EAGER: Metrics to Evaluate Customer Preference Models for use in Engineering Design Optimization

工程师们已经开始使用在市场营销、经济学和心理学中发展起来的顾客偏好模型来设计更好地满足顾客需求的产品。虽然这样的模型对于市场营销目的是足够的，但是在工程设计环境中使用时，它们通常会引入严重的错误。尽管许多工程研究人员普遍使用客户偏好模型，但该领域并没有足够的方法来评估在这种情况下使用的需求模型的准确性或适当性。探索性研究（EAGER）的早期概念资助为基础研究提供支持，以开发度量标准和测试程序，以评估与客户偏好模型相关的各种错误，这些错误可能会误导工程设计人员。结果将允许工程设计师使用估算方法来构建需求模型，从而最大限度地减少设计选择和优化中的错误。通过这项工作开发的度量也将允许从业者评估需求模型，并为他们的设计问题选择最合适的模型。此外，将开展若干教育和传播活动，以提高学生对模型评价技术的学习，并促进联邦机构使用评估方法，这些机构采用需求模型来为其在运输部门的技术发展提供资金和监管方面的信息。研究目标是产生(1)评估与需求模型相关的估计偏差的工程设计特定度量，(2)需求模型估计偏差对最优设计变量选择的重要性的证明，以及(3)确定一种或多种减少影响设计选择和优化的偏差的需求估计方法。本研究将利用离散选择分析和计量经济学估计来确定适合工程设计的度量和估计方法。我们将研究影响工程设计变量需求梯度的两类参数的估计偏差——客户偏好系数和总需求估计。将测试多个指标，以比较需求模型预测与合成的客户购买数据，其中估计和真实参数之间的偏差是已知的。计量经济学中提出的几种不同的需求估计方法将使用确定的度量来评估。最后，将使用一个优化案例研究来说明需求模型偏差对最优设计变量的影响，通过比较使用确定的估计方法减少参数偏差与不减少参数偏差的估计方法的结果。

Engineers have begun to use customer preference models developed in marketing, economics, and psychology to design products that better meet customer desires. While such models are adequate for marketing purposes, they often introduce significant errors when used as-is in an engineering design context. Despite the prevalent use of customer preference models by many engineering researchers, the field does not have adequate methods for evaluating the accuracy or appropriateness of demand models for use in this context. This EArly-concept Grant for Exploratory Research (EAGER) award provides support for fundamental research to develop metrics and a test procedure to evaluate various errors associated with customer preference models that can mislead engineering designers. Results will allow engineering designers to construct demand models using estimation methods that minimize errors in design selection and optimization. The metrics developed through this work will also allow practitioners to evaluate demand models and select the most appropriate model for their design problem. In addition, several education and dissemination activities will be conducted to improve student learning of model evaluation techniques and facilitate use of the evaluation methods by federal agencies that employ demand models to inform their funding and regulation of technology development in the transportation sector.The research objectives are to produce (1) engineering-design specific metrics that will evaluate the estimation biases associated with demand models, (2) a demonstration of the significance of demand-model estimation biases on optimal design variable selection, and (3) identification of one or more demand estimation methods that reduce biases affecting design selection and optimization. The research will draw upon discrete choice analysis and econometric estimation to identify metrics and estimation methods that are appropriate for engineering design. Estimation biases of two types of parameters that affect demand gradients with respect to engineering design variables -- customer preference coefficients and aggregate demand estimates -- will be examined. Multiple metrics will be tested to compare demand-model predictions with synthetic customer purchase data in which biases between the estimates and true parameters are known. Several different demand estimation methods proposed in econometrics will be evaluated using the identified metric(s). Finally, an optimization case study will be used to illustrate the influence of demand model biases on optimal design variables by comparing results using the identified estimation method that reduces parameter biases with one that does not.