An Intelligent Fuzzy MCDM Model Based on D and Z Numbers for Paver Selection: IMF D-SWARA—Fuzzy ARAS-Z Model

Abstract

One of the most important challenges when building road infrastructure is the selection of appropriate mechanization, on which the efficiency of construction and the life of exploitation depends largely. As construction machinery, pavers occupy a significant place in civil engineering projects, so their selection, depending on a road category, is a very important activity. The objective of this paper is to develop an intelligent Fuzzy MCDM (Multi-Criteria Decision-Making) model, which consists of the integration of D and Z numbers for the selection of construction machinery. The IMF D-SWARA (Improved Fuzzy D Step-Wise Weight Assessment Ratio Analysis) method was used to determine weighting coefficients. A novel Fuzzy ARAS-Z (Additive Ratio Assessment) method has been developed to determine an adequate paver for a lower category of roads (asphalt width up to 5 m), which represents an important contribution and novelty of the paper. A total of 10 alternatives were evaluated based on 16 criteria which were classified into 4 main groups. The results have shown that the alternative A8—SUPER 1300-3 represents a paver with the best characteristics for the considered set of parameters. After that, verification tests were calculated, and they include a comparative analysis with four other MCDM methods based on Z numbers, a change in the normalization procedure, and the impact of changing the size of an initial fuzzy matrix. The tests showed the stability of the developed model with negligible deviations.

Integrated Spherical Decision-Making Model for Managing Climate Change Risks in Africa

“…Considering the limitations of prior research, effectively addressing climate change risks and successfully implementing adaptation activities demands an approach that provides a holistic managerial perspective. This method should explicitly take into account multiple criteria to enhance decision outcomes [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59]. Multi-criteria decision-making (MCDM) techniques, adept at structuring complex problems and accommodating various criteria, are well-suited for this purpose [55,[60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79].…”

Z-Numbers-Based MCDM Approach for Personnel Selection at Institutions of Higher Education for Transportation

“…A Z-number indicates the degree of ambiguity in the data and the reliability of the data source. A set of fuzzy numbers that is ordered, Z = ( ∼ A, ∼ B), is a common representation of the Z-numbers, where the fuzzy value for identifying a variable's state is represented by ∼ A, and the second element is the fuzzy number ∼ B describing the reliability of ∼ A [54]. Based on the previous explanation, the mathematical formulation of the Z-number is presented as follows [55].…”

“…To convert the expert's evaluations to fuzzy numbers, Table 4 is designed. It consists of linguistic terms and their corresponding fuzzy numbers [54,55]. These fuzzy numbers are used to represent linguistic terms in mathematical operations.…”