# Research on Comprehensive Evaluation Model of a Truck Dispatching System in Open-Pit Mine

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## Abstract

**:**

## 1. Introduction

## 2. Construction of Comprehensive Evaluation Factor System for Truck Dispatching System in Open-Pit Mines

## 3. Theoretical Basis

#### 3.1. Blind Number Theory

_{i}$\in $ G, f(x) is a gray function on G. If α

_{i}$\in $ (0, 1) (i = 1, 2, …, n), and

_{i}≠ x

_{j}, and $\sum}_{i=1}^{n}{\alpha}_{i}=\alpha \le 1$, the function f(x) is called a blind number. It is represented by $\left\{\left[{x}_{1},{x}_{n}\right],f\left(x\right)\right\}$, where x

_{1}is the lower limit of x, and x

_{n}is the upper limit of x. Hence, ${\alpha}_{i}$ is the credibility of x in x

_{i}, and $\alpha ={\displaystyle \sum}_{i=1}^{n}{\alpha}_{i}$ is the total credibility of x.

#### 3.2. Factor Weight

#### 3.2.1. G1 Method to Calculate the Subjective Weight of Factors

- (1)
- Determine the factor sequence. In order to evaluate the object, it is assumed that n evaluation factors are selected. After all the evaluators are fully discussed and a consensus is formed, the most important factor is selected from the n factors, denoted as X
_{1}, and the weight is denoted as w_{1}. Then, the most important factor is selected from the remaining n − 1 factors until all n factors are selected according to their relative importance in sequence, yielding the factor sequence (X_{1}, X_{2}, …, X_{n}). - (2)
- Determine the relative importance ratio of factors. The relative importance ratio r
_{j}is determined by the evaluator according to the factor sequence (X_{1}, X_{2}, …, X_{n}). Table 2 presents a description of the relative importance factor. The relative importance ratio r_{j}is calculated using Equation (2).$${r}_{j}=\frac{{w}_{j}}{{w}_{j-1}}j=2,3,\cdots n.$$ - (3)
- The n-th factor weight is calculated using Equation (3).$${w}_{n}={(1+{\displaystyle \sum}_{k=2}^{n}{\displaystyle \coprod}_{j=k}^{n}{r}_{j})}^{-1}.$$
- (4)
- The remaining n − 1 factor weights are calculated using Equation (4).$${w}_{j-1}={r}_{j}{w}_{j}.$$

#### 3.2.2. Improved CRITIC Method to Determine Objective Weight of Factors

- (1)
- Build the original evaluation matrix. Assuming that n evaluation factors are selected and m objects are evaluated, the original evaluation factor matrix X is constructed as follows:

- (2)
- Normalize original evaluation matrix. The matrix X is normalized on the basis of the Z-score, and the normalized matrix X* is obtained. The normalization formula is as follows:$${x}_{j}^{*}\left({k}_{i}\right)=\frac{{x}_{j}\left({k}_{i}\right)-\overline{{x}_{j}}}{{s}_{j}},$$
_{j}is the standard deviation of the j-th factor.

- (3)
- Calculate the coefficient of variation. In order to compare the factors more conveniently, the coefficient of variation is introduced. The coefficient of variation is calculated using Equation (7).$${v}_{j}=\frac{{s}_{j}}{\overline{{x}_{j}}},$$
_{j}is the coefficient of variation of the j-th factor.

- (4)
- Determine the coefficient of independence. The correlation coefficient matrix of the normalized matrix X* is determined using the Pearson’s coefficient of correlation, and the independence coefficient between the factors is determined using the correlation coefficient matrix.$$\sum}_{q=1}^{n}\left(1-{\rho}_{q1}\right),{\displaystyle \sum}_{q=1}^{n}\left(1-{\rho}_{q2}\right),\dots ,{\displaystyle \sum}_{q=1}^{n}\left(1-{\rho}_{qm}\right).$$

- (5)
- Calculate objective weights. The comprehensive coefficient is a coefficient that directly reflects the amount of information contained in the factor, thus determining the weight of the factor. The formula for calculating the comprehensive coefficient h
_{j}is as follows:$${h}_{j}={v}_{j}{\displaystyle \sum}_{q=1}^{n}\left(1-{\rho}_{qm}\right),$$_{j}is the comprehensive coefficient of the j-th factor.

_{j}is the weight of the j-th factor.

#### 3.2.3. Calculation of Final Factor Weights Using Game Theory

_{1}, a

_{2}, …, a

_{N}) are normalized to obtain the optimal weight coefficient, and the final weight is determined as follows:

#### 3.3. Improved GRA-TOPSIS Evaluation Method

#### 3.3.1. Traditional TOPSIS Method

- (1)
- Build a multi-attribute decision matrix. Assuming that there are m alternatives for selection and n evaluation factors are selected, the multi-attribute decision matrix A can be constructed as$$A=\left[\begin{array}{cccc}{x}_{11}& {x}_{12}& \cdots & {x}_{1n}\\ {x}_{21}& {x}_{22}& \cdots & {x}_{2n}\\ \vdots & \vdots & \vdots & \vdots \\ {x}_{m1}& {x}_{m2}& \cdots & {x}_{mn}\end{array}\right]$$

- (2)
- Normalize the decision matrix. Considering the difference in dimensions between the factors, the factors need to be normalized, whereby the benefit factor is normalized using Equation (15), and the cost factor is normalized using Equation (16).$${x}_{ij}=\frac{{x}_{ij}-min{x}_{ij}}{max{x}_{ij}-min{x}_{ij}}$$$${x}_{ij}=\frac{max{x}_{ij}-{x}_{ij}}{max{x}_{ij}-min{x}_{ij}}.$$

- (3)
- Build a weighted standardized decision matrix. The weighted standardized decision matrix is developed according to the standardized matrix and the factor weights. The calculation formula is as follows:$$C=\left[\begin{array}{cccc}w11x11& w12x12& \cdots & w1nx1n\\ w21x21& w22x22& \cdots & w2nx2n\\ \vdots & \vdots & \vdots & \vdots \\ wm1xm1& wm2xm2& \cdots & wmnxmn\end{array}\right]$$

- (4)
- Calculate the closeness of the object. The ideal solution is determined as follows:$$\{\begin{array}{c}{C}^{+}=\left\{\left(\underset{i}{max}{x}_{ij}|x\in {X}_{1}\right),\left(\underset{i}{min}{c}_{ij}|x\in {X}_{2}\right)\right\}\\ {C}^{-}=\left\{\left(\underset{i}{min}{x}_{ij}|x\in {X}_{1}\right),\left(\underset{i}{max}{c}_{ij}|x\in {X}_{2}\right)\right\}\end{array},$$
^{+}represents the positive ideal solution set, and C^{−}represents the negative ideal solution set; X_{1}represents the benefit-type factor set, and X_{2}represents the cost-type factor set.

_{i}

^{+}represents the Euclidean distance between each alternative and the positive ideal solution, and d

_{i}

^{−}represent the Euclidean distance between each alternative and the negative ideal solution.

#### 3.3.2. Improved TOPSIS Method

- (1)
- Calculate weighted normalization matrix. The calculation formula of the weighted normalization matrix is shown in Equation (17). According to the calculation results, the optimal set of factors U
_{j}* can be determined, which is then used as the reference sequence for improving GRA-TOPSIS.$${U}_{j}^{*}=\left({R}_{0}^{*}\left(1\right),{R}_{0}^{*}\left(2\right),\dots ,{R}_{0}^{*}\left(m\right)\right),$$_{0}*(j) is the optimal factor value in each object to be evaluated.

- (2)
- Calculate the gray correlation coefficient s
_{ij}of each factor. After determining the gray coefficient of each factor, the gray correlation coefficient matrix S = (s_{ij})_{m}_{×n}can be obtained. The calculation formula of the gray correlation coefficient is$${s}_{ij}=\frac{\underset{i}{min}\underset{j}{min}{\Delta}_{i}\left(j\right)+\zeta \underset{i}{max}\underset{j}{max}{\Delta}_{i}\left(j\right)}{{\Delta}_{i}\left(j\right)+\zeta \underset{i}{max}\underset{j}{max}{\Delta}_{i}\left(j\right)},$$

- (3)
- Determine the positive ideal solution s
_{0}^{+}and negative ideal solution s_{0}^{−}of the matrix S. The calculation formula is as follows:$${s}_{0}^{+}=max{s}_{i}\underset{1\le i\le n}{\left(j\right)}=\left({s}_{0}^{+}\left(1\right),{s}_{0}^{+}\left(2\right),\dots ,{s}_{0}^{+}\left(m\right)\right),$$$${s}_{0}^{-}=min{s}_{i}\underset{1\le i\le n}{\left(j\right)}=\left({s}_{0}^{-}\left(1\right),{s}_{0}^{-}\left(2\right),\dots ,{s}_{0}^{-}\left(m\right)\right),$$_{i}(j) is the gray correlation coefficient of the j-th factor of the i-th object to be evaluated.

- (4)
- Calculate the Euclidean distance using Equation (25).$$\{\begin{array}{c}{d}_{i}^{+}=\sqrt{{\displaystyle {\displaystyle \sum}_{j=1}^{m}}{({s}_{i}\left(j\right)-{s}_{0}^{+}\left(j\right))}^{2}}\\ {d}_{i}^{-}=\sqrt{{\displaystyle {\displaystyle \sum}_{j=1}^{m}}{({s}_{i}\left(j\right)-{s}_{0}^{-}\left(j\right))}^{2}}\end{array}.$$

- (5)
- Calculate the relative closeness of the gray association using Equation (26).$${G}_{i}=\frac{{d}_{i}^{-}}{{d}_{i}^{+}+{d}_{i}^{-}}.$$

#### 3.4. The Realization Process of Comprehensive Evaluation Model Based on Game Theory Using GRA-TOPSIS

## 4. Model Application

_{1}–K

_{5}) were taken as the research objects, and the calculation was carried out according to the principle of the comprehensive evaluation model. Five experts were organized to score the systems according to the system construction, actual operation, and relevant system design data, combined with the scoring criteria.

#### 4.1. Data Processing

_{1}was taken as an example to illustrate the calculation principle of blind number theory. The factor scores of mine K

_{1}given by the experts are shown in Table 4.

_{11}as an example, the experts determined the scoring intervals to be (85–96), (88–95), (92–95), (90–98), and (88–94). After rearrangement, the scoring intervals without crossover were obtained as (85–88), (88–90), (90–92), (92–94), (94–95), (95–96), and (96–98). Combined with the comprehensive credibility of experts, the credibility of the rearranged scoring interval was calculated as θ

_{1}= (88 − 85)/(96 − 85) × 0.195 = 0.0532. Similarly, the credibility of other scoring intervals was θ

_{2}= 0.1638, θ

_{3}= 0.2126, θ

_{4}= 0.3426, θ

_{5}= 0.1384, θ

_{6}= 0.0437, and θ

_{7}= 0.0457. According to the reliability of the recalculated score interval, the blind number function was built as follows:

_{11}falling in four intervals was calculated. The final blind number matrix function was obtained as follows:

_{12}–X

_{34}, the final blind number matrix D was obtained as follows:

_{1}= 30, a

_{2}= 70, a

_{3}= 85, and a

_{4}= 95, and the final evaluation score of each factor was obtained. Taking factor X

_{11}as an example, according to the calculation of the blind number matrix, it can be known that the possibilities of factor X

_{11}falling into the four grading intervals were 0, 0, 0.2170, and 0.7830, respectively. Therefore, the final score of factor X

_{11}could be calculated as x

_{11}= 0 × 30 + 0 × 70 + 0.2170 × 85 + 0.675 × 95 = 92.83. The final scores of the other factors and other objects to be evaluated are shown in Table 5.

#### 4.2. Factor Weight Calculation

- (1)
- Calculate the subjective weights of factors using the G1 method.

_{2}> X

_{3}> X

_{1}, with the relative importance ratios of r

_{1}= 1.8 and r

_{2}= 1.6. According to Equations (2)–(4), we can get w

_{3}= (1 + 1.8 × 1.6 + 1.6) – 1 = 0.183, w

_{2}= 0.183 × 1.6 = 0.291, and w

_{1}= 0.291 × 1.8 = 0.526. Thus, the weights of the first-level factors were (0.183, 0.526, 0.291). In the same way, the weights of secondary factors could be obtained as W

_{1}= 0.072, 0.034, 0.021, 0.018, 0.038, 0.100, 0.043, 0.039, 0.132, 0.120, 0.029, 0.063, 0.050, 0.042, 0.104, and 0.095.

- (2)
- Calculate the objective weights of factors using the improved CRITIC method.

_{2}= 0.137, 0.074, 0.098, 0.134, 0.019, 0.112, 0.018, 0.040, 0.051, 0.083, 0.036, 0.036, 0.037, 0.043, 0.036, and 0.046.

- (3)
- Calculate the final weights of factors using game theory.

_{1}and the objective weights W

_{2}, indicating that the single weighting method was either subjective or objective, with a significant impact on the final evaluation result. Therefore, the weights were combined and optimized according to game theory to achieve balanced weight calculation results. According to Equations (11)–(13), MATLAB software was applied to calculate the combined weight coefficients, obtaining a = 0.5442 and b = 0.5988. After normalizing the combined weight system, the final combined weight coefficients were a* = 0.476 and b* = 0.524. Thus, the final factor combination weights were W = 0.106, 0.055, 0.061, 0.079, 0.028, 0.106, 0.030, 0.039, 0.090, 0.100, 0.0325, 0.049, 0.043, 0.042, 0.068, and 0.069.

#### 4.3. Comprehensive Evaluation of Truck Dispatching System in Open-Pit Mine Based on Improved GRA-TOPSIS Method

_{j}* was selected on the basis of the improved GRA-TOPSIS: 0.106, 0.055, 0.061, 0.079, 0.028, 0.106, 0.030, 0.039, 0.090, 0.100, 0.033, 0.049, 0.043, 0.042, 0.068, and 0.069. Taking U

_{j}* as the reference sequence, the pros and cons of each truck dispatching system were evaluated.

_{1}, E

_{2}, E

_{3}, E

_{4}, and E

_{5}) and the positive and negative ideal solutions were calculated: d

_{1}

^{+}= 1.087, d

_{2}

^{+}= 1.388, d

_{3}

^{+}= 1.739, d

_{4}

^{+}= 1.855, and d

_{5}

^{+}= 1.151; d

_{1}

^{−}= 1.651, d

_{2}

^{−}= 1.004, d

_{3}

^{−}= 0.838, d

_{4}

^{−}= 0.494, and d

_{5}

^{−}= 1.449. The relative closeness of the gray correlation was obtained using Equation (26): G

_{1}= 0.6031, G

_{2}= 0.4198, G

_{3}= 0.3254, G

_{4}= 0.2103, and G

_{5}= 0.5573. Accordingly, the five truck dispatching systems could be ranked from good to bad as E

_{1}> E

_{5}> E

_{2}> E

_{3}> E

_{4}.

_{1}> E

_{5}> E

_{2}> E

_{3}> E

_{4}. Using the GRA and TOPSIS evaluation models, the truck dispatching systems of the five mines were ranked in the same order. Thus, the calculation results of the three evaluation models were highly consistent, indicating that the established GRA-TOPSIS evaluation model had good adaptability in the evaluation of truck dispatching systems in open-pit mines. Using the GRA-TOPSIS, GRA, and TOPSIS methods, the extreme values were 0.3927, 0.2898, and 0.2821, respectively, while the coefficients of variation were 0.1622, 0.1189, and 0.1379, respectively. A larger extreme value and a larger coefficient of variation enable a greater dispersion degree of the comprehensive evaluation value of the truck dispatching system in the open-pit mine, improving the distinction between the pros and cons of each system. The extreme value and coefficient of variation based on the GRA-TOPSIS method were the largest, indicating its superior distribution of results and more obvious comprehensive difference between each system, enabling an intuitive analysis of the comprehensive application level of each system. When using the GRA or TOPSIS methods, the difference between the extreme values and the coefficients of variation was small, and the resolution level was not high. This can lead to ambiguous evaluation results and low accuracy. In summary, the comprehensive evaluation model of GRA-TOPSIS established in this paper has more prominent advantages and stronger adaptability in analyzing and processing the advantages and disadvantages of truck dispatching systems, reflecting this study’s value.

_{1}was the best, while that of K

_{4}was the worst. The closeness calculation result based on the GRA-TOPSIS method was located between the results of GRA and TOPSIS, indicating that the method combined the advantages of both models, taking into account the integrity and correlation of the evaluation. This verifies the rationality of the application of the GRA-TOPSIS comprehensive evaluation model.

#### 4.4. Difference Analysis of Truck Dispatching System Based on Radar Chart

## 5. Conclusions

- (1)
- In order to improve and balance the factor weight calculation, thus avoiding an invalid final evaluation result due to unreliable weight calculation, this paper adopted the G1 method, the improved CRITIC method, and game theory to calculate the subjective weights, objective weights, and final weights of the factors, resulting in a more reasonable weighting.
- (2)
- Considering that the evaluation factors of the truck dispatching system in open-pit mines are not only incompatible, but also mainly qualitative, the TOPSIS method was introduced to evaluate the system. The traditional TOPSIS method was improved using the GRA method, and a comprehensive evaluation model was established. The truck dispatching systems of five open-pit mines were taken as the study objects. In addition, blind number theory was introduced to process the expert scoring results, thereby reducing the influence of subjective factors on their reliability. The results showed that the established GRA-TOPSIS model could improve the resolution level. The model realizes the exact sorting of the overall advantages and disadvantages of the existing truck dispatching system. The model can also study the advantages and disadvantages of each truck dispatching system subsystem, and analyze the differences between the systems according to the calculation results, providing a theoretical basis for the optimization and upgrading of the truck dispatching system. Satisfactory results were obtained, verifying its applicability and providing a scientific and effective method for system evaluation.
- (3)
- In the evaluation of truck dispatching systems in open-pit mines, not only are there many influencing factors, but the relationship between these factors is also complex. Therefore, in follow-up research, it is necessary to further improve the factor system structure and optimize the factor classification standard to improve the applicability of the model. In addition, based on the comprehensive evaluation model of the TOPSIS method, the rationality of the factor quantification determines the reliability of the evaluation results. In this paper, blind number theory is used to process the factor data, and the calculation process is relatively complicated. Therefore, further optimizing the factor data quantification method is also one of the key directions of future research.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 3.**Radar diagram of each subsystem: (

**a**) route optimization; (

**b**) traffic flow planning; (

**c**) real-time dispatching.

Comprehensive evaluation factor system of truck dispatching system “O” in open-pit mine | First-Level Factor | Second-Level Factor | Factor Grading Standard | |||

Excellent | Good | Moderate | Poor | |||

Optimal route X_{1} | Transportation speed x_{11} | (90–100) | (80–90) | (60–80) | (0–60) | |

Road network node x_{12} | (90–100) | (80–90) | (60–80) | (0–60) | ||

Road quality x_{13} | (90–100) | (80–90) | (60–80) | (0–60) | ||

Road slope x_{14} | (90–100) | (80–90) | (60–80) | (0–60) | ||

Road distance x_{15} | (90–100) | (80–90) | (60–80) | (0–60) | ||

Traffic flow planning X_{2} | Road capacity constraints x_{21} | (90–100) | (80–90) | (60–80) | (0–60) | |

Shovel capacity constraints x_{22} | (90–100) | (80–90) | (60–80) | (0–60) | ||

Unloading capacity constraints x_{23} | (90–100) | (80–90) | (60–80) | (0–60) | ||

Ore grade constraints x_{24} | (90–100) | (80–90) | (60–80) | (0–60) | ||

Production Plan x_{25} | (90–100) | (80–90) | (60–80) | (0–60) | ||

Truck capacity x_{26} | (90–100) | (80–90) | (60–80) | (0–60) | ||

Traffic continuity x_{27} | (90–100) | (80–90) | (60–80) | (0–60) | ||

Real-time scheduling X_{3} | Key projects x_{31} | (90–100) | (80–90) | (60–80) | (0–60) | |

System priority x_{32} | (90–100) | (80–90) | (60–80) | (0–60) | ||

Scheduling capability x_{33} | (90–100) | (80–90) | (60–80) | (0–60) | ||

Scheduling efficiency x_{34} | (90–100) | (80–90) | (60–80) | (0–60) |

r_{j} | Relative Importance | r_{j} | Relative Importance |
---|---|---|---|

1.0 | Equally important | 1.2 | Slightly more important |

1.4 | Obviously more important | 1.6 | Significantly more important |

1.8 | Extremely more important | 1.1, 1.3, 1.5, 1.7, 1.9 | Situations between the above description |

No | Academic Rank and Professional Title | Working Years | Academic Degree | Credibility | Comprehensive Credibility |
---|---|---|---|---|---|

S_{1} | Associate professor | 6 | Doctor | 0.85 | 0.195 |

S_{2} | Professor | 7 | Doctor | 0.90 | 0.207 |

S_{3} | Senior engineer | 5 | Master | 0.85 | 0.195 |

S_{4} | Engineer | 8 | Master | 0.85 | 0.195 |

S_{5} | Senior engineer | 12 | Master | 0.90 | 0.207 |

Evaluation Factors | Expert Scoring Results | ||||
---|---|---|---|---|---|

S_{1} | S_{2} | S_{3} | S_{4} | S_{5} | |

X_{11} | (85–96) | (88–95) | (92–95) | (90–98) | (88–94) |

X_{12} | (80–85) | (82–88) | (78–88) | (75–86) | (82–89) |

X_{13} | (70–78) | (72–77) | (75–80) | (77–82) | (75–85) |

X_{14} | (70–75) | (70–78) | (72–75) | (70–80) | (68–73) |

X_{15} | (81–88) | (85–90) | (88–95) | (75–88) | (84–90) |

X_{21} | (75–78) | (75–80) | (80–82) | (77–83) | (75–88) |

X_{22} | (80–85) | (85–92) | (85–95) | (78–83) | (85–98) |

X_{23} | (81–84) | (78–85) | (75–89) | (73–86) | (85–92) |

X_{24} | (82–94) | (85–93) | (90–94) | (88–92) | (90–96) |

X_{25} | (85–95) | (88–95) | (92–98) | (88–95) | (91–97) |

X_{26} | (80–85) | (78–83) | (77–84) | (74–88) | (75–89) |

X_{27} | (81–85) | (85–93) | (85–96) | (78–86) | (85–95) |

X_{31} | (78–84) | (80–88) | (78–89) | (75–87) | (86–93) |

X_{32} | (80–84) | (78–88) | (77–86) | (75–88) | (75–85) |

X_{33} | (75–85) | (78–84) | (76–85) | (75–83) | (74–83) |

X_{34} | (83–87) | (85–94) | (85–95) | (78–88) | (88–93) |

Factor | Mine | ||||
---|---|---|---|---|---|

K_{1} | K_{2} | K_{3} | K_{4} | K_{5} | |

x_{11} | 92.83 | 87.68 | 78.79 | 84.56 | 75.37 |

x_{12} | 83.08 | 81.51 | 84.77 | 75.46 | 77.79 |

x_{13} | 72.72 | 75.86 | 74.96 | 79.52 | 81.38 |

x_{14} | 70.11 | 75.11 | 74.29 | 76.45 | 85.46 |

x_{15} | 85.04 | 83.96 | 81.57 | 80.45 | 83.28 |

x_{21} | 76.3 | 74.12 | 78.28 | 81.36 | 85.74 |

x_{22} | 84.89 | 83.15 | 81.29 | 80.75 | 84.39 |

x_{23} | 83.25 | 81.59 | 83.19 | 78.56 | 79.37 |

x_{24} | 91.69 | 85.37 | 81.39 | 78.54 | 88.25 |

x_{25} | 92.11 | 87.46 | 75.49 | 74.26 | 86.37 |

x_{26} | 80.76 | 78.56 | 74.39 | 75.78 | 81.34 |

x_{27} | 86.18 | 81.58 | 79.48 | 82.57 | 84.88 |

x_{31} | 83.45 | 78.52 | 76.31 | 75.73 | 78.79 |

x_{32} | 81.21 | 80.15 | 84.13 | 78.15 | 82.39 |

x_{33} | 80.99 | 81.45 | 78.59 | 74.63 | 82.34 |

x_{34} | 87.08 | 88.14 | 81.75 | 78.59 | 84.51 |

Mine | GRA-TOPSIS | GRA | TOPSIS | |||
---|---|---|---|---|---|---|

Final Results | Sequence | Final Results | Sequence | Final Results | Sequence | |

K_{1} | 0.6031 | 1 | 0.8408 | 1 | 0.6191 | 1 |

K_{2} | 0.4198 | 3 | 0.6927 | 3 | 0.5189 | 3 |

K_{3} | 0.3254 | 4 | 0.6168 | 4 | 0.3464 | 4 |

K_{4} | 0.2103 | 5 | 0.5511 | 5 | 0.3368 | 5 |

K_{5} | 0.5573 | 2 | 0.7875 | 2 | 0.6108 | 2 |

Extreme value | 0.3927 | 0.2898 | 0.2821 | |||

Coefficient of variation | 0.1622 | 0.1189 | 0.1379 |

Name of Mine | G_{i} (Total Closeness) | Sequence | Criterion Layer Closeness | |||||
---|---|---|---|---|---|---|---|---|

Route Optimization | Sequence | Traffic Flow Planning | Sequence | Real-Time Dispatching | Sequence | |||

K_{1} | 0.6031 | 1 | 0.5589 | 1 | 0.6621 | 1 | 0.6380 | 1 |

K_{2} | 0.4198 | 3 | 0.5096 | 3 | 0.3332 | 3 | 0.5596 | 2 |

K_{3} | 0.3254 | 4 | 0.4441 | 5 | 0.2602 | 4 | 0.3872 | 4 |

K_{4} | 0.2103 | 5 | 0.4671 | 4 | 0.1862 | 5 | 0.2575 | 5 |

K_{5} | 0.5573 | 2 | 0.5197 | 2 | 0.6059 | 2 | 0.5378 | 3 |

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## Share and Cite

**MDPI and ACS Style**

Kou, X.; Xie, X.; Zou, Y.; Kang, Q.; Liu, Q.
Research on Comprehensive Evaluation Model of a Truck Dispatching System in Open-Pit Mine. *Sustainability* **2022**, *14*, 9062.
https://doi.org/10.3390/su14159062

**AMA Style**

Kou X, Xie X, Zou Y, Kang Q, Liu Q.
Research on Comprehensive Evaluation Model of a Truck Dispatching System in Open-Pit Mine. *Sustainability*. 2022; 14(15):9062.
https://doi.org/10.3390/su14159062

**Chicago/Turabian Style**

Kou, Xiangyu, Xuebin Xie, Yi Zou, Qian Kang, and Qi Liu.
2022. "Research on Comprehensive Evaluation Model of a Truck Dispatching System in Open-Pit Mine" *Sustainability* 14, no. 15: 9062.
https://doi.org/10.3390/su14159062