# Fuzzy TOPSIS-EW Method with Multi-Granularity Linguistic Assessment Information for Emergency Logistics Performance Evaluation

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## Abstract

**:**

## 1. Introduction

## 2. Key Problem Statement

#### 2.1. Emergency Logistics System

**Emergency preparedness**is a key link to prevent the occurrence of emergencies, monitor risks and make preparations for reducing the consequences of disasters so that the government and the people form the necessary emergency capacity [33].**Emergency response**is an emergency action, the main function is to send rescue teams and social forces to the disaster areas in a very short time, deliver and deploy rescue materials and conduct other activities in the disaster area [9], so the timeliness and coordination of emergency response largely determine the performance of this stage.**Post-disaster recovery and reconstruction**refers to the ability to ensure the basic life of the masses and quickly restore the order of life in the affected areas.

#### 2.2. Evaluation Indicator System

- The accuracy principle. The definitions of objective indicators should be accurate and the source of data should be reliable. The acquisition of subjective indicators should meet the requirements of comprehensive evaluation techniques.
- The integrity principle. The evaluation indicator system should not only include the contents of prediction, response and recovery, but also consider the organization, coordination and information transmit in the process of emergency logistics operations.
- The easy-to-operate principle. The indicators are clear in definition, and the data is of good accessibility, which can fully show the actual level of emergency logistics, and make for the improvement of performance.
- The independent principle. Indicators should be independent of each other, avoiding duplicate evaluations.
- The comparable principle. To make the evaluation results about indicators comparable in different alternatives, the definitions and measurement criteria of indicators should be normalized.

## 3. Evaluation Methodology

#### 3.1. Overall Framework

#### 3.2. Indicators Description

#### 3.2.1. Information Processing

#### 3.2.2. Logistics Operation

#### 3.2.3. Organization and Coordination

#### 3.2.4. Post-Disaster Response

#### 3.3. MGLA Approach

**Step 1.**At times, the experts employ multi-granularity linguistic term sets to express their own judgment information. In this case, let ${S}^{\beta}=\left\{{S}_{y}^{\beta}\right|y\in \{0,1,\dots ,\beta -1\}\}$ be the $\beta $th pre-established confined and completely ordered linguistic term set, where $\beta $ is called the granularity of ${S}^{\beta}$, ${S}_{y}^{\beta}$ denotes the yth linguistic term of set ${S}^{\beta}$. The judgment of each individual expert can be fully expressed by the utilization of the linguistic label set of granularity.

**Step 2.**Considering these original linguistic terms are obtained from different experts with different linguistic granularities, they should be unified first. This study utilizes some transformation functions to unify the derived multi-granularity linguistic labels into a consistent linguistic term set. With regard to any two linguistic term sets ${\overline{S}}^{\left({\beta}_{1}\right)}=\left\{{S}_{\mu}^{\left({\beta}_{1}\right)}\right|\phantom{\rule{4pt}{0ex}}\mu \in [0,{\beta}_{1}-1]\}$ and ${\overline{S}}^{\left({\beta}_{2}\right)}=\left\{{S}_{\nu}^{\left({\beta}_{2}\right)}\right|\phantom{\rule{4pt}{0ex}}\nu \in [0,{\beta}_{2}-1]\}$, the transformation functions between them are presented below [44]:

**Step 3.**Conducting improved standard and mean deviations method [44]. Let ${S}_{\eta},{S}_{\theta}\in {S}^{\beta}$ be two linguistic variables, the deviation between ${S}_{\eta}$ and ${S}_{\theta}$ is expressed as $d({S}_{\eta},{S}_{\theta})=\left|\right|{S}_{\eta}-{S}_{\theta}\left|\right|=|\eta -\theta |$. To expert ${e}_{k}$ and indicator ${A}_{j}$, the standard deviation between region ${R}_{i}$ and others is as follows [45]:

**Step 4.**Based on the above analysis, maximizing the total mean and standard deviation of all the evaluation indicators to determine the weighting vector w. The objective function is established below:

**Step 5.**The kth expert’s decision matrix is

#### 3.4. Weights Determination

#### 3.4.1. Hesitant Fuzzy Linguistic Judgment Description

#### 3.4.2. Expert Weights Determination

#### 3.4.3. Indicator Weights Computation

**Step 1.**As the lengths of hesitant fuzzy judgements may not be equal, we must use the extension precedure to ensure all fuzzy judgments have the same length: ${t}_{mh}=\left\{{t}_{mh}^{l}\right|l=1,2,\dots ,L;\phantom{\rule{4pt}{0ex}}m=1,2,\dots ,u;\phantom{\rule{4pt}{0ex}}h=1,2,\dots ,p\}$.

**Step 2.**With the expert weights ${w}_{m}^{E}\phantom{\rule{4pt}{0ex}}(m=1,2,\dots ,u)$ determined from optimization model (30), the weighted and extended hesitant fuzzy judgments can be obtained as follows:

**Step 3.**Furthermore, the weighted average operator parameters can be calculated using the following formulas:

**Step 4.**Based on Equations (32)–(34), hesitant fuzzy judgments can be transformed into triangular fuzzy numbers $({\overline{\rho}}_{h},{\overline{\sigma}}_{h},{\overline{\varsigma}}_{h})$. Similar to de-fuzzification for the intuitionistic fuzzy numbers, the weighted average operator can be utilized to calculate the weight of the hth indicator:

**Step 1.**Assuming that the original data matrix R is obtained as follows:

**Step 2.**Then data matrix R is converted into the normalized matrix O as follows:

- (1)
- Normalized matrix of benefit indicators:$${a}_{ih}=\frac{{r}_{ih}-\mathrm{min}\left({r}_{ih}\right)}{\mathrm{max}\left({r}_{ih}\right)-\mathrm{min}\left({r}_{ih}\right)},(i=1,2\dots ,q;\phantom{\rule{4pt}{0ex}}h=1,2\dots ,p).$$
- (2)
- Normalized matrix of cost indicators:$${a}_{ih}=\frac{\mathrm{max}\left({r}_{ih}\right)-{r}_{ih}}{\mathrm{max}\left({r}_{ih}\right)-\mathrm{min}\left({r}_{ih}\right)},(i=1,2\dots ,q;\phantom{\rule{4pt}{0ex}}h=1,2\dots ,p).$$

**Step 3.**Calculate the entropy value of the hth indicator:

**Step 4.**Calculate the weight of the indicator as is as below:

#### 3.5. Relative Closeness to the Ideal Solution Based on TOPSIS

**Step 1.**Calculate the normalized decision matrix, the vector normalization is applied to obtain ${b}_{ih}$ as follows.

**Step 2.**Obtain the expert weights ${w}_{m}^{E}\phantom{\rule{4pt}{0ex}}(m=1,2,\dots ,u)$ using optimization model (30), and the indicator weights ${w}_{h}^{A}\phantom{\rule{4pt}{0ex}}(h=1,2,\dots ,p)$ are determined according to Equation (41).

**Step 3.**Construct the weighted and normalized evaluation matrix Z as follows:

**Step 4.**Determine the best indicator ${\lambda}^{+}$ and worst indicator ${\lambda}^{-}$ respectively. In this paper, there are both benefit indicators and cost indicators, so some indicators are best when they are close to a specific value (denoted ${Z}_{f}$). Then the new values for these indicators are $|{Z}_{g}-{Z}_{f}|$, where ${Z}_{g}$ represents the real values. Therefore, the ideal indicator values ${\lambda}^{+}$ and ${\lambda}^{-}$ are determined as

**Step 5.**Obtain the Euclidean distance between each region (i.e., alternative) and ${\lambda}^{+}$ as follows:

**Step 6.**Compute the relative closeness of each region (i.e., alternative) to the most preferable ${\lambda}^{+}$ using the following formula:

**Step 7.**Rank the alternatives (i.e., regions) by sorting ${Y}_{i}^{+}\phantom{\rule{4pt}{0ex}}(i=1,2,\dots ,q)$ in descending order as ${Y}_{i}^{+}$ can serve as the evaluation score of the emergency logistics performance. In other words, the higher value of ${Y}_{i}^{+}$ indicates better regional emergency logistics performance.

## 4. Case Study

#### 4.1. Case Description

#### 4.2. Data Source

#### 4.3. Result Interpretation

#### 4.4. Comparison Analysis

#### 4.5. Sensitivity Analysis

#### 4.6. Managerial Suggestions

- (1)
- Increasing emergency material reserves. After the disaster, the disaster area is in urgent need of adequate supplies of life support. However, the actual amount of materials is always in short supply, affecting the follow-up relief work. In addition, reducing the storage space and saving storage costs can also improve relief work efficiency.
- (2)
- Improving the informatization of emergency logistics management. it is advised to accelerate the speed of information acquisition and apply advanced information and communication technology to improve the speed and accuracy of pre-disaster prediction, material transportation, and information transmission, and further improve the speed of information transportation command and dispatch and emergency response. For example, it took several hours for Qingchuan County to obtain information about the disaster, which seriously affected the timeliness of disaster relief activities.
- (3)
- Strengthening the construction of contingency logistics plans. Firstly, it is needed to rehearse the emergency logistics plan, improve the practical operation ability of professionals, and increase the emergency response ability. In addition, what should be done is to test, revise, and improve the emergency plan, and more effectively deal with sudden and complex emergencies in practice.
- (4)
- Improving a cross-departmental and cross-regional linkage and cooperation mechanism. For the first time after the disaster, all departments should formulate a common code of action and strengthen mutual communication and cooperation to ensure that personnel and materials can reach the emergency site as quickly and safely as possible. For example, the Wenchuan County and Beichuan County after the earthquake set up an emergency response working group later than the other three areas.

## 5. Conclusions and Future Research Direction

## Author Contributions

## Funding

## Conflicts of Interest

## References

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Challenges Faced | Strategies Adopted |
---|---|

Selection of evaluation indicators | Considering the whole cycle of disasters |

Quantification of some indicators | MGLA method |

Determination of expert weights | Maximum group consensus and minimum hesitation |

Determination of indicator weight | Subjective-objective weighing of the method |

Comparison of regional performance levels | Fuzzy TOPSIS method |

Original Hesitant Fuzzy Terms | Extensions |
---|---|

${K}_{N}^{1}=\left\{0.25\right\}$ | {0.25, 0.25, 0.25, 0.25, 0.25} |

${K}_{N}^{2}=\{0.625,0.75,0.875,1.0\}$ | {0.625, 0.75, 0.8125, 0.875, 1.0} |

${K}_{N}^{3}=\{0.375,0.5,0.625\}$ | {0.375, 0.5, 0.5, 0.5, 0.625} |

${K}_{N}^{4}=\{0.25,0.375,0.5,0.625,0.75\}$ | {0.25, 0.375, 0.5, 0.625, 0.75} |

${K}_{N}^{5}=\{0.75,0.875\}$ | {0.75, 0.8125, 0.8125, 0.8125, 0.875} |

${\mathit{B}}_{13}$ | ${\mathit{B}}_{22}$ | ${\mathit{C}}_{32}$ | ${\mathit{C}}_{41}$ | ${\mathit{C}}_{42}$ | ${\mathit{D}}_{22}$ | |
---|---|---|---|---|---|---|

W | ${S}_{-1}^{5}$ | ${S}_{0}^{5}$ | ${S}_{1}^{5}$ | ${S}_{0}^{5}$ | ${S}_{1}^{5}$ | ${S}_{2}^{5}$ |

B | ${S}_{0}^{5}$ | ${S}_{1}^{5}$ | ${S}_{0}^{5}$ | ${S}_{2}^{5}$ | ${S}_{-1}^{5}$ | ${S}_{1}^{5}$ |

Q | ${S}_{1}^{5}$ | ${S}_{1}^{5}$ | ${S}_{2}^{5}$ | ${S}_{-1}^{5}$ | ${S}_{0}^{5}$ | ${S}_{1}^{5}$ |

M | ${S}_{1}^{5}$ | ${S}_{2}^{5}$ | ${S}_{1}^{5}$ | ${S}_{1}^{5}$ | ${S}_{-1}^{5}$ | ${S}_{0}^{5}$ |

S | ${S}_{2}^{5}$ | ${S}_{2}^{5}$ | ${S}_{1}^{5}$ | ${S}_{1}^{5}$ | ${S}_{1}^{5}$ | ${S}_{1}^{5}$ |

${\mathit{B}}_{13}$ | ${\mathit{B}}_{22}$ | ${\mathit{C}}_{32}$ | ${\mathit{C}}_{41}$ | ${\mathit{C}}_{42}$ | ${\mathit{D}}_{22}$ | |
---|---|---|---|---|---|---|

W | ${S}_{0}^{7}$ | ${S}_{2}^{7}$ | ${S}_{1}^{7}$ | ${S}_{2}^{7}$ | ${S}_{0}^{7}$ | ${S}_{1}^{7}$ |

B | ${S}_{-1}^{7}$ | ${S}_{1}^{7}$ | ${S}_{2}^{7}$ | ${S}_{1}^{7}$ | ${S}_{-1}^{7}$ | ${S}_{0}^{7}$ |

Q | ${S}_{1}^{7}$ | ${S}_{3}^{7}$ | ${S}_{2}^{7}$ | ${S}_{1}^{7}$ | ${S}_{-2}^{7}$ | ${S}_{1}^{7}$ |

M | ${S}_{1}^{7}$ | ${S}_{1}^{7}$ | ${S}_{1}^{7}$ | ${S}_{0}^{7}$ | ${S}_{1}^{7}$ | ${S}_{2}^{7}$ |

S | ${S}_{0}^{7}$ | ${S}_{1}^{7}$ | ${S}_{0}^{7}$ | ${S}_{1}^{7}$ | ${S}_{2}^{7}$ | ${S}_{1}^{7}$ |

${\mathit{B}}_{13}$ | ${\mathit{B}}_{22}$ | ${\mathit{C}}_{32}$ | ${\mathit{C}}_{41}$ | ${\mathit{C}}_{42}$ | ${\mathit{D}}_{22}$ | |
---|---|---|---|---|---|---|

W | ${S}_{1}^{9}$ | ${S}_{2}^{9}$ | ${S}_{1}^{9}$ | ${S}_{2}^{9}$ | ${S}_{3}^{9}$ | ${S}_{1}^{9}$ |

B | ${S}_{3}^{9}$ | ${S}_{2}^{9}$ | ${S}_{-1}^{9}$ | ${S}_{-2}^{9}$ | ${S}_{2}^{9}$ | ${S}_{0}^{9}$ |

Q | ${S}_{2}^{9}$ | ${S}_{3}^{9}$ | ${S}_{0}^{9}$ | ${S}_{1}^{9}$ | ${S}_{0}^{9}$ | ${S}_{1}^{9}$ |

M | ${S}_{1}^{9}$ | ${S}_{1}^{9}$ | ${S}_{2}^{9}$ | ${S}_{0}^{9}$ | ${S}_{-1}^{9}$ | ${S}_{-2}^{9}$ |

S | ${S}_{1}^{9}$ | ${S}_{2}^{9}$ | ${S}_{0}^{9}$ | ${S}_{-3}^{9}$ | ${S}_{0}^{9}$ | ${S}_{2}^{9}$ |

${\mathit{B}}_{13}$ | ${\mathit{B}}_{22}$ | ${\mathit{C}}_{32}$ | ${\mathit{C}}_{41}$ | ${\mathit{C}}_{42}$ | ${\mathit{D}}_{22}$ | |
---|---|---|---|---|---|---|

W | ${S}_{-2}^{9}$ | ${S}_{0}^{9}$ | ${S}_{2}^{9}$ | ${S}_{0}^{9}$ | ${S}_{2}^{9}$ | ${S}_{4}^{9}$ |

B | ${S}_{0}^{9}$ | ${S}_{2}^{9}$ | ${S}_{0}^{9}$ | ${S}_{4}^{9}$ | ${S}_{-2}^{9}$ | ${S}_{2}^{9}$ |

Q | ${S}_{2}^{9}$ | ${S}_{2}^{9}$ | ${S}_{4}^{9}$ | ${S}_{-2}^{9}$ | ${S}_{0}^{9}$ | ${S}_{2}^{9}$ |

M | ${S}_{2}^{9}$ | ${S}_{4}^{9}$ | ${S}_{2}^{9}$ | ${S}_{2}^{9}$ | ${S}_{-2}^{9}$ | ${S}_{0}^{9}$ |

S | ${S}_{4}^{9}$ | ${S}_{4}^{9}$ | ${S}_{2}^{9}$ | ${S}_{2}^{9}$ | ${S}_{2}^{9}$ | ${S}_{2}^{9}$ |

${\mathit{B}}_{13}$ | ${\mathit{B}}_{22}$ | ${\mathit{C}}_{32}$ | ${\mathit{C}}_{41}$ | ${\mathit{C}}_{42}$ | ${\mathit{D}}_{22}$ | |
---|---|---|---|---|---|---|

W | ${S}_{0}^{9}$ | ${S}_{8/3}^{9}$ | ${S}_{4/3}^{9}$ | ${S}_{8/3}^{9}$ | ${S}_{0}^{9}$ | ${S}_{4/3}^{9}$ |

B | ${S}_{-4/3}^{9}$ | ${S}_{4/3}^{9}$ | ${S}_{8/3}^{9}$ | ${S}_{4/3}^{9}$ | ${S}_{-4/3}^{9}$ | ${S}_{0}^{9}$ |

Q | ${S}_{4/3}^{9}$ | ${S}_{4}^{9}$ | ${S}_{8/3}^{9}$ | ${S}_{4/3}^{9}$ | ${S}_{-8/3}^{9}$ | ${S}_{4/3}^{9}$ |

M | ${S}_{4/3}^{9}$ | ${S}_{4/3}^{9}$ | ${S}_{4/3}^{9}$ | ${S}_{0}^{9}$ | ${S}_{4/3}^{9}$ | ${S}_{8/3}^{9}$ |

S | ${S}_{0}^{9}$ | ${S}_{4/3}^{9}$ | ${S}_{0}^{9}$ | ${S}_{4/3}^{9}$ | ${S}_{8/3}^{9}$ | ${S}_{4/3}^{9}$ |

${\mathit{B}}_{13}$ | ${\mathit{B}}_{22}$ | ${\mathit{C}}_{32}$ | ${\mathit{C}}_{41}$ | ${\mathit{C}}_{42}$ | ${\mathit{D}}_{22}$ | |
---|---|---|---|---|---|---|

W | ${S}_{1}^{9}$ | ${S}_{2}^{9}$ | ${S}_{1}^{9}$ | ${S}_{2}^{9}$ | ${S}_{3}^{9}$ | ${S}_{1}^{9}$ |

B | ${S}_{3}^{9}$ | ${S}_{2}^{9}$ | ${S}_{-1}^{9}$ | ${S}_{-2}^{9}$ | d ${S}_{2}^{9}$ | ${S}_{0}^{9}$ |

Q | ${S}_{2}^{9}$ | ${S}_{3}^{9}$ | ${S}_{0}^{9}$ | ${S}_{1}^{9}$ | ${S}_{0}^{9}$ | ${S}_{1}^{9}$ |

M | ${S}_{1}^{9}$ | ${S}_{1}^{9}$ | ${S}_{2}^{9}$ | ${S}_{0}^{9}$ | ${S}_{-1}^{9}$ | ${S}_{-2}^{9}$ |

S | ${S}_{1}^{9}$ | ${S}_{2}^{9}$ | ${S}_{0}^{9}$ | ${S}_{-3}^{9}$ | ${S}_{0}^{9}$ | ${S}_{2}^{9}$ |

Indicator | ${\mathit{E}}_{1\mathit{hi}}$ | ${\mathit{E}}_{2\mathit{hi}}$ | ${\mathit{E}}_{3\mathit{hi}}$ | ${\mathit{E}}_{4\mathit{hi}}$ | ${\mathit{E}}_{5\mathit{hi}}$ |
---|---|---|---|---|---|

${B}_{11}$ | (0.5) | (0.5, 0.625) | (0.5, 0.625, 0.75) | (0.5) | (0.25, 0.375) |

${B}_{12}$ | (0.125, 0.25, 0.375) | (0.5) | (0.375) | (0.5, 0.625) | (0.375) |

${B}_{13}$ | (0.875) | (0.75) | (0.75, 0.825, 1) | (0.75) | (0.75, 0.875) |

${B}_{21}$ | (0.375) | (0.625, 0.75) | (0.5) | (0.25, 0.375, 0.5) | (0.5, 0.625) |

${B}_{22}$ | (0.625, 0.75) | (0.875, 1) | (0.75, 0.875) | (0.875) | (0.625, 0.75) |

${B}_{31}$ | (0.375) | (0.25, 0.375, 0.5) | (0.5) | (0.625) | (0.2, 0.375) |

${C}_{11}$ | (0.875) | (0.75, 0.875, 1) | (0.75, 0.875, 1) | (1) | (0.75, 0.875) |

${C}_{21}$ | (0.25, 0.375) | (0.25, 0.375, 0.5) | (0.375) | (0.25, 0.375) | (0.25) |

${C}_{31}$ | (1) | (0.875, 1) | (0.625, 0.75, 0.875, 1) | (1) | (0.875, 1) |

${C}_{32}$ | (0.625, 0.75, 0.875) | (0.875) | (0.625, 0.75) | (0.625, 0.75) | (0.75, 0.875, 1) |

${C}_{41}$ | (0.5, 0.625) | (0.375, 0.5, 0.625) | (0.375) | (0.625, 0.75, 0.875, 1) | (0.625) |

${C}_{42}$ | (0.25) | (0.25, 0.375) | (0.125, 0.25) | (0.375) | (0.125, 0.25, 0.375) |

${D}_{11}$ | (0.875, 1) | (1) | (1) | (0.75, 0.875) | (0.875) |

${D}_{12}$ | (0.75) | (0.5) | (0.75, 0.875) | (0.75, 0.875) | (0.75, 0.825) |

${D}_{21}$ | (0.25) | (0.375) | (0.5, 0.625) | (0.25, 0.375, 0.5) | (0.25) |

${D}_{22}$ | (0.375) | (0.25, 0.375) | (0.625) | (0.125, 0.25) | (0.25, 0.375) |

${D}_{31}$ | (0.5, 0.625, 0.75) | (0.5) | (0.5, 0.625) | (0.625.0.75) | (0.5, 0.625, 0.75) |

${H}_{11}$ | (0.25, 0.375, 0.5, 0.625, 0.75) | (0.25, 0.375) | (0.25) | (0.25, 0.375) | (0.5) |

${H}_{12}$ | (0.875) | (0.875, 1) | (0.625, 0.75) | (0.875, 1) | (0.75, 0.875) |

${H}_{13}$ | (0.25, 0.375, 0.5) | (0.5) | (0.25) | (0.5) | (0.375, 0.50.625, 0.75, 0875) |

${H}_{21}$ | (0.875) | (1) | (0.75) | (1) | (0.875, 1) |

${H}_{22}$ | (0.625, 0.75, 0.875) | (0.875) | (0.75, 0.875) | (0.5, 0.625) | (0.375, 0.5, 0.625) |

Region | Fuzzy TOPSIS-EW Method | TOPSIS-EW Method | TOPSIS Method with Equal Expert Weights | |||
---|---|---|---|---|---|---|

${\mathit{Y}}_{\mathit{i}}^{+}$ | Rank by ${\mathit{Y}}_{\mathit{i}}^{+}$ | ${\mathit{Y}}_{\mathit{i}}^{{}^{\prime}+}$ | Rank by ${\mathit{Y}}_{\mathit{i}}^{{}^{\prime}+}$ | ${\mathit{Y}}_{\mathit{i}}^{{}^{\u2033}+}$ | Rank by ${\mathit{Y}}_{\mathit{i}}^{{}^{\u2033}+}$ | |

W | 0.0971 | V | 0.0793 | V | 0.1178 | V |

B | 0.4465 | III | 0.5118 | II | 0.3584 | III |

Q | 0.8906 | I | 0.9083 | I | 0.8696 | I |

M | 0.4518 | II | 0.4002 | III | 0.5159 | II |

S | 0.2012 | IV | 0.2273 | IV | 0.1727 | IV |

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## Share and Cite

**MDPI and ACS Style**

Liu, Y.; Li, L.; Tu, Y.; Mei, Y.
Fuzzy TOPSIS-EW Method with Multi-Granularity Linguistic Assessment Information for Emergency Logistics Performance Evaluation. *Symmetry* **2020**, *12*, 1331.
https://doi.org/10.3390/sym12081331

**AMA Style**

Liu Y, Li L, Tu Y, Mei Y.
Fuzzy TOPSIS-EW Method with Multi-Granularity Linguistic Assessment Information for Emergency Logistics Performance Evaluation. *Symmetry*. 2020; 12(8):1331.
https://doi.org/10.3390/sym12081331

**Chicago/Turabian Style**

Liu, Yanwu, Liang Li, Yan Tu, and Yanlan Mei.
2020. "Fuzzy TOPSIS-EW Method with Multi-Granularity Linguistic Assessment Information for Emergency Logistics Performance Evaluation" *Symmetry* 12, no. 8: 1331.
https://doi.org/10.3390/sym12081331