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Selection and use of a multi-criteria decision aiding method in the context of conceptual design with imprecise information: Application to a solar collector development

机译:在信息不精确的概念设计中选择和使用多准则决策辅助方法:在太阳能收集器开发中的应用

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摘要

Making decisions on a sound basis in early phases is one of the most difficult challenges in the product development process, especially when dealing with immature concepts. Moreover, life-cycle cost can be influenced up to 70% by decisions taken during the conceptual design phases. The need for reliable multi-criteria decision aiding methods is thus greater in these phases. Various multi-criteria decision aiding methods are proposed and used in the literature. The main criticism of these methods is that they usually produce contradictory results for the same problem. In this work, seven widely used multi-criteria decision aiding methods (weighed sum, weighted product, Kim and Lin, compromise programming, TOPSIS, quadratic mean and ELECTRE I) are analysed. This analysis was based on a real industrial case to develop a solar collector. The proposed multi-criteria decision aiding methods were compared in terms of three criteria deemed relevant in the relevant context: (1) adaptation of the type of results the multi-criteria decision aiding method is expected to bring, (2) correct handling of input information and (3) adaptation of the degree of compensation. Based on these criteria, it was proven that weighted product is the most appropriate multi-criteria decision aiding method in our case. In addition, it has been demonstrated that sensitivity analysis can improve the benefit of using the multi-criteria decision aiding method chosen when dealing with imprecise information due to immaturity of concepts.
机译:在早期阶段合理地做出决策是产品开发过程中最困难的挑战之一,尤其是在处理不成熟的概念时。此外,在概念设计阶段做出的决定最多可影响生命周期成本的70%。因此,在这些阶段中,对可靠的多准则决策辅助方法的需求更大。文献中提出并使用了多种多准则决策辅助方法。对这些方法的主要批评是,对于相同的问题,它们通常会产生矛盾的结果。在这项工作中,分析了七种广泛使用的多准则决策辅助方法(加权总和,加权乘积,Kim和Lin,折衷编程,TOPSIS,二次均值和ELECTRE I)。该分析基于开发太阳能收集器的实际工业案例。根据在相关情况下认为相关的三个标准对建议的多标准决策辅助方法进行了比较:(1)适应多标准决策辅助方法带来的结果类型;(2)正确处理输入信息和(3)补偿程度的适应。基于这些标准,已证明加权积是本例中最合适的多标准决策辅助方法。此外,已经证明,由于概念的不成熟,敏感性分析可以提高在处理不精确信息时使用多标准决策辅助方法的好处。

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