A Robust-Reliable Decision-Making Methodology Based on a Combination of Stakeholders’ Preferences Simulation and KDD Techniques for Selecting Automotive Platform Benchmark

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Abstract
The automotive family design is known as one of the most complex engineering design problems with multiple groups of stakeholders involved from different domains of interest and contradictory attributes. Taking into account all stakeholders’ preferences, which are generally symmetrical, non-deterministic distributions around a mean value, and determining the right value of attributes for each alternative are two basic challenges for these types of decision-making problems. In this research, the possibility to achieve a robust-reliable decision by focusing on the two aforementioned challenges is explored. In the proposed methodology, a random simulation technique is used to elicit stakeholders’ preferences and determine the relative importance of attributes. The decision space and values of attributes are determined using the Knowledge Discovery in Databases (KDD) technique, and to achieve a robust-reliable decision, statistical and sensitivity analyses are performed. By implementing this methodology, the decision-maker is assured that the preferences of all stakeholders are taken into account and the determined values for attributes are reliable with the least degree of uncertainty. The proposed methodology aims to select benchmark platforms for the development of an automotive family. The decision space includes 546 automobiles in 11 different segments based on 34 platforms. There are 6223 unique possible states of stakeholders’ preferences. As a result, five platforms with the highest degree of desirability and robustness to diversity and uncertainty in the stakeholders’ preferences are selected. The presented methodology can be implemented in complex decision-making problems, including a large and diverse number of stakeholders and multiple attributes. In addition, this methodology is compatible with many Multi-Attribute Decision-Making (MADM) techniques, including SAW, AHP, SWARA, and TOPSIS.
Funding Information
  • The Slovak Scientific Grand Agency VEGA (2/0135/23, 18/CRT/6223)
  • Science Foundation Ireland Centre for Research Training in Artificial Intelligence (2/0135/23, 18/CRT/6223)

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