, so called the minimizing squared deviations from extreme points (MSD) which locates the attribute weights at the barycenter of a weight set. Accordingly, the MSD approach extends the rank order centroid (ROC) weighting method which is known to outperform other approximate weighting methods in case of ranked attribute weights. The evidence of the MSD’s superiority over a linear program-based weighting method is verified via simulation analysis under different forms of incomplete attribute weights.     

随机订单干扰下考虑合并决策的供应链网络调度研究

为满足客户个性化需求的快速响应，企业需具备柔性的外部供应链网络结构，以协同方式共同完成产品生产。本文考虑具有交互特征的多个不同类型协同供应链网络，构建生产成本、库存成本、等待成本以及订单延期交货成本最小化的目标函数，并设计合并决策判断变量构建同类订单在相同协同企业处的开始时间约束。此外，模型中考虑确定订单以及随机订单两种类型订单，并设计随机订单在区间时间段中离散时间点的到达概率。为获取协同供应链网络生产调度优化策略，基于随机订单到达与否的场景构建四个子决策模型，并进一步设计判断提前安排随机订单协同生产和不提前安排随机订单协同生产不同调度策略下成本差异的主决策模型。仿真结果表明合并决策在带来生产成本效益的同时也引起了部分订单的延期交货，且不同类型的协同供应链网络对随机订单的抗干扰能力存在一定程度的差异。     

Multi-time scale Markov decision process approach to strategic network growth of reverse supply chains

This paper addresses a complex set of decisions that surround the growth over time of reverse supply chain networks that collect used products for reuse, refurbishment, and/or recycling by processors. The collection network growth problem is decomposed into strategic, tactical and operational problems. This paper focuses on the strategic problem which is to determine how to allocate capital budget resource effectively to grow the network to meet long term collection targets and collection cost constraints. We model the strategic problem as a Markov decision process which can also be posed as multi-time scale Markov decision problem. The recruitment problem in a tactical level appears as a sub-problem for the strategic model. Using dynamic programming, linear programming and Q-Learning approaches, an heuristic is implemented to solve realistically sized problems. A numerical study demonstrates that the heuristic can obtain a good solution for the large-scale problem in reasonable time which is not possible when trying to obtain the optimal solution with the exact DP approach.     

基于约束满意的供应链应急制造采购策略研究

针对紧急需求与采购成本之间的冲突,研究了模糊时间要求和模糊物资需求下多供应商和单一制造商构成的供应链的应急制造采购问题.建立了供应链应急制造采购的多目标、多约束组合优化模型,提出了一种由约束满意优化模型转换、搜索算法、满意度函数和满意度要求调整方法构成的交互式满意决策方法.运用该方法能够获得体现决策者偏好的满意采购策略,实现以合理成本满足应急需求的目的.通过模拟算例解释了交互式满意决策的过程.     

Comparative Statics,Informativeness, and the Interval Dominance Order

We identify a new way to order functions, called the interval dominance order, that generalizes both the single crossing property and a standard condition used in statistical decision theory. This allows us to provide a unified treatment of the major theorems on monotone comparative statics with and without uncertainty, the comparison of signal informativeness, and a non‐Bayesian theorem on the completeness of increasing decision rules. We illustrate the concept and results with various applications, including an application to optimal stopping time problems where the single crossing property is typically violated.     

A numerical analysis of supply chain performance under split decision rights

We examine periodic review supply chain models where order quantity and shipping frequency are both decision variables and decision-making rights are split between supply chain agents. We address two general questions: (1) when does decentralized decision making result in the greatest loss in supply chain performance; and (2) what effect does the distribution of channel power have on performance loss. We characterize optimal policies where possible in each scenario and we use numerical analysis to generate insights. We find that performance losses from decentralized control are somewhat limited in our results due to risk pooling and that the magnitude of performance loss is strongly influenced by the relative holding and penalty costs, but somewhat invariant to demand uncertainty. Furthermore, we find that concentrating channel power with the supplier can lead to supply chain profits that are very close to a centralized scenario, but also results in lower customer service levels.     

供应链生产能力的柔性决策研究

面对不确定性的市场需求,能力的选择和分配是供应链设计中的重要问题.本文应用实物期权方法研究供应链中制造商的生产能力决策,给出柔性条件下的能力决策规则.结果表明,较高(低)的能力投资成本、风险较大(小)的市场和较低(高)的缺货成本将使得制造商推迟(提前)其扩大生产能力决策的实施.     

A case-based distance method for screening in multiple-criteria decision aid

Screening is a process of multiple-criteria decision aid (MCDA) in which a large set of alternatives is reduced to a smaller set that most likely contains the best choice. We propose screening using a distance model calibrated on the basis of the decision-maker's own judgement. Viewing MCDA as preference aggregation based on consequence data, we define consequence and preference expressions (values and weights) and describe how they are aggregated. Then we define screening and explain some of its properties. Using an appropriate definition of distance, our case-based distance method screens a set of alternatives using criterion weights and a distance threshold obtained by quadratic optimization using the decision-maker's selection of alternatives from a test set. This case-based method can elicit the decision maker's preferences more expeditiously and accurately than direct inquiry. An application in water supply planning is used to demonstrate the procedure.