A quantitative approach to design alternative evaluation based on data-driven performance prediction. Issue 2 (April 2017)

• Record Type: Journal Article
• Authors: Zhang, Zi-jian; Gong, Lin; Jin, Yan; Xie, Jian; Hao, Jia
• Is Part Of: Advanced engineering informatics. Volume 32:Issue 2(2017:Apr.)
• Page Start: 52
• Page End: 65
• Publication Date: 2017-04
• Subjects: Design alternative evaluation -- Rough DEMATEL -- Quantitative evaluation -- Data-driven -- Performance prediction; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2016.12.009
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: A systematic approach is proposed to quantitatively evaluate design alternatives. The judgments of designers are quantified in the vague and subjective environment. Obtains the optimal alternative based on the data-driven performance prediction. Strengthens efficiency and objectivity by reducing vagueness and human involvement. Abstract: Design alternative evaluation in the early stages of engineering design plays an important role in determining the success of new product development, as it influences considerably the subsequent design activities. However, existing approaches to design alternative evaluation are overly reliant on experts' ambiguous and subjective judgments and qualitative descriptions. To reduce subjectivity and improve efficiency of the evaluation process, this paper proposes a quantitative evaluation approach through data-driven performance predictions. In this approach, the weights of performance characteristics are determined based on quantitative assessment of expert judgments, and the ranking of design alternatives is achieved by predicting performance values based on historical product design data. The experts' subjective and often vague judgments are captured quantitatively through a rough number based Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. In order to facilitate performance based quantitative ranking of alternatives at the early stages of design where no performance calculation is possible, a particle swarm optimization based support vector machine (PSO-SVM) is applied for historical data based performance prediction. The final ranking of alternatives given the predicted values of multiple performance characteristics is achieved through Višekriterijumska Optimizacija I kompromisno Rešenje (VIKOR). A case study is carried out to demonstrate the validity of the proposed approach.