模糊决策属性约简及其在工位评估中的应用

受粗糙集理论中知识约简的启发,在模糊多属性决策理论中提出属性约简的概念,构建属性约简方法,寻求属性集合中所有最简单的保序属性约简子集以及属性约简核.用属性约简子集代替原有的属性集合,通常可以显著简化模糊多属性决策问题.还将此属性约简方法应用到生产线工位绩效评估中,表明此属性约简算法的实用性.     

协调决策形式背景的属性约简

本文研究基于对象定向概念格和属性定向概念格协调决策形式背景属性约简的问题。首先介绍了协调决策形式背景属性约简的概念,讨论了其等价的定义。进一步给出了属性约简的判定定理和相应的可辩识属性矩阵,从而为概念格中协调决策形式背景属性约简提供了一种具体的方法。     

MMAS与粗糙集在齿轮箱故障诊断中的应用

粗糙集理论是一种新的处理含糊和不确定性知识的数学工具,属性约简是粗集理论研究的重要内容,属性约简算法有很多种,而计算一个最佳约简是NP难问题。为了能够有效地获取信息系统的约简,提出了一种新的约简算法。该算法选择最大-最小蚂蚁系统(MMAS),以Fisher准则作为启发式信息来提高搜索效率,将蚁群优化算法引入属性约简中,利用粗糙集理论对故障诊断决策表进行约简,形成清晰、简明的故障诊断规则,为下一步的故障诊断打下了坚实的基础。     

基于相似矩阵的折半属性约简算法

针对大多数属性约简算法时间复杂度比较高的问题,利用粗糙集理论提出了一种新的解决办法,该方法基于相似矩阵概念,利用属性在相似矩阵中出现的频率给出了属性重要性的计算公式,以此作为启发式知识来约间决策表中的冗余属性,并将折半查询的思想运用到算法中以加快约简的速度。实验表明该算法是简单有效的.     

集值信息系统的属性约简

属性约简是粗糙集理论研究中的重要内容之一。本文主要研究集值信息系统的属性约简问题。在集值信息系统中基于拟序关系引入了信息量的概念,给出了属性特征的判定方法,以及信息量与属性约简之间的关系。根据信息量定义了属性重要性,研究了属性重要性与属性约简之间的关系。进而得到了基于信息量和属性重要性的属性约简算法,给出了该算法的时间复杂度。通过实例说明,该算法是有效的。     

基于分治的属性约简相对核计算

针对信息表中相对核的计算采用分治策略提出了一个新的属性约简方法，将在计算整个全域上的属性约简问题转化为计算在相应划分的子区域上属性约简问题．将原有计算POS X0(y)的算法复杂度O(|A||U|^2)，降为O(|A|(|Y1|^2 |Y2|^2 … |Yn|^2))，对于一般比较大的|U|说，效率提高非常明显，提高了属性约简的可计算性.     

基于广义粗糙集的知识约简方法研究

针对粗糙集理论在知识约简中的实际需要,提出了建立在一般二元关系基础上的广义粗糙集知识约简方法。首先证明了广义粗糙集是经典粗糙集的一般性推广,而经典粗糙集是广义粗糙集的特例;然后以一般二元关系为分类基础,给出一般关系决策系统中的知识约简判定定理和辨识矩阵;最后根据实例提取最小的属性集,验证了该方法的实用性。该方法摆脱了二元等价关系对经典粗糙集的困扰,既保证了粗糙集理论在知识发现研究中的理论优势,又拓展了粗糙集理论在实际应用中的适用范围,具有较强的实用性。     

基于形式概念分析的多光谱遥感影像波段选择

针对遥感图像特征选择和提取的波段选择,利用形式概念分析中属性约简的思想设计了波段约简选择算法。该方法利用遥感影像的信息量作为指标,将多光谱影像作为形式背景,各波段作为形式属性,通过属性约简得到核心属性即最优波段,这样不仅避免了多光谱遥感数据波段之间的相关性对波段选择的影响,还提高了选取训练样本的效率。最后采用了美国地球观测卫星Landsat拍摄的TM遥感影像数据进行实验,与常用的波段选择方法相比,本文的方法更为简便和高效。     

基于粗糙集与信息熵的不完备测试信息条件下故障诊断

针对测试信息不完备条件下故障诊断决策问题,引入粗糙集和信息熵方法。利用决策属性支持度求相对核,以此作为启发信息;通过建立属性知识与信息熵的联系,提出基于信息熵的属性约简方法。结合决策属性支持度和信息熵设计了约简算法流程,减少属性集搜索空间,求得到最优属性约简集。实例表明,该算法适用于协调的不完备决策信息系统和不协调的不完备决策信息系统,能够解决不完备测试信息条件下故障诊断决策问题。     

具有Fuzzy决策的信息系统的知识约简

知识约简是粗糙集理论的核心问题之一。讨论了具有Fuzzy决策的信息系统的知识约简问题。给出了知识约简的几种定义、相应的知识约简的判定定理和可辨识属性矩阵，从而为具有Fuzzy决策的信息系统的知识约简的计算提供了具体方法。     

Learning Paths to Offshore Outsourcing: From Cost Reduction to Knowledge Seeking

A corporation's offshore outsourcing may be seen as the result of a discrete, strategic decision taken in response to an increasing pressure from worldwide competition. However, empirical evidence of a representative cross-sector sample of international Danish firms indicates that offshore sourcing in low-cost countries is best described as a learning-by-doing process in which the offshore outsourcing of a corporation goes through a sequence of stages towards sourcing for innovation. Initially, a corporation's outsourcing is driven by a desire for cost minimization. Over a period of time the outsourcing experience lessens the cognitive limitations of decision-makers as to the advantages that can be achieved through outsourcing in low-cost countries: the insourcer/vendor may not only offer cost advantages, but also quality improvement and innovation. The quality improvements that offshore outsourcing may bring about evoke a realization in the corporation that even innovative processes can be outsourced.     

Combining the cost of reducing uncertainty with the selection of risk assessment models for remediation decision of site contamination

The decision as to whether a contaminated site poses a threat to human health and should be cleaned up relies increasingly upon the use of risk assessment models. However, the more sophisticated risk assessment models become, the greater the concern with the uncertainty in, and thus the credibility of, risk assessment. In particular, when there are several equally plausible models, decision makers are confused by model uncertainty and perplexed as to which model should be chosen for making decisions objectively. When the correctness of different models is not easily judged after objective analysis has been conducted, the cost incurred during the processes of risk assessment has to be considered in order to make an efficient decision. In order to support an efficient and objective remediation decision, this study develops a methodology to cost the least required reduction of uncertainty and to use the cost measure in the selection of candidate models. The focus is on identifying the efforts involved in reducing the input uncertainty to the point at which the uncertainty would not hinder the decision in each equally plausible model. First, this methodology combines a nested Monte Carlo simulation, rank correlation coefficients, and explicit decision criteria to identify key uncertain inputs that would influence the decision in order to reduce input uncertainty. This methodology then calculates the cost of required reduction of input uncertainty in each model by convergence ratio, which measures the needed convergence level of each key input's spread. Finally, the most appropriate model can be selected based on the convergence ratio and cost. A case of a contaminated site is used to demonstrate the methodology.     

A cost estimation model to support automation decision in assembly systems design

Cost analysis is crucial in the design of assembly systems and the decision on their level of automation (LoA). This paper presents a cost estimation model of assembly system that is used to decide their LoA during the early phase of projects. Based on an extensive literature review, a complete cost model integrating multiple cost drivers is proposed. This model is then exploited to create the objective function of an integer linear programme model utilised to solve the LoA decision problem. The work provides a way to perform cost estimation of assembly systems alternatives and to decide the most appropriate LoA in assembly. The cost estimation model is built with a parametric approach allowing the definition of various optimisation objectives. The proposed integer programme, complement this approach by proposing the suitable constraints set, that describes the LoA decision problem.     

Bayesian risk-based decision method for model validation under uncertainty

This paper develops a decision-making methodology for computational model validation, considering the risk of using the current model, data support for the current model, and cost of acquiring new information to improve the model. A Bayesian decision theory-based method is developed for this purpose, using a likelihood ratio as the validation metric for model assessment. An expected risk or cost function is defined as a function of the decision costs, and the likelihood and prior of each hypothesis. The risk is minimized through correctly assigning experimental data to two decision regions based on the comparison of the likelihood ratio with a decision threshold. A Bayesian validation metric is derived based on the risk minimization criterion. Two types of validation tests are considered: pass/fail tests and system response value measurement tests. The methodology is illustrated for the validation of reliability prediction models in a tension bar and an engine blade subjected to high cycle fatigue. The proposed method can effectively integrate optimal experimental design into model validation to simultaneously reduce the cost and improve the accuracy of reliability model assessment.     

跨境VMI供货模式下生产-库存联合优化研究

研究基于跨境VMI(vendor managed inventory)供货模式下的生产-库存联合优化问题。以一个实际案例企业作为应用背景,比较了VMI供货模式和传统的供货模式在海运和空运2种运输方式下的不同特点及其对决策的影响。研究结果表明,对于案例企业而言,采用VMI供货模式的库存管理是可行的,考虑目前公司采用2种可选的运输模式,选择海运的总成本最低。通过对资金费用率、库存费用比率和缺货费用率进行灵敏度分析,探讨了不同环境变量对最优决策的影响。研究结果对跨境供应链多国生产型企业改善跨境物资采购和生产-库存决策有一定参考价值。     

产品生命周期成本概念及分析方法

随着人们成本意识的增强,生命周期成本日益引起设计者和用户的重视。对产品生命周期和产品生命周期成本概念进行了论述,介绍了参数模型、类推模型和详细模型3种生命周期成本分析模型,最后以某厂计划购买自动包装生产线为例,说明如何利用生命周期成本概念进行投资决策分析。     

约束理论在回收逆向物流中的应用

将约束理论应用于回收逆向物流中,以有效产出作为决策目标,构建了一个逆向物流回收配送整合决策模型.针对模拟算例,通过对比基于TOC的决策模型和基于成本最小化的传统决策模型的运算结果,说明了约束理论在解决这类问题中的有效性.     

OPTIMAL PRODUCTION PLANNING AND INVENTORY CONTROL*

This paper is concerned with the optimal production planning and inventory control. The first problem is a multiperiod production scheduling problem in which the objective is to minimize the operating cost for a planning period. This cost is composed principally of the sum of the production cost and inventory carrying cost. The second problem considers an inventory system with two decision variables in each planning period. These are the production schedule and work force which are to be determined so as to minimize the operating cost which includes the costs of changing the production rate, of changing the work force and of carrying the inventory. The maximum principle in the discrete form is used to reduce both the first problem which has N decision variables and the second problem which has 2N decision variables respectively to a series of two decision variables problems. The so-called sequential simplex pattern search technique is used to determine the optimal values of these two decision variables. Numerical examples are given to demonstrate the method.     

随机环境下班轮公司之间合作调箱研究

目的研究随机因素对2种调箱方式的影响,以体现合作调箱方式在随机环境下的优越性。方法综合考虑港口空箱需求和空箱运力限额的随机性,以决策周期内调箱总成本最小为目标,构建随机环境下班轮公司之间合作调箱模型,然后通过算例,研究不同港口服务水平和运力限制水平对2种调箱方式的影响,以及对调箱系统中随机因素进行灵敏度分析。结果当班轮公司采用保守-成本型决策、保守-服务型决策、冒险-成本型决策、冒险-服务型决策时,2种调箱方式的总成本差值分别为9638,22862,10710,19284美元。结论降低运力限制水平可以压缩调箱总成本,但是提高港口服务水平会增加调箱总成本,且当港口服务水平和运力限制水平均处于高值时,合作调箱方式的优势更加显著;与单独调箱方式相比,合作调箱方式可以降低调箱系统中随机因素波动对调箱总成本的影响。     

A Model for Sensitivity Analysis of Reimbursement in Hospitals

Hospital rate setting and financial planning decisions are complicated by the fact that 70% to 80% of the operating revenues generated from patients are covered by third parties (namely Blue Cross, Medicare, Medicaid). Reimbursement by the third parties for services is associated with a host of regulatory constraints which govern the cost finding procedures used, as well as the cost and rate increases allowed. In this paper we present two models to aid the hospital's financial decision makers understanding of the sensitivity in the cost allocation process. The models provide a means by which the decision maker can obtain answers to such questions as: “What is the impact of an increase in cost of Y, in the cost center D, on the total cost of revenue center M?” Management use of the above information is discussed. The paper illustrates the use of models in performing sensitivity analysis as opposed to brute force methods commonly used.