A general framework for multiresponse optimization problems based on goal programming
A general framework for multiresponse optimization problems based on goal programming
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DOI：
10.1016/j.ejor.2007.05.030
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发表时间：
2008-09
期刊：
影响因子：
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通讯作者：
R. Kazemzadeh;M. Bashiri;A. Atkinson;R. Noorossana
中科院分区：
文献类型：
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作者：
R. Kazemzadeh;M. Bashiri;A. Atkinson;R. Noorossana
设置过程变量以满足过程中所需的质量特性（或响应变量）规范是过程质量控制中的常见问题之一。但通常过程中存在不止一种质量特征，实验者试图同时优化所有这些特征。由于响应变量在尺度、测量单位、最优类型及其偏好等属性上有所不同，因此MRS问题的模型构建和优化有不同的方法。本研究根据一些现有的工作和某些类型的相关决策者提出了 MRS 问题的通用框架，并试图将所有特征聚合到一种方法中。所提出的框架包含四个非期望部分：偏差、响应变化、预测错误以及与响应特定区域的分离。我们用两个文献示例展示了所提出的框架，并通过与提到的现有作品进行比较来讨论结果。
Setting of process variables to meet a required specification of quality characteristic (or response variable) in a process, is one of the common problems in the process quality control. But generally there are more than one quality characteristics in the process and the experimenter attempts to optimize all of them simultaneously. Since response variables are different in some properties such as scale, measurement unit, type of optimality and their preferences, there are different approaches in model building and optimization of MRS problems. This study propose a general framework in MRS problems according to some existing works and some types of related decision makers and attempts to aggregate all of characteristics in one approach. The proposed framework contains four non-desirability parts of bias, response variation, errors in predictions and separation from responses’ specific region. We demonstrate the proposed framework with two examples of the literature and the results has been discussed with comparing of mentioned existing works.