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

工程师们已经开始使用市场营销学、经济学和心理学中发展起来的客户偏好模型来设计更好地满足客户需求的产品。虽然这样的模型对于营销目的是足够的，但在工程设计环境中按原样使用时，它们通常会引入重大错误。尽管许多工程研究人员普遍使用客户偏好模型，但该领域还没有足够的方法来评估需求模型在此背景下使用的准确性或适当性。这一早期概念探索性研究(AGUGER)奖为基础研究提供支持，以开发指标和测试程序，以评估与可能误导工程设计人员的客户偏好模型相关的各种错误。结果将允许工程设计人员使用估计方法来构建需求模型，从而将设计选择和优化中的错误降至最低。通过这项工作开发的指标还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，还将开展几项教育和传播活动，以改善学生对模型评估技术的学习，并促进联邦机构使用评估方法，这些机构利用需求模型为其在交通部门的技术开发提供资金和监管信息。研究目标是产生(1)工程设计特定指标，将评估与需求模型相关的估计偏差，(2)展示需求模型估计偏差对最优设计变量选择的重要性，以及(3)确定一个或多个需求估计方法，以减少影响设计选择和优化的偏差。这项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。我们将研究影响需求梯度的两类参数相对于工程设计变量的估计偏差--客户偏好系数和总需求估计。将测试多个指标，以将需求模型预测与合成客户购买数据进行比较，在合成客户购买数据中，估计参数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指标进行评估(S)。最后，通过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，通过比较使用已识别的减少参数偏差的估计方法和没有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.