# A Novel General Data Envelopment Analysis Based Approach for MCDM Issues of Hydrogen Energy under a Fuzzy Environment

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Preliminary Knowledge

#### 2.1. The Data Envelopment Analysis (DEA) Method

#### 2.2. The Fuzzy AHP Method

**Definition 1**

**[14].**

**Definition 2**

**[26,43].**

**Definition 3**

**[26,43].**

**Definition 4**

**[26,43].**

#### 2.3. The Hesitant Fuzzy Linguistic Term Set (HFLTS)

**Definition 5**

**[56].**

**Definition 6**

**[23,53].**

**Definition 7**

**[23,54].**

**Definition 8**

**[46].**

#### 2.4. The Soft Set

**Definition 9**

**[24,61].**

**Definition 10**

**[24,62].**

**Definition 11**

**[40,62].**

## 3. Methodology

#### 3.1. The Planning of the Research Method

#### 3.2. The Research Procedure

- Step 1.
- Define the problem and construct the problem hierarchy.

- Step 2.
- Identifying evaluation criteria of MCDM problems.

- Step 3.
- Fill in incomplete information.

- Step 4.
- Defuzzify the HFI.

- Step 5.
- Determine the weight of the assessment criteria.

- Step 6.
- Evaluate the relative efficiency.

- Step 7.
- Ranking of the relative efficiency of DMU.

## 4. Numerical Analysis

#### 4.1. Overview

#### 4.2. Application of DEA Approach

#### 4.3. Application of Typical Analytic Hierarchy Process/Data Envelopment Analysis (AHP/DEA) Method

#### 4.4. Application of the Fuzzy Analytic Hierarchy Process/Data Envelopment Analysis Method

#### 4.5. Application of the Proposed Method

- Step 1 and Step 2.
- Organize a committee to construct and determine the evaluation criteria for HET

- Step 3.
- Fill in incomplete information

- Step 4.
- Defuzzify the hesitant fuzzy information

- Step 5.
- Determine the weight of the evaluation criteria

- Step 6 and Step 7.
- Evaluation and ranking of the relative efficiency of DMUs

#### 4.6. Comparison and Discussion

- (1)
- Consider the relative weight of the criteria. Since typical DEA methods assume equal weights of criteria, it is difficult to distinguish the importance of each criterion efficiently. On the other hand, the AHP/DEA method, fuzzy AHP/DEA method, and the proposed novel DEA-based method used the AHP method to full consideration the relative weight of HET criteria.
- (2)
- Consistency test of information provided by experts. The typical DEA method and the fuzzy AHP/DEA method cannot handle the consistency test of information provided by experts. The AHP/DEA method and the proposed novel DEA-based method can handle consistency tests of information provided by experts.
- (3)
- Handling of incomplete information. The typical DEA method, the AHP/DEA method, and the fuzzy AHP/DEA method cannot handle incomplete information provided by experts. When processing questionnaire information, as long as there is incomplete information in the questionnaire, it is usually regarded as invalid and discarded. Since the information provided by Experts 4 and 5 includes hesitant or nonexistent information, it is deleted during the solution process and will possibly ignore important information provided by some experts, resulting in biased and unobjective calculation results. The proposed novel, DEA-based method integrates the typical DEA method, AHP method, HFLTS, and the soft set to process the MCDM problems under a fuzzy environment. The proposed method can deal with hesitant and nonexistent information and can help decision makers to consider all available information fully to obtain a more reasonable and correct solution result.
- (4)
- The critical technologies ranking of HET. According to the typical DEA method [63], the critical technologies ranking of the HET was $\mathrm{T}1=\mathrm{T}6=\mathrm{T}7=\mathrm{T}8\succ \mathrm{T}10\succ \mathrm{T}9\succ \mathrm{T}11\succ \mathrm{T}2\succ \mathrm{T}5\succ \mathrm{T}4\succ \mathrm{T}13\succ \mathrm{T}12\succ \mathrm{T}3$. Based on the typical AHP/DEA method [64], the critical technologies ranking of the HET was $\mathrm{T}1=\mathrm{T}6=\mathrm{T}7=\mathrm{T}8\succ \mathrm{T}10\succ \mathrm{T}2\succ \mathrm{T}9\succ \mathrm{T}11\succ \mathrm{T}4=\mathrm{T}5\succ \mathrm{T}13\succ \mathrm{T}3\succ \mathrm{T}12$. According to the fuzzy AHP/DEA method [43], the critical technologies ranking of the HET was $\mathrm{T}1=\mathrm{T}6=\mathrm{T}7=\mathrm{T}8\succ \mathrm{T}10\succ \mathrm{T}2\succ \mathrm{T}9\succ \mathrm{T}11\succ \mathrm{T}4=\mathrm{T}5\succ \mathrm{T}13\succ \mathrm{T}3\succ \mathrm{T}12$. Based on the proposed method, the critical technologies ranking of the HET was $\mathrm{T}1=\mathrm{T}7=\mathrm{T}8\succ \mathrm{T}6\succ \mathrm{T}10\succ \mathrm{T}11\succ \mathrm{T}2\succ \mathrm{T}4\succ \mathrm{T}5\succ \mathrm{T}9\succ \mathrm{T}13\succ \mathrm{T}12\succ \mathrm{T}3$.

## 5. Conclusions

- (1)
- Considers the weight of evaluation criteria of MCDM problems.
- (2)
- Processes ambiguous information provided by experts.
- (3)
- Processes hesitant information provided by experts.
- (4)
- Processes incomplete information provided by experts.
- (5)
- Fully considers the available information provided by experts.
- (6)
- Performs consistency tests to check whether the evaluation information is consistent.
- (7)
- The fuzzy AHP/DEA method [43] is a special case of the proposed general DEA-based approach.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

MCDM | Multi-criteria decision-making |

DEA | Data envelopment analysis |

AHP | Analytic hierarchy process |

HFLTS | Hesitant fuzzy linguistic term set |

DMUs | Decision-making units |

PROMETHEE | Preference ranking organization method for enrichment evaluation |

TOPSIS | Technique for order preference by similarity to ideal solution |

ELECTRE | Elimination and choice translating reality |

HET | Hydrogen energy technology |

CCR | Charnes-Cooper-Rhodes |

TFNs | Triangular fuzzy numbers |

COA | Center of area |

CR | Consistency ratio |

CI | Consistency index |

RI | Random index |

LCD | Liquid crystal display |

HFI | Hesitant fuzzy information |

EI | Economic impact |

BP | Business potential |

IA | Inner ability |

TD | Technology derivatives |

DC | Development cost |

R&D | Research and development |

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Triangular Fuzzy Scale | Reciprocal Scale | Definition of Preference Judgments |
---|---|---|

(1, 1, 1) | (1, 1, 1) | Equal importance |

(3/2, 2, 5/2) | (2/5, 1/2, 2/3) | Central point between the equal and weak importance |

(5/2, 3, 7/2) | (2/7, 1/3, 2/5) | Weak importance |

(7/2, 4, 9/2) | (2/9, 1/4, 2/7) | Central point between the weak and strong importance |

(9/2, 5, 11/2) | (2/11, 1/5, 2/9) | Strong importance |

(11/2, 6, 13/2) | (2/13, 1/6, 2/11) | Central point between the strong and very strong importance |

(13/2, 7, 15/2) | (2/15, 1/7, 2/13) | Very strong importance |

(15/2, 8, 17/2) | (2/17, 1/8,2/15) | Central point between the very strong and more importance |

(17/2, 9, 19/2) | (2/19, 1/9, 2/17) | Absolutely more importance |

Size of Matrix (n) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|

RI | 0.00 | 0.00 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 |

Expert | EI | BP | IA | TD | |
---|---|---|---|---|---|

EI | Expert 1 | (1, 1, 1) | (1, 1, 1) | (1, 1, 1) | (2/3, 1, 3/2) |

Expert 2 | (1, 1, 1) | (2/3, 1, 3/2) | (1, 1, 1) | (2/3, 1, 3/2) | |

Expert 3 | (1, 1, 1) | (2/3, 1, 3/2) | (2/3, 1, 3/2) | (3/2, 2, 5/2) | |

Expert 4 | (1, 1, 1) | (2/3, 1, 3/2) | (3/2, 2, 5/2) | (5/2, 3, 7/2) | |

Expert 5 | (1, 1, 1) | (3/2, 2, 5/2) | (3/2, 2, 5/2) | (3/2, 2, 5/2) | |

BP | Expert 1 | (1, 1, 1) | (1, 1, 1) | (1, 1, 1) | (2/3, 1, 3/2) |

Expert 2 | (2/3, 1, 3/2) | (1, 1, 1) | (2/3, 1, 3/2) | (1, 1, 1) | |

Expert 3 | (2/3, 1, 3/2) | (1, 1, 1) | (2/3, 1, 3/2) | (3/2, 2, 5/2) | |

Expert 4 | (2/3, 1, 3/2) | (1, 1, 1) | (*, *, *) | (3/2, 2, 5/2) | |

Expert 5 | (2/5, 1/2, 2/3) | (1, 1, 1) | (5/2, 3, 7/2) | (5/2, 3, 7/2) | |

IA | Expert 1 | (1, 1, 1) | (1, 1, 1) | (1, 1, 1) | (2/3, 1, 3/2) |

Expert 2 | (1, 1, 1) | (2/3, 1, 3/2) | (1, 1, 1) | (2/3, 1, 3/2) | |

Expert 3 | (2/3, 1, 3/2) | (2/3, 1, 3/2) | (1, 1, 1) | (3/2, 2, 5/2) | |

Expert 4 | (2/5, 1/2, 2/3) | (*, *, *) | (1, 1, 1) | (5/2, 3, 7/2) | |

Expert 5 | (2/5, 1/2, 2/3) | (2/7, 1/3, 2/5) | (1, 1, 1) | (3/2–5/2, 2–3, 5/2–7/2) | |

TD | Expert 1 | (2/3, 1, 3/2) | (2/3, 1, 3/2) | (2/3, 1, 3/2) | (1, 1, 1) |

Expert 2 | (2/3, 1, 3/2) | (1, 1, 1) | (2/3, 1, 3/2) | (1, 1, 1) | |

Expert 3 | (2/5, 1/2, 2/3) | (2/5, 1/2, 2/3) | (2/5, 1/2, 2/3) | (1, 1, 1) | |

Expert 4 | (2/7, 1/3, 2/5) | (2/5, 1/2, 2/3) | (2/7, 1/3, 2/5) | (1, 1, 1) | |

Expert 5 | (2/5, 1/2, 2/3) | (2/7, 1/3, 2/5) | (2/7–2/5, 1/3–1/2, 2/5–2/3) | (1, 1, 1) |

T1 | T2 | T3 | T4 | T5 | T6 | T7 | T8 | T9 | T10 | T11 | T12 | T13 | |||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Input | 500 | 500 | 500 | 500 | 500 | 540 | 540 | 500 | 500 | 500 | 500 | 540 | 540 | ||

Output | EI | Expert 1 | 8 | 7 | 6 | 7 | 7 | 8 | 8 | 8 | 6 | 6 | 5 | 7 | 6 |

Expert 2 | 7 | 8 | 5 | 7 | 7 | 7 | 7 | 8 | 5 | 6 | 6 | 6 | 6 | ||

Expert 3 | 8 | 7 | 5 | 7 | 7 | 7 | 6 | 7 | 6 | 6 | 5 | 6 | 7 | ||

Expert 4 | 9 | 8 | 6 | 7 | 8 | 8 | 9 | 8 | 6 | 7 | 6 | 8 | 7 | ||

Expert 5 | 9 | 8 | 5 | 8 | 8 | 9 | 9 | 9 | 7 | 7 | 7 | 8 | 7 | ||

BP | Expert 1 | 7 | 4 | 5 | 5 | 4 | 8 | 8 | 7 | 6 | 7 | 7 | 7 | 7 | |

Expert 2 | 6 | 5 | 4 | 4 | 5 | 9 | 9 | 8 | 7 | 8 | 6 | 6 | 6 | ||

Expert 3 | 6 | 4 | 4 | 4 | 3 | 6 | 6 | 5 | 5 | 6 | 6 | 5 | 7 | ||

Expert 4 | 8 | 5 | 5 | * | * | 8 | 9 | 8 | 7 | 7 | 7 | * | * | ||

Expert 5 | 8 | 5 | 5 | 6 | 3 | 7 | 7 | 7 | 6 | 7 | 8 | 8 | 7 | ||

IA | Expert 1 | 9 | 7 | 6 | 7 | 7 | 9 | 9 | 8 | 8 | 8 | 8 | 5 | 4 | |

Expert 2 | 7 | 6 | 5 | 7 | 7 | 8 | 8 | 7 | 7 | 7 | 7 | 5 | 3 | ||

Expert 3 | 7 | 5 | 4 | 5 | 5 | 7 | 7 | 6 | 6 | 6 | 6 | 4 | 3 | ||

Expert 4 | 9 | 7 | 6 | 7 | 6 | 9 | 9 | 7 | 7 | 8 | 9 | 6 | 5 | ||

Expert 5 | 8 | 7 | 6 | 8 | 8 | 8 | 9 | 8 | 7–8 | 7–8 | 8–9 | 6 | 5 | ||

TD | Expert 1 | 8 | 7 | 7 | 7 | 7 | 8 | 9 | 8 | 7 | 7 | 7 | 4 | 4 | |

Expert 2 | 6 | 7 | 5 | 6 | 6 | 7 | 7 | 7 | 6 | 6 | 5 | 3 | 3 | ||

Expert 3 | 7 | 6 | 6 | 6 | 7 | 7 | 7 | 7 | 7 | 6 | 6 | 3 | 3 | ||

Expert 4 | 9 | 8 | 6 | 8 | 6 | 8 | 8 | 9 | 6 | 8 | 8 | 5 | 5 | ||

Expert 5 | 7–8 | 6–7 | 8–9 | 8 | 8 | 9 | 9 | 8 | 7 | 8 | 8 | 5 | 5 |

DMU | Input | Output | Efficiency Score | Rank | |||
---|---|---|---|---|---|---|---|

DC | EI | BP | IA | TD | |||

T1 | 500 | 7.67 | 6.33 | 7.67 | 7.00 | 1.000 | 1 |

T2 | 500 | 7.33 | 4.33 | 6.00 | 6.67 | 0.916 | 8 |

T3 | 500 | 5.33 | 4.33 | 5.00 | 6.00 | 0.819 | 13 |

T4 | 500 | 7.00 | 4.33 | 6.33 | 6.33 | 0.877 | 10 |

T5 | 500 | 7.00 | 4.00 | 6.33 | 6.67 | 0.916 | 8 |

T6 | 540 | 7.33 | 7.67 | 8.00 | 7.33 | 1.000 | 1 |

T7 | 540 | 7.00 | 7.67 | 8.00 | 7.67 | 1.000 | 1 |

T8 | 500 | 7.67 | 6.67 | 7.00 | 7.33 | 1.000 | 1 |

T9 | 500 | 5.67 | 6.00 | 7.00 | 6.67 | 0.939 | 6 |

T10 | 500 | 6.00 | 7.00 | 7.00 | 6.33 | 0.986 | 5 |

T11 | 500 | 5.33 | 6.33 | 7.00 | 6.00 | 0.932 | 7 |

T12 | 540 | 6.33 | 6.00 | 4.67 | 3.33 | 0.825 | 12 |

T13 | 540 | 6.33 | 6.67 | 3.33 | 3.33 | 0.870 | 11 |

DMU | Input | Output | Efficiency Score | Rank | |||
---|---|---|---|---|---|---|---|

DC | EI | BP | IA | TD | |||

T1 | 500 | 2.010 | 1.658 | 2.010 | 1.498 | 1.000 | 1 |

T2 | 500 | 1.920 | 1.134 | 1.572 | 1.427 | 0.955 | 6 |

T3 | 500 | 1.396 | 1.134 | 1.310 | 1.284 | 0.818 | 12 |

T4 | 500 | 1.834 | 1.134 | 1.658 | 1.355 | 0.912 | 9 |

T5 | 500 | 1.834 | 1.048 | 1.658 | 1.427 | 0.912 | 9 |

T6 | 540 | 1.920 | 2.010 | 2.096 | 1.569 | 1.000 | 1 |

T7 | 540 | 1.834 | 2.010 | 2.096 | 1.641 | 1.000 | 1 |

T8 | 500 | 2.010 | 1.748 | 1.834 | 1.569 | 1.000 | 1 |

T9 | 500 | 1.486 | 1.572 | 1.834 | 1.427 | 0.938 | 7 |

T10 | 500 | 1.572 | 1.834 | 1.834 | 1.355 | 0.985 | 5 |

T11 | 500 | 1.396 | 1.658 | 1.834 | 1.284 | 0.932 | 8 |

T12 | 540 | 1.658 | 1.572 | 1.224 | 0.713 | 0.808 | 13 |

T13 | 540 | 1.658 | 1.748 | 0.872 | 0.713 | 0.870 | 11 |

EI | BP | IA | TD | |
---|---|---|---|---|

EI | (1.000, 1.000, 1.000) | (0.778, 1.000, 1.333) | (0.889, 1.000, 1.167) | (0.944, 1.333, 1.833) |

BP | (0.750, 1.000, 1.286) | (1.000, 1.000, 1.000) | (0.778, 1.000, 1.333) | (1.056, 1.333, 1.667) |

IA | (0.857, 1.000, 1.125) | (0.750, 1.000, 1.286) | (1.000, 1.000, 1.000) | (0.944, 1.333, 1.833) |

TD | (0.545, 0.750, 1.059) | (0.600, 0.750, 0.947) | (0.545, 0.750, 1.059) | (1.000, 1.000, 1.000) |

$\mathit{V}\left({\mathit{S}}_{1}\ge {\mathit{S}}_{\mathit{i}}\right)$ | Value | $\mathit{V}\left({\mathit{S}}_{2}\ge {\mathit{S}}_{\mathit{i}}\right)$ | Value |
---|---|---|---|

$V\left({S}_{1}\ge {S}_{2}\right)$ | 1.000 | $V\left({S}_{2}\ge {S}_{1}\right)$ | 1.000 |

$V\left({S}_{1}\ge {S}_{3}\right)$ | 1.000 | $V\left({S}_{2}\ge {S}_{3}\right)$ | 1.000 |

$V\left({S}_{1}\ge {S}_{4}\right)$ | 1.000 | $V\left({S}_{2}\ge {S}_{4}\right)$ | 1.000 |

$V\left({S}_{3}\ge {S}_{i}\right)$ | Value | $V\left({S}_{4}\ge {S}_{i}\right)$ | Value |

$V\left({S}_{3}\ge {S}_{1}\right)$ | 1.000 | $V\left({S}_{4}\ge {S}_{1}\right)$ | 0.645 |

$V\left({S}_{3}\ge {S}_{2}\right)$ | 1.000 | $V\left({S}_{4}\ge {S}_{2}\right)$ | 0.648 |

$V\left({S}_{3}\ge {S}_{4}\right)$ | 1.000 | $V\left({S}_{4}\ge {S}_{3}\right)$ | 0.651 |

DMU | Input | Output | Efficiency Score | Rank | |||
---|---|---|---|---|---|---|---|

DC | EI | BP | IA | TD | |||

T1 | 500 | 2.104 | 1.737 | 2.104 | 1.239 | 1.000 | 1 |

T2 | 500 | 2.011 | 1.188 | 1.646 | 1.180 | 0.956 | 6 |

T3 | 500 | 1.462 | 1.188 | 1.372 | 1.062 | 0.819 | 12 |

T4 | 500 | 1.920 | 1.188 | 1.737 | 1.120 | 0.913 | 9 |

T5 | 500 | 1.920 | 1.097 | 1.737 | 1.180 | 0.913 | 9 |

T6 | 540 | 2.011 | 2.104 | 2.195 | 1.297 | 1.000 | 1 |

T7 | 540 | 1.920 | 2.104 | 2.195 | 1.357 | 1.000 | 1 |

T8 | 500 | 2.104 | 1.830 | 1.920 | 1.297 | 1.000 | 1 |

T9 | 500 | 1.556 | 1.646 | 1.920 | 1.180 | 0.938 | 7 |

T10 | 500 | 1.646 | 1.920 | 1.920 | 1.120 | 0.986 | 5 |

T11 | 500 | 1.462 | 1.737 | 1.920 | 1.062 | 0.932 | 8 |

T12 | 540 | 1.737 | 1.646 | 1.281 | 0.589 | 0.808 | 13 |

T13 | 540 | 1.737 | 1.830 | 0.914 | 0.589 | 0.870 | 11 |

DMU | Arithmetic Mean | Weighted | Efficiency Score | Rank | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|

DC | EI | BP | IA | TD | EI | BP | IA | TD | |||

T1 | 500 | 8.200 | 7.000 | 8.000 | 7.500 | 2.452 | 2.016 | 1.888 | 1.328 | 1.000 | 1 |

T2 | 500 | 7.600 | 4.600 | 6.400 | 6.900 | 2.272 | 1.325 | 1.510 | 1.221 | 0.927 | 7 |

T3 | 500 | 5.400 | 4.600 | 5.400 | 6.500 | 1.615 | 1.325 | 1.274 | 1.151 | 0.833 | 13 |

T4 | 500 | 7.200 | 4.750 | 6.800 | 7.000 | 2.153 | 1.368 | 1.605 | 1.239 | 0.909 | 8 |

T5 | 500 | 7.400 | 3.750 | 6.600 | 6.800 | 2.213 | 1.080 | 1.558 | 1.204 | 0.904 | 9 |

T6 | 540 | 7.800 | 7.600 | 8.200 | 7.800 | 2.332 | 2.189 | 1.935 | 1.381 | 0.979 | 4 |

T7 | 540 | 7.800 | 7.800 | 8.400 | 8.000 | 2.332 | 2.246 | 1.982 | 1.416 | 1.000 | 1 |

T8 | 500 | 8.000 | 7.000 | 7.200 | 7.800 | 2.392 | 2.016 | 1.699 | 1.381 | 1.000 | 1 |

T9 | 500 | 6.000 | 6.200 | 7.100 | 6.600 | 1.794 | 1.786 | 1.676 | 1.168 | 0.888 | 10 |

T10 | 500 | 6.400 | 7.000 | 7.300 | 7.000 | 1.914 | 2.016 | 1.723 | 1.239 | 0.969 | 5 |

T11 | 500 | 5.800 | 6.800 | 7.700 | 6.800 | 1.734 | 1.958 | 1.817 | 1.204 | 0.967 | 6 |

T12 | 540 | 7.000 | 6.500 | 5.200 | 4.000 | 2.093 | 1.872 | 1.227 | 0.708 | 0.845 | 12 |

T13 | 540 | 6.600 | 6.750 | 4.000 | 4.000 | 1.973 | 1.944 | 0.944 | 0.708 | 0.866 | 11 |

DMU | Efficiency Score | Rank | ||||||
---|---|---|---|---|---|---|---|---|

Average | AHP | Fuzzy AHP | SAHP | Typical DEA Method [63] | Typical AHP/DEA Method [64] | Fuzzy AHP/DEA Method [43] | Proposed Method | |

T1 | 1.000 | 1.000 | 1.000 | 1.000 | 1 | 1 | 1 | 1 |

T2 | 0.917 | 0.955 | 0.956 | 0.927 | 8 | 6 | 6 | 7 |

T3 | 0.818 | 0.818 | 0.819 | 0.833 | 13 | 12 | 12 | 13 |

T4 | 0.877 | 0.912 | 0.913 | 0.909 | 10 | 9 | 9 | 8 |

T5 | 0.916 | 0.912 | 0.913 | 0.904 | 9 | 9 | 9 | 9 |

T6 | 1.000 | 1.000 | 1.000 | 0.979 | 1 | 1 | 1 | 4 |

T7 | 1.000 | 1.000 | 1.000 | 1.000 | 1 | 1 | 1 | 1 |

T8 | 1.000 | 1.000 | 1.000 | 1.000 | 1 | 1 | 1 | 1 |

T9 | 0.939 | 0.938 | 0.938 | 0.888 | 6 | 7 | 7 | 10 |

T10 | 0.985 | 0.985 | 0.986 | 0.969 | 5 | 5 | 5 | 5 |

T11 | 0.932 | 0.932 | 0.932 | 0.967 | 7 | 8 | 8 | 6 |

T12 | 0.824 | 0.808 | 0.808 | 0.845 | 12 | 13 | 13 | 12 |

T13 | 0.870 | 0.870 | 0.870 | 0.866 | 11 | 11 | 11 | 11 |

Method Selection | Solving Characteristic | ||||
---|---|---|---|---|---|

Weight Consideration | Ambiguous or Hesitant Information | Missing or Nonexistence Information | Consideration of All Available Information | Consistency Check | |

Typical DEA method [63] | No | No | No | No | No |

Typical AHP/DEA method [64] | Yes | No | No | No | Yes |

Fuzzy AHP/DEA method [43] | Yes | Yes | No | No | No |

Proposed method | Yes | Yes | Yes | Yes | Yes |

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## Share and Cite

**MDPI and ACS Style**

Chung, H.-Y.; Chang, K.-H.
A Novel General Data Envelopment Analysis Based Approach for MCDM Issues of Hydrogen Energy under a Fuzzy Environment. *Systems* **2022**, *10*, 176.
https://doi.org/10.3390/systems10050176

**AMA Style**

Chung H-Y, Chang K-H.
A Novel General Data Envelopment Analysis Based Approach for MCDM Issues of Hydrogen Energy under a Fuzzy Environment. *Systems*. 2022; 10(5):176.
https://doi.org/10.3390/systems10050176

**Chicago/Turabian Style**

Chung, Hsiang-Yu, and Kuei-Hu Chang.
2022. "A Novel General Data Envelopment Analysis Based Approach for MCDM Issues of Hydrogen Energy under a Fuzzy Environment" *Systems* 10, no. 5: 176.
https://doi.org/10.3390/systems10050176