在线定制的产品涉及到设计、生产等复杂环节，所以定制品的方案设计及其可行性等信息的快速反馈是实施在线定制的难点。提出基于CBR（case-based reasoning）的匹配定制方法，减少定制时间避免不必要的重复设计。首先运用层次分析法和质量功能展开确定需求工程设计权重；结合模糊数学修改高斯函数，构造出一种具有接近客观现实、方便处理、区分性强等优点的匹配度计算方法；利用Beta分布构建机器学习方法，得到随市场偏好变动而调整的匹配度阀值。其次针对顾客对不同产品的价格敏感度不同，引入调节因子修正最终匹配值对比匹配阀值从而获得定制产品的设计方案及可行性并反馈。最后以电冰箱定制为例，证明该方法有效且易于实施。

Online customized product involves complex processes, including design and production, and hence the quick feedback of the design and its feasibility is important and difficult for implementing online customization. Therefore, a case-based reasoning (CBR) customized method is proposed, which can shorten the time for designing and avoid unnecessary repeated design. Firstly, the weights of demand and engineering design are determined by quality function deployment(QFD) and analytic hierarchy process(AHP). And a more objective, conveniently-handled and easily-distinguished method based on the Gaussian member function is constructed to calculate the matching value. Besides, a machine learning method is constructed on the basis of Beta distribution. Matching threshold can be obtained through machine learning, which changes depending on the market. Secondly, according to customers' diversified sensitivities towards various products, regulatory factors are introduced to adjust the final matching value. By comparing the matching threshold, the design and its feasibility can be acquired. Finally, customized refrigerator is taken as an example to prove the practicability and effectiveness of the method.

国家科技支撑计划资助项目（2015BAF05B03）。