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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

**:**

## 1. Introduction

- Alternatives: solutions or options that should be evaluated based on attributes, and ranked or selected according to the most appropriate.
- Attributes: properties, qualities, or features of the alternatives. Each attribute can have several sub-attributes.
- The relative importance of attributes (an attribute’s weight of importance): The degree of preference of an attribute over another attribute or sub-attribute.
- Evaluation function: final criterion for evaluating and ranking the alternatives.

- How to ensure the reliability of the decision?
- How to ensure the robustness of the decision?

## 2. The Proposed Methodology

## 3. Implementation

#### 3.1. Problem Inputs

- The selected automotive platform should support the automotive family in segments B, C, and SS (Small SUV).
- The automobiles developed based on the platforms must be less than 25 years old.
- It is desirable to develop low-cost automobiles using the platforms.
- It is desirable to develop automobiles in various price classes based on the platforms.
- It is desirable to develop different segments of automobiles based on the platforms.
- It is desirable to develop different models of automobiles based on the platforms.
- More popular and trustworthy platforms are desirable.

#### 3.2. Elicitation of Constraints and Decision Attributes

- The automotive family developed based on the platform must include at least one of the B or C or SS segments.
- All the automobiles manufactured based on each of the platforms must be under 25 years old.

#### 3.3. Valuation of the Attributes and Alternatives Definition

#### 3.4. Generation of the Relative Importance of Attributes

- A scale of 1 to 9 was used to quantify the relative importance of each attribute.
- Uniform and symmetric probability distributions were used to determine the relative importance of the attributes.
- There was no similarity between any of the sets of attributes’ relative importance.

#### 3.5. Evaluating, Ranking, and Storing the Alternatives

- ${\mathrm{E}}_{\mathrm{j}}$: The value of the evolution function for each alternative.
- ${\mathrm{W}}_{\mathrm{i}}$: Attributes weight of importance.

#### 3.6. Statistical Analysis and Sensitivity Assessment of Outputs

#### 3.7. Identification of the Robust-Reliable Decision

- 1.
- Achieving the best relative position (mean position number) in all rankings with the different relative importance of attributes (desirability criterion)

- 2.
- The lowest standard deviation in the occupied positions in the ranking (robustness criterion)

## 4. Discussion

## 5. Conclusions

- As the most widely used, reliable, and proven MADM method, the SAW decision-making method was used to reduce the level of computational complexity for problems containing a large number of decision options.
- Building a database for the product and using KDD techniques instead of relying solely on the vague and uncertain statements of the experts, led to a precise determination of the attribute values and decision space. As a result, the level of uncertainty and lack of knowledge in decision-making processes were greatly reduced.
- The simulation of the preferences of all stakeholders provided a comprehensive view of possible changes in the priority of alternatives over changes in the relative importance of attributes.
- Statistical analysis and sensitivity assessment were used to determine which alternatives were the most robust and reliable.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 4.**Each alternative’s position in ranking after solving the decision-making problem 6223 times.

**Figure 6.**Box diagram of the positions occupied by the alternatives in 6223 solutions of the decision problem.

Alternative Number | Platform Name | Segment Adaptation | Price (USD) | Platform Flexibility | Platform Popularity | |||
---|---|---|---|---|---|---|---|---|

Minimum Price | Price Range | Segment Score | Model Score | Annual Production | Number of Manufacturers | |||

P1 | BMW CLAR | 82 | 39,587 | 147,152 | 6 | 13 | 686,606 | 2 |

P2 | BMW Life-Drive | 42 | 41,508 | 121,792 | 2 | 2 | 31,258 | 1 |

P3 | BMW UKL | 90 | 20,026 | 79,872 | 4 | 13 | 680,465 | 3 |

P4 | Fiat Compact | 108 | 16,110 | 38,511 | 5 | 7 | 329,759 | 5 |

P5 | Fiat Mini | 40 | 8839 | 17,012 | 2 | 5 | 548,969 | 3 |

P6 | Fiat-GM Small | 120 | 11,256 | 37,080 | 6 | 18 | 914,152 | 5 |

P7 | Ford Global B | 65 | 12,260 | 21,893 | 3 | 8 | 760,270 | 1 |

P8 | Ford Global C | 65 | 18,455 | 33,582 | 3 | 8 | 1,475,409 | 2 |

P9 | Ford C2 | 50 | 24,885 | 19,945 | 2 | 4 | 321,196 | 2 |

P10 | GM Delta | 83 | 16,137 | 59,150 | 4 | 23 | 1,457,551 | 6 |

P11 | GM Epsilon | 65 | 21,548 | 51,218 | 5 | 20 | 748,907 | 7 |

P12 | GM Gamma | 105 | 9758 | 20,146 | 5 | 11 | 941,542 | 3 |

P13 | GM Lambda | 33 | 29,371 | 18,919 | 2 | 4 | 240,138 | 4 |

P14 | GM Theta | 33 | 17,122 | 38,056 | 2 | 10 | 341,423 | 8 |

P15 | Hyundai-Kia J | 65 | 13,642 | 74,790 | 3 | 27 | 2,196,539 | 2 |

P16 | Hyundai-Kia Small | 90 | 12,771 | 23,139 | 4 | 18 | 1,127,852 | 2 |

P17 | Hyundai-Kia Y | 75 | 19,146 | 39,405 | 5 | 17 | 1,385,195 | 2 |

P18 | Mercedes-Benz MFA | 50 | 30,228 | 44,207 | 2 | 3 | 472,036 | 1 |

P19 | Mercedes-Benz W176 | 50 | 25,250 | 42,883 | 2 | 4 | 226,943 | 2 |

P20 | Mitsubishi GS | 91 | 14,925 | 43,940 | 5 | 21 | 798,688 | 8 |

P21 | PSA CMP EMP1 | 50 | 20,888 | 21,683 | 2 | 4 | 295,487 | 3 |

P22 | PSA EMP2 | 83 | 23,803 | 47,552 | 4 | 17 | 805,299 | 5 |

P23 | PSA PF1 | 90 | 10,040 | 22,624 | 4 | 16 | 994,922 | 3 |

P24 | PSA PF2 | 100 | 14,689 | 32,478 | 5 | 21 | 760,777 | 4 |

P25 | Renault-Nissan B | 122 | 8919 | 35,325 | 6 | 48 | 2,372,822 | 6 |

P26 | Renault-Nissan C | 65 | 11,614 | 29,373 | 3 | 16 | 976,126 | 4 |

P27 | Renault-Nissan CMF | 116 | 16,760 | 31,599 | 6 | 15 | 1,294,809 | 2 |

P28 | Toyota B | 83 | 10,774 | 18,851 | 4 | 16 | 858,857 | 2 |

P29 | Toyota MC | 116 | 16,864 | 46,570 | 6 | 23 | 1,959,953 | 3 |

P30 | Toyota TNGA | 125 | 19,600 | 38,528 | 7 | 15 | 2,029,559 | 3 |

P31 | VW A | 107 | 13,525 | 38,988 | 5 | 29 | 1,643,290 | 4 |

P32 | VW A0 | 90 | 9310 | 29,389 | 4 | 26 | 1,227,147 | 5 |

P33 | VW MLB | 65 | 28,843 | 267,499 | 5 | 21 | 936,248 | 5 |

P34 | VW MQB | 133 | 18,745 | 59,112 | 7 | 41 | 3,253,274 | 5 |

Normalization Methods | Equation |
---|---|

Max (Linear normalization) | ${\left({\mathrm{V}}_{\mathrm{i}\mathrm{j}}\right)}_{\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}}=\frac{{\mathrm{V}}_{\mathrm{i}\mathrm{j}}}{Max\left({V}_{i}\right)}$ (for Benefit) ${\left({\mathrm{V}}_{\mathrm{i}\mathrm{j}}\right)}_{\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}}=1-\frac{{\mathrm{V}}_{\mathrm{i}\mathrm{j}}}{Max\left({V}_{i}\right)}$ (for Cost) |

Max-Min (Linear normalization) | ${\left({\mathrm{V}}_{\mathrm{i}\mathrm{j}}\right)}_{\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}}=\frac{{\mathrm{V}}_{\mathrm{i}\mathrm{j}}-Min\left({V}_{i}\right)}{Max\left({V}_{i}\right)-Min\left({V}_{i}\right)}$ (for Benefit) ${\left({\mathrm{V}}_{\mathrm{i}\mathrm{j}}\right)}_{\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}}=\frac{Max\left({V}_{i}\right)-{\mathrm{V}}_{\mathrm{i}\mathrm{j}}}{Max\left({V}_{i}\right)-Min\left({V}_{i}\right)}$ (for Cost) |

Sum (Linear normalization) | ${\left({\mathrm{V}}_{\mathrm{i}\mathrm{j}}\right)}_{\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}}=\frac{{\mathrm{V}}_{\mathrm{i}\mathrm{j}}}{{\sum}_{j=1}^{n}{\mathrm{V}}_{\mathrm{i}\mathrm{j}}}$ (for Benefit) ${\left({\mathrm{V}}_{\mathrm{i}\mathrm{j}}\right)}_{\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}}=\frac{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{${\mathrm{V}}_{\mathrm{i}\mathrm{j}}$}\right.}{{\sum}_{j=1}^{n}\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{${\mathrm{V}}_{\mathrm{i}\mathrm{j}}$}\right.}$ (for Cost) |

Vector normalization | ${\left({\mathrm{V}}_{\mathrm{i}\mathrm{j}}\right)}_{\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}}=\frac{{\mathrm{V}}_{\mathrm{i}\mathrm{j}}}{\sqrt{\sum _{\mathrm{j}=1}^{\mathrm{n}}{{\mathrm{V}}_{\mathrm{i}\mathrm{j}}}^{2}}}$ (for Benefit) ${\left({\mathrm{V}}_{\mathrm{i}\mathrm{j}}\right)}_{\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}}=1-\frac{{\mathrm{V}}_{\mathrm{i}\mathrm{j}}}{\sqrt{\sum _{\mathrm{j}=1}^{\mathrm{n}}{{\mathrm{V}}_{\mathrm{i}\mathrm{j}}}^{2}}}$ (for Cost) |

Logarithmic normalization | ${\left({\mathrm{V}}_{\mathrm{i}\mathrm{j}}\right)}_{\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}}=\frac{\mathrm{ln}{\mathrm{V}}_{\mathrm{i}\mathrm{j}}}{\mathrm{ln}\left({\prod}_{j=1}^{n}{\mathrm{V}}_{\mathrm{i}\mathrm{j}}\right)}$ (for Benefit) ${\left({\mathrm{V}}_{\mathrm{i}\mathrm{j}}\right)}_{\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}}=\frac{1-\frac{\mathrm{ln}{\mathrm{V}}_{\mathrm{i}\mathrm{j}}}{\mathrm{ln}\left({\prod}_{j=1}^{n}{\mathrm{V}}_{\mathrm{i}\mathrm{j}}\right)}}{n-1}$ (for Cost) |

Alternative Number | Platform Name | Segment Adaptation | Price | Platform Flexibility | Platform Popularity | |||
---|---|---|---|---|---|---|---|---|

Minimum Price | Price Range | Segment Score | Model Score | Annual Production | Number of Manufacturers | |||

P1 | BMW CLAR | 0.1646 | 0.6628 | 0.3695 | 0.2339 | 0.1172 | 0.0946 | 0.0841 |

P2 | BMW Life-Drive | 0.0843 | 0.6465 | 0.3058 | 0.078 | 0.018 | 0.0043 | 0.0421 |

P3 | BMW UKL | 0.1806 | 0.8294 | 0.2006 | 0.1559 | 0.1172 | 0.0938 | 0.1262 |

P4 | Fiat Compact | 0.2168 | 0.8628 | 0.0967 | 0.1949 | 0.0631 | 0.0454 | 0.2104 |

P5 | Fiat Mini | 0.0803 | 0.9247 | 0.0427 | 0.078 | 0.0451 | 0.0757 | 0.1262 |

P6 | Fiat-GM Small | 0.2409 | 0.9041 | 0.0931 | 0.2339 | 0.1623 | 0.126 | 0.2104 |

P7 | Ford Global B | 0.1305 | 0.8956 | 0.055 | 0.117 | 0.0722 | 0.1048 | 0.0421 |

P8 | Ford Global C | 0.1305 | 0.8428 | 0.0843 | 0.117 | 0.0722 | 0.2034 | 0.0841 |

P9 | Ford C2 | 0.1004 | 0.788 | 0.0501 | 0.078 | 0.0361 | 0.0443 | 0.0841 |

P10 | GM Delta | 0.1666 | 0.8626 | 0.1485 | 0.1559 | 0.2074 | 0.2009 | 0.2524 |

P11 | GM Epsilon | 0.1305 | 0.8165 | 0.1286 | 0.1949 | 0.1804 | 0.1032 | 0.2945 |

P12 | GM Gamma | 0.2107 | 0.9169 | 0.0506 | 0.1949 | 0.0992 | 0.1298 | 0.1262 |

P13 | GM Lambda | 0.0662 | 0.7498 | 0.0475 | 0.078 | 0.0361 | 0.0331 | 0.1683 |

P14 | GM Theta | 0.0662 | 0.8542 | 0.0956 | 0.078 | 0.0902 | 0.0471 | 0.3366 |

P15 | Hyundai-Kia J | 0.1305 | 0.8838 | 0.1878 | 0.117 | 0.2435 | 0.3027 | 0.0841 |

P16 | Hyundai-Kia Small | 0.1806 | 0.8912 | 0.0581 | 0.1559 | 0.1623 | 0.1554 | 0.0841 |

P17 | Hyundai-Kia Y | 0.1505 | 0.8369 | 0.0989 | 0.1949 | 0.1533 | 0.1909 | 0.0841 |

P18 | Mercedes-Benz MFA | 0.1004 | 0.7425 | 0.111 | 0.078 | 0.0271 | 0.0651 | 0.0421 |

P19 | Mercedes-Benz W176 | 0.1004 | 0.7849 | 0.1077 | 0.078 | 0.0361 | 0.0313 | 0.0841 |

P20 | Mitsubishi GS | 0.1826 | 0.8729 | 0.1103 | 0.1949 | 0.1894 | 0.1101 | 0.3366 |

P21 | PSA CMP EMP1 | 0.1004 | 0.8221 | 0.0544 | 0.078 | 0.0361 | 0.0407 | 0.1262 |

P22 | PSA EMP2 | 0.1666 | 0.7973 | 0.1194 | 0.1559 | 0.1533 | 0.111 | 0.2104 |

P23 | PSA PF1 | 0.1806 | 0.9145 | 0.0568 | 0.1559 | 0.1443 | 0.1371 | 0.1262 |

P24 | PSA PF2 | 0.2007 | 0.8749 | 0.0816 | 0.1949 | 0.1894 | 0.1049 | 0.1683 |

P25 | Renault-Nissan B | 0.2449 | 0.924 | 0.0887 | 0.2339 | 0.4329 | 0.327 | 0.2524 |

P26 | Renault-Nissan C | 0.1305 | 0.9011 | 0.0738 | 0.117 | 0.1443 | 0.1345 | 0.1683 |

P27 | Renault-Nissan CMF | 0.2328 | 0.8572 | 0.0793 | 0.2339 | 0.1353 | 0.1785 | 0.0841 |

P28 | Toyota B | 0.1666 | 0.9082 | 0.0473 | 0.1559 | 0.1443 | 0.1184 | 0.0841 |

P29 | Toyota MC | 0.2328 | 0.8564 | 0.1169 | 0.2339 | 0.2074 | 0.2701 | 0.1262 |

P30 | Toyota TNGA | 0.2509 | 0.8331 | 0.0967 | 0.2729 | 0.1353 | 0.2797 | 0.1262 |

P31 | VW A | 0.2148 | 0.8848 | 0.0979 | 0.1949 | 0.2615 | 0.2265 | 0.1683 |

P32 | VW A0 | 0.1806 | 0.9207 | 0.0738 | 0.1559 | 0.2345 | 0.1691 | 0.2104 |

P33 | VW MLB | 0.1305 | 0.7543 | 0.6717 | 0.1949 | 0.1894 | 0.129 | 0.2104 |

P34 | VW MQB | 0.2669 | 0.8403 | 0.1484 | 0.2729 | 0.3698 | 0.4484 | 0.2104 |

Alt. No. | P6 | P10 | P31 | P20 | P25 | P29 | P30 | P33 | P34 | |
---|---|---|---|---|---|---|---|---|---|---|

Position in Ranking | ||||||||||

Rank 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 6207 | |

Rank 2 | 0 | 0 | 0 | 6197 | 0 | 0 | 0 | 10 | 16 | |

Rank 3 | 0 | 25 | 80 | 26 | 0 | 5499 | 0 | 593 | 0 | |

Rank 4 | 160 | 104 | 210 | 0 | 5207 | 442 | 0 | 100 | 0 | |

Rank 5 | 2162 | 104 | 528 | 0 | 726 | 162 | 2190 | 351 | 0 | |

Rank 6 | 1498 | 171 | 955 | 0 | 184 | 96 | 3039 | 199 | 0 | |

Rank 7 | 958 | 497 | 2142 | 0 | 106 | 17 | 784 | 577 | 0 | |

Rank 8 | 685 | 1701 | 1524 | 0 | 0 | 5 | 210 | 875 | 0 | |

Rank 9 | 584 | 1606 | 603 | 0 | 0 | 2 | 0 | 932 | 0 | |

Rank 10 | 52 | 616 | 75 | 0 | 0 | 0 | 0 | 492 | 0 | |

Rank 11 | 46 | 175 | 88 | 0 | 0 | 0 | 0 | 635 | 0 | |

Rank 12 | 78 | 319 | 18 | 0 | 0 | 0 | 0 | 455 | 0 | |

Rank 13 | 0 | 387 | 0 | 0 | 0 | 0 | 0 | 241 | 0 | |

Rank 14 | 0 | 384 | 0 | 0 | 0 | 0 | 0 | 377 | 0 | |

Rank 15 | 0 | 134 | 0 | 0 | 0 | 0 | 0 | 185 | 0 | |

Rank 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 43 | 0 | |

Rank 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 0 | |

Rank 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 45 | 0 | |

Rank 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 0 | |

Rank 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | |

Rank 21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | |

Rank 22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Rank 23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Rank 24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Rank 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Rank 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Rank 27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Rank 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Rank 29 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Rank 30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Rank 31 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Rank 32 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Rank 33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Rank 34 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

Alt. No. | The Most Frequented Occupied Position in the Ranking (Mode) | The Percentage of Maximum Repetition in the Ranking | The Mean Value of Occupied Positions in the Ranking (Mean Rank Number) | The Number of Occupied Positions in the Ranking | The Standard Deviation of Occupied Positions in the Ranking |
---|---|---|---|---|---|

P6 | Rank 5 | 34.70% | 6.4023 | 9 positions | 1.5801 |

P10 | Rank 8 | 27.30% | 9.3054 | 13 positions | 2.3584 |

P20 | Rank 7 | 34.40% | 7.07 | 10 positions | 1.4271 |

P25 | Rank 2 | 99.50% | 2.0042 | 2 positions | 0.0645 |

P29 | Rank 4 | 83.70% | 4.2269 | 4 positions | 0.5803 |

P30 | Rank 3 | 88.40% | 3.1862 | 7 positions | 0.5955 |

P31 | Rank 6 | 48.80% | 5.8415 | 4 positions | 0.7667 |

P33 | Rank 9 | 15% | 9.0633 | 21 positions | 3.5196 |

P34 | Rank 1 | 99.70% | 1.0026 | 2 positions | 0.0506 |

Prioritization Based on the Desirability | Prioritization Based on the Robustness | ||||
---|---|---|---|---|---|

Prioritized Alternatives | Alt. No. | Mean Position in the Ranking | Prioritized Alternatives | Alt. No. | The Standard Deviation of Occupied Positions |

The first (The most desirable) | P34 | 1.0026 | The first (The most robust) | P34 | 0.05064086 |

The second | P25 | 2.0042 | The second | P25 | 0.06450266 |

The third | P30 | 3.1862 | The third | P29 | 0.58030464 |

The fourth | P29 | 4.2269 | The fourth | P30 | 0.59554597 |

The fifth | P31 | 5.8416 | The fifth | P31 | 0.76667009 |

Prioritized Alternatives | Alt. No. | Platform Name |
---|---|---|

The first (The most robust-reliable decision) | P34 | VW MQB |

The second | P25 | Renault-Nissan B |

The third | P30 | Toyota TNGA |

The fourth | P29 | Toyota MC |

The fifth | P31 | VW A |

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## Share and Cite

**MDPI and ACS Style**

Saghari, A.; Budinská, I.; Hosseinimehr, M.; Rahmani, S.
A Robust-Reliable Decision-Making Methodology Based on a Combination of Stakeholders’ Preferences Simulation and KDD Techniques for Selecting Automotive Platform Benchmark. *Symmetry* **2023**, *15*, 750.
https://doi.org/10.3390/sym15030750

**AMA Style**

Saghari A, Budinská I, Hosseinimehr M, Rahmani S.
A Robust-Reliable Decision-Making Methodology Based on a Combination of Stakeholders’ Preferences Simulation and KDD Techniques for Selecting Automotive Platform Benchmark. *Symmetry*. 2023; 15(3):750.
https://doi.org/10.3390/sym15030750

**Chicago/Turabian Style**

Saghari, Asad, Ivana Budinská, Masoud Hosseinimehr, and Shima Rahmani.
2023. "A Robust-Reliable Decision-Making Methodology Based on a Combination of Stakeholders’ Preferences Simulation and KDD Techniques for Selecting Automotive Platform Benchmark" *Symmetry* 15, no. 3: 750.
https://doi.org/10.3390/sym15030750