Evaluation of product conceptual design based on Pythagorean fuzzy set under big data environment

Abstract

The concept design evaluation phase of the new product launch is extremely important. However, current evaluation information relies mainly on the a priori knowledge of decision makers and is subjective and ambiguous. For this reason, a conceptual design solution decision model based on Pythagorean fuzzy sets in a big data environment is proposed. Firstly, we use the ability of big data to mine and analyze information to construct a new standard for product concept design evaluation in the big data environment. Secondly, the Pythagorean fuzzy set (PFS), Analytic Hierarchy Process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are integrated into a decision model. AHP, extended by the Pythagorean fuzzy set, is used to determine the weights of new conceptual design criteria in a big data environment. The Pythagorean fuzzy TOPSIS is used to prioritize alternative conceptual design solutions. The feasibility of the approach is proven with a practical case, the generalizability of the method is confirmed with two descriptive digital cases, and the reliability, validity, and superiority of the process are demonstrated with sensitivity analysis, comparative analysis, and computational complexity analysis.

Integrating Fuzzy Rough Sets with LMAW and MABAC for Green Supplier Selection in Agribusiness

“…The exclusive focus on economic metrics can inadvertently neglect vital environmental and social aspects, leading to adverse environmental impacts, reputational risks, and strained stakeholder relationships [27][28][29]. Furthermore, the subjective nature of qualitative assessments employed in traditional approaches introduces biases and inconsistencies, making it challenging to objectively evaluate suppliers' sustainability performance [30][31][32][33].…”

Optimizing Residential Construction Site Selection in Mountainous Regions Using Geospatial Data and eXplainable AI

“…The multi-criteria decision making (MCDM) approach, in particular the Analytical Hierarchy Process (AHP), is used to tackle complex decisions by assigning weights to thematic layers based on expert judgment and site-specific conditions, thus evaluating the relative importance of different parameters [24][25][26]. Despite its widespread application in geospatial zonation mapping, groundwater studies, and hazard mapping, the AHP faces significant challenges in dealing with imprecise input data and inherent uncertainties that can significantly affect the decision-making process [27][28][29][30][31]. These uncertainties can include the subjective judgments of experts, variability in data quality, and inconsistencies in the weighting of criteria, which can lead to less reliable results [27,32].…”

“…To improve accuracy and solve these problems, the integration of the AHP with fuzzy set theory, which forms the Fuzzy-AHP, has been proposed. This integration allows for a more nuanced consideration of uncertainties by incorporating linguistic variables and fuzzy numbers, thus refining accuracy in applications such as the selection of safe sites for residential building construction [28,29,33]. This study aims to develop integrated methods using remote sensing, geographic information systems (GISs), and Fuzzy-AHP for the selection of safe building sites in mountainous regions.…”