Multi-Criteria Decision-Making for Electric Bicycle Selection

Transmission rod is an indispensable part in diesel and gasoline engines. Its job is to convert rotation into translational motion or vice versa. The transmission rod material selection plays a very important role, affecting its working function and durability. This study was conducted to compare two Multi Criteria Decision Making (MCDM) methods in transmission rod material selection. They are PIV (Proximity Indexed Value) method, and FUCA (Faire Un Choi Adéquat) method. Seven types of steel commonly used in transmission rods were reviewed for ranking, inclusive of: 20 steel, 40 steel, 45 steel, 18Cr2Ni4WA steel, 30 CrMoA steel, 45Mn2 steel and 40CrNi steel. Nine parameters were used as criteria to evaluate each steel including minimum yield strength, ultimate tensile strength, minimum elongation ratio, contraction ratio, modulus of elasticity, mean coefficient of thermal expansion, thermal conductivity, specific thermal capacity, and density. The weights of the criteria were calculated using three methods inclusive of MEAN weight method, Entropy weight method and MEREC weight method (Method based on the Removal Effects of Criteria). Each MCDM method was combined with the three weight methods mentioned above to rank the alternatives. The obtained results show that when using both PIV and FUCA methods to rank the alternatives, the best and worst alternatives are found regardless of the weight of the criteria. The best alternative determined using the PIV method is also the best alternative determined using the FUCA method. It means that the two PIV and FUCA methods have been shown to be equally effective. Among the seven transmission rod materials reviewed, 20 steel was identified as the best, and 40CrNi steel was identified as the worst

Section: Introductionmentioning

confidence: 99%

Comparison of two methods in multi-criteria decision-making: application in transmission rod material selection

Thinh,

Mai

2023

“…Ten criteria were used, including price (C1), distance traveled after one battery charge (C2), charging time (C3), maximum speed (C4), weight of the bicycle (C5), load capacity of the bicycle (C6), seat height (C7), overall length of the bicycle (C8), overall width of the bicycle (C9), and overall height of the bicycle (C10). Two criteria, C1 and C3, are Type C criteria, while the remaining eight criteria are Type B [17]. The MARA method is again combined with ten data normalization methods to rank the types of electric bicycles in this case, and the results are shown in Table 8.…”

Section: Casementioning

confidence: 99%

Research on expanding the scope of application of the MARA method

Thinh,

Dua

2024

Data normalization is a crucial step in multi-criteria decision-making (MCDM) processes. The choice of data normalization method significantly influences the ranking of alternatives. The available data normalization methods in the MARA (Magnitude of the Area for the Ranking of Alternatives) method may not be applicable in certain cases. This study aims to broaden the application scope of the MARA method. Therefore, an investigation into the compatibility of data normalization methods when combined with the MARA method has been conducted. Ten data normalization methods were utilized, including Linear normalization, Weitendorf normalization, Sum linear normalization, Vector normalization, Logarithmic normalization, Max linear normalization, Min linear normalization, Jüttler-Körth normalization, Peldschus normalization, and Stop normalization. The compatibility between the MARA method and these ten data normalization methods was tested in five different scenarios. In the initial four scenarios, variations in the number of alternatives, criteria, and criterion types were introduced. Eight out of the ten data normalization methods were proven to be suitable for integration with the MARA method. In the fifth scenario, a hypothetical situation was presented where the data normalization methods available in the MARA method could not be used. Alternative data normalization methods were employed, and their combination with the MARA method was compared to using other multi-criteria decision-making methods. The results affirmed the accuracy of these combinations. This exploration has expanded the application scope of the MARA method compared to its original version

“…The PSI method has found application in solving multi-objective optimization problems across various fields. Examples include optimizing the selection of personnel for the position of a foreman [11], optimizing the selection of personnel in a textile company in Denizli [12], optimizing the selection of countries with the best tourism potential [13], and optimizing the selection of electric bicycles [14], among others. Similarly, the CURLI method has been applied to solve multi-objective optimization problems in diverse fields.…”

Section: Mathematicsmentioning

confidence: 99%

Comparision of both methods psi and curli: applied in solving multi-objective optimization problem of turning process

Trung,

Phuong Giang,

Son

2024

Self Cite

Solving a multi-objective optimization problem involves finding the best solution to simultaneously satisfy multiple predefined objectives. Currently, various mathematical methods are available for solving optimization problems in general, and multi-objective optimization in particular. The comparison of mathematical methods when addressing the same problem has been explored in numerous studies. In this study, let’s conduct a comparison of two multi-objective optimization methods: the PSI method and the CURLI method. These two methods were applied collectively to tackle a multi-objective optimization problem related to a turning process. Experimental data were borrowed from a previous study, and a total of sixteen experiments were conducted. Roughness average (Ra), Roundness Error (RE), Tool Wear (VB), and Material Removal Rate (MRR) were the four output parameters measured in each experiment. The objective of solving the multi-objective optimization problem was to identify an experiment among the sixteen existing experiments that simultaneously minimized the three parameters of Ra, RE, and VB while maximizing MRR. The optimal results determined using the PSI and CURLI methods were also compared with the optimal results obtained through other methods (COCOSO, MABAC, MAIRCA, EAMR and TOPSIS) in published documents. The comparison results indicate that the optimal experiment found using the CURLI method consistently matches that of other methods. In contrast, the optimal results obtained through the PSI method differ significantly from those obtained through other methods. The Spearman correlation ranking coefficient between CURLI and the five methods COCOSO, MABAC, MAIRCA, EAMR, and TOPSIS is very high, ranging from 0.9 to 1. In contrast, this coefficient is very small when comparing PSI with the aforementioned five methods, falling within the range of –0.6088 to –0.3706 in this case. Ultimately, this study concludes that the CURLI method is suiTable for solving the multi-objective optimization problem in the turning process, whereas the PSI method is deemed unsuitable