基于非对称损失函数的过程均值与标准差优化设计

质量特性的过程均值与过程标准差的选定是一个重要的命题,它们是影响质量成本的重要因素.由顾客定义的特性目标均值以及初始标准差往往并不一定能使得总体的质量损失最小化。针对此问题,本文首先讨论了非对称的质量损失函数,并在此基础上构造出质量特性目标均值与标准差的优选模型,进而得到最优目标过程均值与标准差。在模型的应用中表明:其他情况不变的情况下,质量特性最优均值与最优标准差对质量损失系数具有一定的稳健性。     

最优过程均值和生产运行长度的确定

实际生产中,过程均值由于受到随机振荡的影响,经常从受控状态逐渐漂移到失控状态,从而导致大量不合格品的出现.针对这种情况,本文假定随机振荡次数服从泊松过程,每次振荡引起过程均值漂移相互独立且服从同一指数分布,结合不对称田口质量损失函数,建立了最佳初始过程均值的经济模型,并讨论了最优生产运行长度的确定.通过与初始过程均值设置在目标值处的情形比较,说明本文模型对降低生产成本的有效性。灵敏度分析表明了各参数对最优过程均值和生产运行长度的影响.     

线性不对称损失时非正态分布过程均值优化

针对常见的两种非正态分布———梯形分布和三角分布，研究线性不对称质量损失时其过程均值的优化问题，建立了梯形分布在五种不同情况下线性不对称质量损失的数学模型，基于以上模型给出了线性不对称质量损失时梯形分布最优过程均值的确定方法；研究三角分布在四种不同情况下线性不对称质量损失的数学模型，并给出了线性不对称质量损失时三角分布最优过程均值的确定方法。最后，用实例验证本过程均值优化模型的有效性。实例表明，应用线性不对称损失函数，适当的改变过程均值，可以有效地降低产品的质量损失，通过调整工艺过程将获得最佳经济效益。     

基于非对称损失的过程均值设计研究

在损失非对称的情况下，使工序加工出的产品均值等于目标值，并不会使期望损失最小，优化过程均值，使其接近目标值，尤为重要。本研究了三种典型的非对称损失的过程均值设计问题，探讨了非对称比率与质量损失率之间的关系，提出了有效偏移的概念，给出了具体的调整措施，最后给出一个实例。     

基于粗糙集和模糊C均值聚类的空战效能评估

针对评估指标的重要性不一,且存在冗余问题,基于粗集可辨识矩阵,提出了一种计算指标属性重要度和约简的有效、简便算法,对样本信息进行约简,并计算约简后各指标的权重.其中,针对连续属性值离散化过程可能造成信息损失问题,采用了模糊C均值聚类算法离散化连续属性值.最后,建立了基于粗糙集和模糊C均值聚类的空战效能评估模型,并通过实例验证了该模型的可行性和有效性.     

基于中国地震的损失模型(英文)

对损失分布的估计一直是保险公司的重要问题. 有多种参数方法以及非参数方法拟合损失分布. 本文作者提出了结合参数和非参数的方法来解决损失分布拟合问题. 首先通过超额均值图确定大小损失之间的阈限,再利用广义Pareto分布拟合阈值以上损失, 转换后的核密度估计拟合阈值以下损失. 最后, 通过实证分析将该方法和其他方法进行了误差分析比较, 取得了理想的结果.     

空间非参回归的变量选择

空间变系数回归模型是空间线性回归模型的重要推广,在实际中有广泛的应用.然而,这个模型的变量选择问题还没有解决.本文通过一般的M型损失函数将均值回归、中位数回归、分位数回归和稳健均值回归纳入同一框架下,然后基于B样条近似,提出一个能够同时进行变量选择和函数系数估计的自适应组内(adaptive group)L_r(r≥1)范数惩罚的M型估计量.新方法有几个显著的特点:(1)对异常点和重尾分布稳健;(2)能够兼容异方差性,允许显著变量集合随所考虑的分位点不同而变化;(3)兼顾了估计量的有效性和稳健性.在较弱假设条件下,建立了变量选择的oracle性质.随机模拟和实例分析验证了所提方法在有限样本时的表现.     

道德风险影响下的保险最优投资研究

在考虑道德风险的情况下,以均值方差准则为目标研究保险人最优投资问题.假设保险盈余过程服从C-L模型,金融市场上存在一种无风险资产和一种风险资产可供投资,其中风险资产的价格过程服从几何布朗运动.在纯道德风险保险契约设计中,借鉴相关研究对努力水平和效用化努力成本的假设,量化道德风险对盈余过程的影响.在均值方差目标下,建立保险人最优投资问题的广义Hamilton-Jacobi-Bellman(HJB)方程,给出保险人时间一致的均衡投资策略和价值函数.结果显示累计索赔比例参数越大,公司对最优努力水平越敏感,采取措施降低道德风险有利于公司收益提升;努力成本参数越大,公司会降低努力水平减少支出,避免损失.     

关于田口参数设计的专家评论(Ⅱ)

4.数据分析.信噪比,数据变换,广义线性模型Madhav Phadke 与某些统计文献中提出的数据变换不同,选择信噪比是为了确定信号因子和质量特性间的理想关系,并估价噪声因子对选择理想函数的灵敏度,不是确定稳定方差的数据变换.由于它允许估价噪声因子对理想函数的灵敏度,采取合适的调节也是重要的. 以下我将证明常见类型望目特性信噪比的合理性.令 为目标值,与 有关的二次损失为. Q=(μ-t)2+σ2(4.1)其中μ和σ表示响应变量的均值和标准差.假设两种不同工艺的均值和标准差已知,如何判别哪个工艺条件更合适?如何估价它们对噪声因子的灵敏性?为了…     

不完全信息下损失风险的研究

在获得损失分布不完全信息情况下,提出用方差和熵共同度量损失风险的方法.在不完全信息条件下,通过最大熵原理在最不确定的情况下得到最大熵损失分布,并获得了损失分布的熵函数值.用熵值度量损失分布对于均匀分布的离散程度,从而度量概率波动带来的风险;用方差度量损失对于均值的离散程度,从而度量状态波动带来的风险.由于熵是与损失变量更高阶矩信息相联系的,所以新方法是从更全面的角度对损失风险的预测.通过算例,进一步看出在获得高阶矩信息下,熵参与风险度量的必要性.     

Economic selection of process mean for single-vendor single-buyer supply chain

Process mean selection for a container-filling process is an important decision in a single-vendor single-buyer supply chain. Since the process mean determines the vendor’s conforming and yield rates, it influences the vendor–buyer decisions regarding the production lot size and number of shipments delivered from the vendor to buyer. It follows, therefore, that these decisions should be determined simultaneously in order to control the supply chain total cost. In this paper, we develop a model that integrates the single-vendor single-buyer problem with the process mean selection problem. This integrated model allows the vendor to deliver the produced lot to buyer in number of unequal-sized shipments. Moreover, every outgoing item is inspected, and each item failing to meet a lower specification limit is reprocessed. Further, in order to study the benefits of using this integrated model, two baseline cases are developed. The first of which considers a hierarchical model where the vendor determines the process mean and schedules of production and shipment separately. This hierarchical model is used to show the impact of integrating the process mean selection with production/inventory decisions. The other baseline case is studied in the sensitivity analysis where the optimal solution for a given process is compared to the optimal solution when the variation in the process output is negligible. The integrated model is expected to lead to reduction in reprocessing cost, minimal loss to customer due to the deviation from the optimum target value, and consequently, providing better products at reduced cost for customers. Also, a solution procedure is devised to find the optimal solution for the proposed model and sensitivity analysis is conducted to investigate the effect of the model key parameters on the optimal solution.     

Joint economic design of EWMA control charts for mean and variance

Control charts with exponentially weighted moving average (EWMA) statistics (mean and variance) are used to jointly monitor the mean and variance of a process. An EWMA cost minimization model is presented to design the joint control scheme based on pure economic or both economic and statistical performance criteria. The pure economic model is extended to the economic-statistical design by adding constraints associated with in-control and out-of-control average run lengths. The quality related production costs are calculated using Taguchi’s quadratic loss function. The optimal values of smoothing constants, sampling interval, sample size, and control chart limits are determined by using a numerical search method. The average run length of the control scheme is computed by using the Markov chain approach. Computational study indicates that optimal sample sizes decrease as the magnitudes of shifts in mean and/or variance increase, and higher values of quality loss coefficient lead to shorter sampling intervals. The sensitivity analysis results regarding the effects of various inputs on the chart parameters provide useful guidelines for designing an EWMA-based process control scheme when there exists an assignable cause generating concurrent changes in process mean and variance.     

Reverse programming the optimal process mean problem to identify a factor space profile

For the manufacturer that intends to reduce the processing costs without sacrificing product quality, the identification of the optimal process mean is a problem frequently revisited. The traditional method to solving this problem involves making assumptions on the process parameter values and then determining the ideal location of the mean based upon various constraints such as cost or the degree of quality loss when a product characteristic deviates from its desired target value. The optimal process mean, however, is affected not only by these settings but also by any shift in the variability of a process, thus making it extremely difficult to predict with any accuracy. In contrast, this paper proposes the use of a reverse programming scheme to determine the relationship between the optimal process mean and the settings within an experimental factor space. By doing so, one may gain increased awareness of the sensitivity and robustness of a process, as well as greater predictive capability in the setting of the optimal process mean. Non-linear optimization programming routines are used from both a univariate and multivariate perspective in order to illustrate the proposed methodology.     

Economic manufacturing quantity,optimum process mean,and economic specification limits setting under the rectifying inspection plan

Economic manufacturing quantity, process mean, and specification limits setting are three important methods for the inventory and quality control problems. In the imperfect production system, we usually consider the manufacturing quantity for reducing the inventory cost, determine the process level for reducing the production cost, and select the specification limits for screening the products. In this paper, we propose the above integrated model based on the application of rectifying inspection plan for obtaining maximum expected total profit of product. The asymmetric quadratic quality loss function is adopted for measuring the product quality. The sensitivity analyses of parameters are provided for illustration.     

Simultaneous kriging-based estimation and optimization of mean response

Robust optimization is typically based on repeated calls to a deterministic simulation program that aim at both propagating uncertainties and finding optimal design variables. Often in practice, the “simulator” is a computationally intensive software which makes the computational cost one of the principal obstacles to optimization in the presence of uncertainties. This article proposes a new efficient method for minimizing the mean of the objective function. The efficiency stems from the sampling criterion which simultaneously optimizes and propagates uncertainty in the model. Without loss of generality, simulation parameters are divided into two sets, the deterministic optimization variables and the random uncertain parameters. A kriging (Gaussian process regression) model of the simulator is built and a mean process is analytically derived from it. The proposed sampling criterion that yields both optimization and uncertain parameters is the one-step ahead minimum variance of the mean process at the maximizer of the expected improvement. The method is compared with Monte Carlo and kriging-based approaches on analytical test functions in two, four and six dimensions.     

The canning problem revisited: The case of capacitated production and fixed demand

We report on optimizing a ‘variable yield’ machine-fill type of production process with limited capacity n, a fixed demand d and a process yield rate that depends on a controllable mean setting. Using a profit function that includes both the cost of production and a penalty for under-production, we show that obtaining an optimal mean setting is straightforward. As the ingredient cost becomes small compared to recycling and production costs, the relative profit is found to be a strongly asymmetric function of the process mean. The resulting managerial insights suggest that, with respect to optimal mean setting, for a wide variety of cost parameters, overfilling is in general better than underfilling.     

Median Predictive Cost of Error with an Asymmetric Cost Function

The problem of reducing predictive cost is considered in the case when the cost of error function is not symmetric and the optimality criterion is the minimization of the median cost of the error of prediction. Examples are given and comparisons made with the usual solution based on the minimization of the mean cost. In the case of a Gaussian process, the median solution is found to be a simple additive adjustment to the predictive mean, and far easier to compute than the solution based on expected cost.     

Process mean determination under constant raw material supply

Setting the mean (target value) for a production process is an important decision for a producer when material cost is a significant portion of production cost. Because the process mean determines the process conforming rate, it affects other production decisions, including, in particular, production setup and raw material procurement policies. In this paper, we consider the situation in which the product of interest is assumed to have a lower specification limit, and the items that do not conform to the specification limit are scrapped with no salvage value. The production cost of an item is a linear function of the amount of the raw material used in producing the item, and the supply rate of the raw material is finite and constant. Furthermore, it is assumed that quantity discounts are available in the raw material cost and that the discounts are determined by the supply rate. Two types of discounts are considered in this paper: incremental quantity discounts and all-unit quantity discounts. A two-echelon model is formulated for a single-product production process to incorporate the issues associated with production setup and raw material procurement into the classical process mean problem. Efficient solution algorithms are developed for finding the optimal solutions of the model.     

Optimal production correction and maintenance policy for imperfect process

This study considers imperfect production processes that require production correction and maintenance. Two states of the production process are performed, namely: the type I state (out-of-control state) and the type II state (in-control state). At the beginning of the production of the each renewal cycle, the state of the process is assumed not always to be restored to “in-control”. The type I state involves the adjustment of the production mechanism, whereas the type II state does not. Production correction is either imperfect; worsening a production system, or perfect, returning it to “in-control”. After N + 1 type I states, the operating system must be maintained and returned to the beginning condition. The mean loss cost due to reproduction through production correction per the total expected cost until the N + 1 type I states are entered successively is determined. The existence of a unique and finite optimal N for an imperfect process under certain reasonable conditions is shown. A numerical example is presented.