基于模糊逻辑推理的改进多响应参数优化方法研究

研究一种基于模糊逻辑推理的改进多响应参数优化方法.通过满意度函数求解各个响应的满意度值,然后经过模糊逻辑推理将多个响应的满意度值转化为模糊推理等级(FRG).模糊推理规则可以充分考虑实际工艺操作经验和专家知识,利用响应间相互关系的机理和统计信息,得到的模糊推理等级能够客观反映多个质量特性的整体水平.将基于模糊逻辑推理的改进多响应参数优化方法运用到铣切削生产过程的参数优化设计中,使两个响应的粗糙程度均有所下降,验证了方法的有效性.     

基于模式搜索的渴求函数法在多响应优化中的应用

渴求函数法是处理多响应参数优化的常用方法之一,它通过最大化总体渴求值获得因子的最佳水平组合.然而,随着因子个数和响应个数的增加,渴求函数往往变得多约束、多峰分布、高度非线性,传统的基于梯度的优化算法不适用.根据因子及响应个数等问题复杂程度不同,提出了以模式搜索算法为基础,用重叠等值线图或遗传算法设定模式搜索的起始点,对总体渴求函数进行寻优的新方法.算例验证了该方法的有效性.     

基于满意度函数的多响应稳健优化模型及实证研究

针对传统的综合满意度模型在研究多响应时较少考虑噪声因素的问题,本文通过引入噪声因素对传统的满意度模型实施改进,进行多响应稳健优化分析。首先,构建包含可控因素和噪声因素的多质量特性的响应曲面模型;其次,借助传统综合满意度模型的构建方法,将多质量特性的响应曲面模型整合为改进的满意度模型;最后,将信噪比作为衡量改进的满意度模型稳健优化的指标,得到稳健优化的参数组合。实证研究表明:构建同时包含可控因素和噪声因素的改进的满意度模型是可行的,在此模型的基础上利用信噪比能够有效地找到多响应稳健优化的参数组合。     

基于似无关回归的多元稳健损失函数方法

针对多响应参数优化问题,考虑响应间相关性和可控因子波动的影响,提出了一种基于似无关回归的多元稳健损失函数方法。首先采用似无关回归对模型拟合和过程优化中的相关参数进行估计,更有效地利用响应间相关性信息;然后利用给定点处梯度信息来估计可控因子波动对过程稳健性的影响。算例表明,当响应间存在相关性时,与最小二乘方法相比,采用似无关回归拟合的响应曲面模型精度更高;与传统质量损失函数相比,在采用相同质量成本矩阵时,采用稳健损失函数方法得到的最优解处期望质量损失更小。     

基于期望函数的土遗址锚固参数组合优化方法

针对土遗址锚固工程需求，提出了一种基于期望函数的锚固参数组合优化方法.该方法通过对锚固长度、锚孔直径等参数的组合优化,获得了最大锚固力与最小遗址伤害的良好平衡.试验设计为全因子试验，利用响应面方法构建分析模型，而后将统计中的期望函数法引入锚固参数优化中，建立了多重目标响应指标与锚固参数水平的关系.研究结果表明:当分别满足锚固力最大化和遗址伤害最小化目标时，相应的锚固参数取值间存在冲突；多重响应优化能够确定目标响应需求下锚固参数的可行域范围，方便工程设计人员根据实际工程条件对锚固参数进行可视化取值.     

演化均匀优化算法

针对演化算法，使用随机法产生初始点带来的缺陷和均匀设计方法的优点，提出了演化均匀优化算法，进行了算法收敛性分析，并把它应用于多峰函数的数值优化计算，给出了计算示例，结果表明该方法是一种可行而且有效的优化算法．     

基于模糊决策的多响应参数优化方法

为增强产品的鲁棒性,使产品质量得到明显改善和提升,本文研究一种基于模糊决策的多响应参数优化方法。将满意度函数与模糊逻辑推理结合,对复杂生产系统进行简化,避免权值设置的同时,也充分提取各响应机理之间有效信息,实现了生产过程的模糊决策。然后在主效应分析的基础上构建神经网络预测模型,获得最优工艺参数组合。最后将本文方法应用于铁基合金热喷涂工艺参数优化问题中,使热喷涂工艺稳健性得到提升,证明了文中方法是有效的。     

求解全局优化问题的填充函数算法

填充函数法是求解多变量、多极值函数全局优化问题的有效方法.这种方法的关键是构造填充函数.本文在无Lipschitz连续条件下,对一般无约束最优化问题提出了一类单参数填充函数.讨论了其填充性质,并设计了一个求解约束全局优化问题的填充函数算法,数值实验表明,算法是有效的.     

基于模糊推理的改进多响应参数稳健优化方法

提出一种基于模糊逻辑推理的改进多响应稳健参数优化方法,建立模糊逻辑推理系统,由多个响应的满意度值推算出一个模糊推理等级.拟合含有噪声因子的多响应过程的神经网络预测模型,可以预测参数优化结果,减少实验量.通过案例分析验证了所提稳健参数优化方法的有效性.     

一个新的填充函数

填充函数法是一种解无约束全局极小化问题的方法.这种方法的关键是构造填充函数,在已发表的文献中已经介绍了几种填充函数.在此介绍只含一个参数的填充函数,并且根据此填充函数提出了一种填充函数算法.给出了用这种填充函数法解几个测试问题的计算结果.     

Dynamic conformance and design quality in a supply chain: an assessment of contracts’ coordinating power

We consider a two-echelon supply chain involving one manufacturer and one supplier who collaborate on improving both design and conformance quality. Design quality is supposed to increase product desirability, and therefore market demand, while conformance quality should reduce the proportion of defective items, and therefore increase the manufacturer’s sales revenue. We investigate how the supply chain parties allocate effort between design and conformance quality under both cooperative and non-cooperative settings in an intertemporal framework. Furthermore, we evaluate wholesale price contracts and revenue-sharing contracts in terms of their performance and coordination power. We show that although a revenue-sharing contract enables the manufacturer to effectively involve the supplier in quality improvement, neither contract type allows for perfect coordination resulting in the quality that can be achieved by a cooperative supply chain. We thus suggest a reward-based extension to the revenue-sharing contract, to ensure system-wide optimal quality performance. Importantly, we find that the supplier would be better off adopting a reward-based revenue sharing contract and refusing a standard revenue-sharing contract, while the opposite would be true for the manufacturer.     

An interactive desirability function method to multiresponse optimization

Multiresponse optimization problems often involve incommensurate and conflicting responses. To obtain a satisfactory compromise in such a case, a decision maker (DM)’s preference information on the tradeoffs among the responses should be incorporated into the problem. This paper proposes an interactive method based on the desirability function approach to facilitate the preference articulation process. The proposed method allows the DM to adjust any of the preference parameters, namely, the shape, bound, and target of a desirability function in a single, integrated framework. The proposed method would be highly effective in generating a compromise solution that is faithful to the DM’s preference structure.     

Robust optimization for multiple responses using response surface methodology

Typically in the analysis of industrial data for product/process optimization, there are many response variables that are under investigation at the same time. Robustness is also an important concept in industrial optimization. Here, robustness means that the responses are not sensitive to the small changes of the input variables. However, most of the recent work in industrial optimization has not dealt with robustness, and most practitioners follow up optimization calculations without consideration for robustness. This paper presents a strategy for dealing with robustness and optimization simultaneously for multiple responses. In this paper, we propose a robustness desirability function distinguished from the optimization desirability function and also propose an overall desirability function approach, which makes balance between robustness and optimization for multiple response problems. Simplex search method is used to search for the most robust optimal point in the feasible operating region. Finally, the proposed strategy is illustrated with an example from the literature. Copyright © 2009 John Wiley & Sons, Ltd.     

最优绩效分配的多目标优化方法

如何体现某种程度的激励并兼顾公平性是绩效分配问题研究的关键之一. 基于考核对象的个体差异性, 对考核对象进行分类处理, 通过引入以体现激励程度的控制参数和所有考核对象的基础工作量为变量的分值转化函数与满意度函数, 建立以所有考核对象的总体满意度的最大化和考核对象的满意度尽可能均衡为目标的多目标优化模型. 进而利用多目标优化模型的epsilon-约束标量化方法证明了弱有效解的存在性. 作为其应用, 研究了某高校教师的最优绩效分配问题.     

A robust desirability function method for multi-response surface optimization considering model uncertainty

A robust desirability function approach to simultaneously optimizing multiple responses is proposed. The approach considers the uncertainty associated with the fitted response surface model. The uniqueness of the proposed method is that it takes account of all values in the confidence interval rather than a single predicted value for each response and then defines the robustness measure for the traditional desirability function using the worst case strategy. A hybrid genetic algorithm is developed to find the robust optima. The presented method is compared with its conventional counterpart through an illustrated example from the literature.     

Optimization of generalized desirability functions under model uncertainty

Desirability functions are increasingly used in multi-criteria decision-making which we support by modern optimization. It is necessary to formulate desirability functions to obtain a generalized version with a piecewise max type-structure for optimizing them in different areas of mathematics, operational research, management science and engineering by nonsmooth optimization approaches. This optimization problem needs to be robustified as regression models employed by the desirability functions are typically built under lack of knowledge about the underlying model. In this paper, we contribute to the theory of desirability functions by our robustification approach. We present how generalized semi-infinite programming and disjunctive optimization can be used for this purpose. We show our findings on a numerical example. The robustification of the optimization problem eventually aims at variance reduction in the optimal solutions.     

Some Methods Based on the D-Gap Function for Solving Monotone Variational Inequalities

The D-gap function has been useful in developing unconstrained descent methods for solving strongly monotone variational inequality problems. We show that the D-gap function has certain properties that are useful also for monotone variational inequality problems with bounded feasible set. Accordingly, we develop two unconstrained methods based on them that are similar in spirit to a feasible method of Zhu and Marcotte based on the regularized-gap function. We further discuss a third method based on applying the D-gap function to a regularized problem. Preliminary numerical experience is also reported.     

多重响应的非参数预测的渐近贝叶斯设计

本文研究在单位方体上多重响应预测的试验设计问题.将多重响应函数视作相互独立的无限维随机过程的实现,因此我们采用非参数贝叶斯方法及渐近技术建立渐近贝叶斯设计准则.在一定条件下,我们证明单位方体上的均匀设计测度是最优渐近贝叶斯设计.