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An MAGDM method for design concept evaluation based on incomplete information.

Zhe Chen1,2, Zhao Pan1, Qing Ma1

  • 1Shandong Jiaotong University, Jinan, China.

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|November 23, 2022
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Summary
This summary is machine-generated.

This study addresses challenges in new product development by introducing a novel method for design concept evaluation with incomplete information. The proposed approach effectively determines missing data and ranks design schemes, improving R&D decision-making.

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Area of Science:

  • Engineering
  • Decision Science
  • Product Development

Background:

  • Design concept evaluation in R&D is challenged by decision-maker subjectivity and incomplete information.
  • Existing methods struggle with imprecise or missing data, hindering objective product development assessments.
  • Experts often use linguistic preferences, complicating quantitative analysis in decision matrices.

Purpose of the Study:

  • To develop a robust Multiple Attributes Group Decision-making (MAGDM) method for design concept evaluation under incomplete information.
  • To address the challenge of missing data by integrating a trust mechanism and attribute-based determination.
  • To enhance the accuracy and reliability of R&D concept selection processes.

Main Methods:

  • A three-step MAGDM approach incorporating a trust-based data repairing method for missing values.
  • A hybrid weighting technique combining the Analytic Hierarchy Process (AHP) and entropy for index attribute importance.
  • Application of the Rough-TOPSIS method for the final ranking of design concepts.

Main Results:

  • The proposed trust-based data repairing method effectively handles incomplete information in decision matrices.
  • The combined AHP-entropy approach provides a comprehensive weighting of attributes.
  • The Rough-TOPSIS method successfully ranks design schemes, demonstrating the method's practical utility.

Conclusions:

  • The developed MAGDM method offers an effective solution for design concept evaluation with incomplete information.
  • The integration of trust theory and attribute-based data repair enhances decision-making accuracy.
  • The method's successful application in a tourism product design case study validates its effectiveness.