A Decision Modeling Approach for Data Acquisition Systems of the Vehicle Industry Based on Interval-Valued Linear Diophantine Fuzzy Set

M. J. Baqer, H. A. AlSattar, Sarah Qahtan, A. A. Zaidan, Mohd Azri Mohd Izhar and Iraq T. Abbas
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M. J. Baqer: Department of Computer Science, School of Computing Faculty of Engineering, University Technology Malaysia, Johor, Malaysia
H. A. AlSattar: ��Department of Business Administration, College of Administrative Science, The University of Mashreq, 10021 Baghdad, Iraq‡MEU Research Unit, Middle East University, Amman, Jordan
Sarah Qahtan: �Department of Computer Center College of Health and Medical Technology-Baghdad Middle, Technical University, Baghdad, Iraq
A. A. Zaidan: �SP Jain School of Global Management, Lidcombe Sydney, NSW 2141, Australia
Mohd Azri Mohd Izhar: Department of Computer Science, School of Computing Faculty of Engineering, University Technology Malaysia, Johor, Malaysia
Iraq T. Abbas: ��Department of Mathematics, Baghdad College of Science Department of Mathematics, Iraq

International Journal of Information Technology & Decision Making (IJITDM), 2025, vol. 24, issue 01, 89-168

Abstract: Modeling data acquisition systems (DASs) can support the vehicle industry in the development and design of sophisticated driver assistance systems. Modeling DASs on the basis of multiple criteria is considered as a multicriteria decision-making (MCDM) problem. Although literature reviews have provided models for DASs, the issue of imprecise, unclear, and ambiguous information remains unresolved. Compared with existing MCDM methods, the robustness of the fuzzy decision by opinion score method II (FDOSM II) and fuzzy weighted with zero inconsistency II (FWZIC II) is demonstrated for modeling the DASs. However, these methods are implemented in an intuitionistic fuzzy set environment that restricts the ability of experts to provide membership and nonmembership degrees freely, simulate real-world ambiguity efficiently, utilize a narrow fuzzy number space, and deal with interval data. Thus, this study used a more efficient fuzzy environment interval-valued linear Diophantine fuzzy set (IVLDF) with FWZIC II for criterion weighting and IVLDF with FDOSM for DAS modeling to address the issues and support industrial community characteristics in the design and implementation of advanced driver assistance systems in vehicles. The proposed methodology comprises two consecutive phases. The first phase involves adapting a decision matrix that intersects DAS alternatives and criteria. The second phase (development phase) proposes a decision modeling approach based on formulation of IVLD-FWZIC II and IVLD-FDOSM II to model DASs. A total of 14 DASs were modeled on the basis of 15 DAS criteria, including seven subcriteria for “comprehensive complexity assessment†and eight subcriteria for “design and implementation,†which had a remarkable effect on the DAS design when implemented by industrial communities. Systematic ranking, sensitivity analysis, and modeling checklists were conducted to demonstrate that the modeling results were subject to systematic ranking, as indicated by the high correlations across all described scenarios of changing criterion weight values, supporting the most important research points, and proposing a value-adding process in modeling the most desirable DAS.

Keywords: Data acquisition system; FDOSM; FWZIC; intelligent transportation system; interval-valued linear Diophantine fuzzy set; multicriteria decision making (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219622023500487

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