GOALI: Robust Product Design Selection under Uncertainty and for Competitive Advantage

本研究的目标是：(1)建立一个综合的框架和方法，以帮助稳健地选择产品设计，无论是单一产品还是产品线，考虑到测量客户偏好的不确定性、由于偏好随时间变化而引起的不确定性、竞争反应的不确定性和不可控工程设计参数的不确定性，并在此框架的基础上构建和验证原型决策支持系统。在该方法中，产品/生产线设计选择被视为包括两个主要阶段的选择：设计方案生成和设计方案评价。在第一阶段(设计模块)，将共性信息和工程设计公式中的不确定性一起输入到多目标稳健优化中，以生成设计方案的帕累托稳健最优集。在第二阶段(市场模块)，通过考虑客户偏好和选择，为集合中的每个备选方案生成需求和市场份额估计。根据需求函数和共性，为每个生成的设计备选方案计算成本函数，并用于计算每个备选方案的净利润现值。将在两个模块之间进行递归处理，以利用模拟方法说明两个模块中不可控参数的不确定性，并获得一套可靠的建议。如果成功，这项研究的结果将在工程设计、营销科学和管理的不同领域推进最先进的技术。这项调查的结果将在工程设计自动化和营销科学界广泛传播，将知识整合到马里兰大学的课程中，并将技术和技术转移到工业中。拟议的研究还将为学生提供机会，学习产品设计选择中涉及的商业和工程实践，以及通过基础研究提高此类实践的有效性的方法。

The objective of this research is to (1) develop an integrated framework and methodology to aid robust selection of product designs, either a single product or a product line, taking into account the uncertainties in measuring customer preferences, uncertainties due to changing preferences over time, uncertainties in competitive reactions, and uncertainty in uncontrollable engineering design parameters, and based on this framework (2) construct and validate a prototype decision support system. In the proposed approach, the product/product-line design selection is viewed as a selection with two main stages: design alternative generation and design alternative evaluation. In the first stage (design module), commonality information, along with uncertainty in the engineering design formulation, is fed into a multi-objective robust optimization to generate a Pareto robust optimal set of design alternatives. In the second stage (market module), demand and market share estimates are generated for each alternative in the set by accounting for customer preferences and choices. Based on the demand function and commonality, cost functions are calculated for each generated design alternative and are used to compute the net present value of profit for each alternative. A recursive process between the two modules will be undertaken to account for the uncertainty in uncontrollable parameters in both modules using simulation methods and to obtain a robust set of recommendations. If successful, the outcome of this research will advance the state of the art across different domains in engineering design, marketing science and management. The results of this investigation will be broadly disseminated in the engineering design automation and marketing science communities, the knowledge integrated into courses at the University of Maryland, and the technology and techniques transferred to industry. The proposed research will also provide opportunities for students to learn business and engineering practices involved in product design selection and ways to enhance the effectiveness of such practices through basic research.