Adopting hybridized multicriteria decision model as a decision tool in engineering design

Abstract

Purpose The purpose of this paper is to determine the suitability of adopting hybridized multicriteria decision-making models as a decision tool in engineering design. This decision tool will assist design engineers and manufacturers to determine a robust design concept before simulation and manufacturing while all the design features and sub features would have been identified during the decision-making process. Design/methodology/approach Fuzzy analytical hierarchy process (FAHP) and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) are hybridized and applied to obtain optimal design of a reconfigurable assembly fixture (RAF) from a set of alternative design concepts. Design features and sub features associated with the RAF are identified and compared using fuzzified pairwise comparison matrices to obtain weights of their relative importance in the optimal design. The FAHP obtained the fuzzy synthetic extent (FSE) values of the design features and sub features. The FSE values are used as weights of the design features and sub features in generating the decision matrix. FTOPSIS and FTOPSIS based on left and right scores were adopted to predict effects of the weights. Results were obtained for normalized and unnormalized weights of the design features and its effects on the relative closeness coefficients of the design alternatives. Findings The improved performance of the FTOPSIS based on left and right scores is due to the involvement of the left and right scores of weights of the design features in the computation of distances from positive and negative ideal solutions. Embedding the weights of the design features in the normalized decision matrix before estimating the distances of the design concepts from ideal solutions reduces the dependency of the closeness coefficients on the weights of the design features. This also decreases the difference in the final values of the design concepts. In essence, the weights of the design features have an impact in the closeness coefficient. There is reduction in the closeness coefficients of the design concepts due to normalization of the weights of the design features. However, normalizing the weights of the design features did not affect the variations in the final values of the design concept. As the final value of the design concepts can be influenced by the normalized weights of the design features, it can be implied that normalization of weights of the sub features will also affect the decision matrix. The study has been able to proof that hybridizing FAHP and FTOPSIS can produce effective results for decisions on optimal design by the application of FTOPSIS based on left and right scores rather than the general FTOPSIS. Originality/value This research develops a hybridized multicriteria decision-making model for decision-making in engineering design. It presents a detailed extension of hybridized FAHP and FTOPSIS based on left and right scores as a useful tool for considering the relative importance of design features and sub features in optimal design selection.

Fusing Multi-Attribute Decision Models for Decision Making to Achieve Optimal Product Design

“…In other studies, the decision matrix is usually obtained by apportioning crisp values [5,14] or fuzzy numbers [1] to represent the availability of design features in the design alternatives. Also, in other studies involving application of hybridized multi attribute decision models to conceptual design, the decision matrices are created by aggregating the availability of sub features in the alternative designs using fuzzy numbers [34,35]. The reliability of these methods depends on the expertise of the design engineers to accurately quantify the availability of the sub features in the alternatives.…”

“…Some of these applications includes supplier selection [27], auxiliary systems of ship main engines [28], green supply chain implementation in the textile industry [29], gas turbine component selection [30], healthcare decision making [31], combination of materials selection [32] and Steam boilers [33]. However, extending the application of hybridized models to ranking of design concepts is still a decision task of interest because only few article has addressed such approach [34,35]. The choice of models to select for integration depends on the computational procedure of the models and nature of the features used in the decision process.…”

Design concept evaluation technique via functional link matrix and fuzzy VIKOR based on left and right scores

“…Contrary to this approach, determination of weights of design features have been achieved through Fuzzified Pairwise Comparison Matrix (FPCM). This usually occurs whenever the fuzzy AHP approached is applied (Olabanji & Mpofu, 2020b;Renzi et al, 2015) or when it is hybridized with other MCDM models (Olabanji & Mpofu, 2020c). The FPCM are solved by determining the Fuzzy Synthetic Extent values of the design features (Güngör et al, 2011).…”

“…When it is required to rank alternative design concepts with a consideration of the interrelationships and dependencies of the design features, the MADM approach is usually applied because it provides a clear picture of the contributions of each design features to the selected optimal design. Two major tasks in the MADM approach is the determination of weights for the design features and obtaining ratings for availability of the design features in the alternative designs without bias (Olabanji & Mpofu, 2020c;Tiwari et al, 2016).…”

“…The decisions from each team is averaged to obtain a result or used separately to make decisions on optimal design with an implication that the final decision is based on the different weights from the decision makers or design teams. This implies that there are different solutions based on different apportioning of weights to design features and determination of availability of the design features present in the design concepts (Olabanji & Mpofu, 2020c).…”