scholarly journals Optimization of Process Parameters for Friction Materials Using Multi-Criteria Decision Making: A Comparative Analysis

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1570
Author(s):  
Dinesh Shinde ◽  
Hasan Öktem ◽  
Kanak Kalita ◽  
Shankar Chakraborty ◽  
Xiao-Zhi Gao
Keyword(s):  
Heat Treatment ◽  
Decision Making ◽  
Comparative Analysis ◽  
Case Studies ◽  
Process Parameters ◽  
Surface Hardness ◽  
Molding Pressure ◽  
Multi Criteria Decision Making ◽  
Friction Materials ◽  
Molding Temperature

Friction materials, mainly made of reinforced composites, should possess excellent physical and tribological properties, such as a higher coefficient of friction, minimum wear, adequate surface hardness, and higher porosity for various automotive brake applications. Attainment of those properties greatly depends on the settings of different input parameters, such as molding pressure, temperature and time, temperature and time of heat treatment/sintering, etc., during the processing of friction materials. In this article, four multi-criteria decision-making (MCDM) tools, i.e., technique for order of preference by similarity to ideal solution (TOPSIS), evaluation based on distance from average solution (EDAS), VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and multi-objective optimization on the basis of ratio analysis (MOORA) are applied to identify the optimal parametric combinations for two different friction materials. A comparative analysis of the derived results suggests the same optimal parametric mixes for all the MCDM techniques for both case studies. Thus, for the considered case studies, the optimal parametric combinations as molding time = 8 min, molding temperature = 175 °C, molding pressure = 27 MPa, sintering time = 10 h and sintering temperature = 225 °C, and molding pressure = 27.90 MPa, molding temperature = 170 °C, curing time = 8 min, and heat treatment time = 1 h, respectively, would help in attaining the most desired properties of friction materials. Improvements in the predicted response values prove the efficacy of the adopted MCDM techniques in determining the optimal combinations of various process parameters for friction materials.

Complex q-rung orthopair fuzzy Schweizer–Sklar Muirhead mean aggregation operators and their application in multi-criteria decision-making

2021 ◽  
pp. 1-23
Author(s):  
Peide Liu ◽  
Tahir Mahmood ◽  
Zeeshan Ali
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Imaginary Part ◽  
Fuzzy Set ◽  
Score Function ◽  
Aggregation Operators ◽  
Unit Interval ◽  
Multi Criteria Decision Making ◽  
Accuracy Function ◽  
Special Cases

Complex q-rung orthopair fuzzy set (CQROFS) is a proficient technique to describe awkward and complicated information by the truth and falsity grades with a condition that the sum of the q-powers of the real part and imaginary part is in unit interval. Further, Schweizer–Sklar (SS) operations are more flexible to aggregate the information, and the Muirhead mean (MM) operator can examine the interrelationships among the attributes, and it is more proficient and more generalized than many aggregation operators to cope with awkward and inconsistence information in realistic decision issues. The objectives of this manuscript are to explore the SS operators based on CQROFS and to study their score function, accuracy function, and their relationships. Further, based on these operators, some MM operators based on PFS, called complex q-rung orthopair fuzzy MM (CQROFMM) operator, complex q-rung orthopair fuzzy weighted MM (CQROFWMM) operator, and their special cases are presented. Additionally, the multi-criteria decision making (MCDM) approach is developed by using the explored operators based on CQROFS. Finally, the advantages and comparative analysis are also discussed.

Multi-criteria decision-making toward supplier selection: exploration of PROMETHEE II method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nishant Agrawal
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Supply Chain ◽  
Case Studies ◽  
Supplier Selection ◽  
Multi Criteria Decision Making ◽  
Content Type ◽  
Promethee Ii ◽  
Wide Range ◽  
Managerial Implications

PurposeSupplier Selection (SS) is one of the vital decisions frequently executed by numerous industries. In recent times, the number of suppliers has increased enormously depending on a wide range of criteria. A selection of suppliers is a sensitive process that may impact various supply chain activities. The purpose of this research is to explore an underutilized technique called PROMETHEE II method for SS.Design/methodology/approachVarious tools and techniques are available under multi-criteria decision-making tools, which sometimes creates confusion in researchers' minds regarding reliability. PROMETHEE II was the most prominent method for ranking all available alternatives that ultimately avoid decision-making errors. To execute this equal and unequal weights approach has been used with three case studies.FindingsIn this research, three case studies have been used and soved with the help of the PROMETHEE II approach. The study also provides fundamental insights into the supplier's ranking on different criteria using sensitivity analysis. Further, criteria were divided as per benefits and non-beneficial to get a robust result. The pros and cons of PROMETHEE II approaches are also highlighted compared to other MCDM tools in this study.Originality/valueMost of the SS research uses either AHP or TOPSIS as per existing literature. There are very few attempts highlighted in the literature that use PROMETHEE II for the SS problem with sensitivity analysis. The proposed method is probable to motivate decision-makers to consider using a more sophisticated method like PROMETHEE II in supplier evaluation processes. This study opens a new direction for the ranking of suppliers in the field of the supply chain. The study also bears significant practical as well as managerial implications.

Sign in / Sign up

Export Citation Format

Share Document