On the effect of subjective, objective and combinative weighting in multiple criteria decision making: A case study on impact optimization of composites

Abstract

To date, no specific framework has been developed to guide composite structure designers to select the optimum fiber types and fabric weave patterns for a given application. This article aims to, first, investigate the effect of weighting methods in multiple criteria decision making (MCDM) and then arrive at a systematic framework for optimum weave pattern selection in fiber reinforced polymer (FRP) composites. Namely, via measured data from an industrial case study, the TOPSIS MCDM technique has been applied to choose the best candidate among different polypropylene/glass laminates. As an input to TOPSIS, different types of subjective and objective weighting methods were initially compared to assess the role of relative importance values (weights) of design criteria. These included the Entropy method, the modified digital logic (MDL) method, and the criteria importance through inter-criteria correlation (CRITIC) method. Next, two new subjective weighting methods, named ‘Numeric Logic (NL)’ and ‘Adjustable Mean Bars (AMB)’ methods, were introduced to give more practical and effective means to the decision makers during the weighting of criteria. In particular, compared to the MDL, the NL method increased the accuracy of assigned weights for an expert DM. On the other hand, the AMB provided a more interactive, visual approach through MCDM weighting process for less experienced DMs. Finally, a generalized combinative weighting framework is presented to show how different types of weightings may be combined to find more reliable rankings of alternatives. The combinative weighting could specifically accommodate different scenarios where a group of designers are involved and have different levels of experience, while given a large number of alternatives/criteria in highly nonlinear applications such as impact design of composite materials.