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In view of the incompleteness of the methods used in the location selection of today’s shared car charging stations, the author proposes to combine the two methods of AHP and fuzzy evaluation to construct an evaluation index system and a fuzzy level evaluation model. The two alternative electric vehicle charging station addresses are comprehensively evaluated to achieve the most preferred address. Pr actice has proved that this method has a high degree of accuracy.

The fuzzy comprehensive evaluation method is a general judgment that can reasonably synthesize these attributes or factors for things with multiple attributes or their overall advantages and disadvantages affected by many factors. The fuzzy comprehensive evaluation model generally consists of six elements: the evaluation object set, the evaluation object impact factor set, the importance index of each factor, the evaluation level set of the influence factor, the fuzzy evaluation matrix, and the comprehensive evaluation result. The location of the car-sharing site is affected by a variety of qualitative and quantitative factors, and the six elements can be analyzed by the fuzzy comprehensive evaluation method.

1) Analysis of evaluation factors and establishment of indicator system. Demand is the first consideration for the development of car sharing, and it is also a factor to consider when site selection [

For shared sites, vehicle costs, land costs, management costs, and technology costs are not small and need to be carefully considered. This paper attributes these factors to the impact of the economic side on site selection.

Coordination is also a very important factor. In the selection of sites, we must fully consider the coordination with other public transportation, so that the sharing of cars can be implemented smoothly. In addition, the construction of shared car sites involves the use of land and the supporting facilities of the surrounding facilities [

Finally, car sharing sites must also consider their social benefits, mainly from three aspects: the satisfaction of consumer travel needs, the degree of mitigation of traffic congestion, and the impact on the natural environment.

2) Establish an indicator weight set.

a) Construct a judgment matrix. The Analytic Hierarchy Process (AHP) [

Let the judgment matrix A = (aij) (i, j = 1, 2, ..., n), where aij represents the relative importance of the i element to the j element, and aij > 0,, aij = 1/aji, aii = 1. This paper compares the different indicators using the 1 - 9 scale method proposed by American operations researcher Professor T.L. Saaty. Judging the comparison values of the elements in the matrix, generally 1, 3, 5, 7, 9 respectively indicate that i is equally important, slightly important, significant, strong, and extremely important relative to the j element, 2, 4, 6, 8 The median value of the adjacent judgment.

b) Calculation of indicator weights. Each column of the judgment matrix A is normalized, and the sum of each line after normalization is obtained [

c) Consistency test. Since the judgment matrix is derived from the importance of the two elements, there is a certain error. To ensure the reliability of the calculation results, a consistency check is required. The test formula is: CR = CI/RI. Where CI = (λ_{max} − n)/(n − I), λ_{max} is the maximum eigenvalue of the judgment matrix A, n is the dimension of the judgment matrix, CR is the consistency ratio, and RI is the random consistency index (see

If CR < 0.1, then the judgment matrix satisfies the consistency test; otherwise, the judgment matrix needs to be adjusted.

d) Sharing the weight calculation results of each indicator of the location of the car site [

n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
---|---|---|---|---|---|---|---|---|---|---|---|

RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.5 | 1.19 | 1.51 |

0 | C1 | C2 | C3 | C4 | Index weight | Consistency test |
---|---|---|---|---|---|---|

C1 | 1 | 1 | 1 | 1 | 0.250 | λ_{max}=4 |

C2 | 1 | 1 | 1 | 1 | 0.250 | CI=0 |

C3 | 1 | 1 | 1 | 1 | 0.250 | CR=0 |

C4 | 1 | 1 | 1 | 1 | 0.250 | Satisfy consistency |

C1 | D1 | D2 | Indicator weight WC1 | Consistency test |
---|---|---|---|---|

D1 | 1 | 2 | 0.667 | λ_{max} = 2 |

D2 | 1/2 | 1 | 0.333 | CI = 0, CR = 0 Meets consistency |

C2 | D3 | D4 | D5 | D6 | Indicator weight WC2 | Consistency test |
---|---|---|---|---|---|---|

D3 | 1 | 1 | 1/2 | 1/3 | 0.141 | λ_{max} = 4.01 |

D4 | 1 | 1 | 1/2 | 1/3 | 0.141 | RI = 0.9 |

D5 | 2 | 2 | 1 | 1/2 | 0.263 | CR = 0.023 < 0.1 |

D6 | 3 | 3 | 2 | 1 | 0.455 | Satisfy consistency |

C3 | D7 | D8 | D9 | Indicator weight WC3 | Consistency test |
---|---|---|---|---|---|

D7 | 1 | 2 | 3 | 0.540 | λ_{max} = 3.009 |

D8 | 1/2 | 1 | 2 | 0.297 | CI = (λ_{max} − n)/(n − 1) = 0.046 |

D9 | 1/3 | 1/2 | 1 | 0.163 | CR = 0.027 < 0.1 Meets consistency |

D2 | E4 | E5 | E6 | Indicator weight WD2 | Consistency test |
---|---|---|---|---|---|

E4 | 1 | 2 | 3 | 0.528 | λ_{max} = 3.054 |

E5 1 3 | 1/2 | 1 | 3 | 0.332 | RI = 0.58 |

E6 | 1/3 | 1/3 | 1 | 0.140 | CR = 0.0155 < 0.1 Satisfy consistency |

The evaluation level set is a set of various evaluation results that the judges may make for the evaluation factors, V = {v1, v2, ∙∙∙, vm}, V represents the evaluation set, and vi represents the evaluation index. The evaluation level established in this paper is V = {v1, v2,..., vm}, v = It is very suitable for building a site, suitable for building a site, generally suitable for building a site, less suitable for building a site [

After constructing the evaluation level set V, it is necessary to quantify the evaluated things one by one from the single factor ui (i = 1, 2, ∙∙∙, n), that is, to determine the graded subset vi of the evaluated things from the single factor ui, the membership degree, and then the fuzzy evaluation matrix R. The element rij in the matrix R represents the degree of membership of vj in a given thing from the factor vi. The performance of an evaluated thing in a certain factor vi is described by the fuzzy vector (R/ui) = (ri1, ri2, ∙∙∙, rim).

Due to the complexity of the location of the car sharing site and the numerous factors involved in the evaluation, in order to make the results scientific and reasonable, it is necessary to fully consider the influence of various factors and comprehensively reflect the information of the single factor evaluation. This requires that all the influencing factors should be balanced according to the weight of weights [

Taking the location evaluation point P of an urban car sharing site as an example, the comprehensive evaluation is carried out by combining AHP and fuzzy comprehensive evaluation method, and the frequency of each factor corresponding to each evaluation level is obtained by the method of summarizing the results of the experts, and normalized [

R D 1 = ( 0.2 0.6 0.2 0 0 0.3 0.6 0.1 0 0 0.1 0.5 0.3 0.1 0 )

The evaluation matrix for the three factors that influence consumers’ choice of car sharing is:

R D 2 = ( 0.2 0.5 0.2 0.1 0 0.3 0.6 0.1 0 0 0.1 0.4 0.4 0.1 0 )

B D 1 = W D 1 R D 1 = ( 0.20000 0.5750 0.2000 0.0250 0 )

B D 2 = W D 2 R D 2 = ( 0.2192 0.5192 0.1948 0.0668 0 )

After the synthesis operation, a comprehensive evaluation decision matrix is obtained:

R = ( 0.206 0.556 0.198 0.038 0 0.073 0.331 0.309 0.200 0.054 0.146 0.429 0.270 0.100 0.054 0.163 0.400 0.225 0.172 0.036 )

The weight vector of the comprehensive evaluation W = W0 = (0.250, 0.250, 0.250, 0.250, 0.25) Therefore, the fuzzy evaluation result of the suitability of establishing a shared car site at site P is: B = W・R = (0.1475 0.4294 0.2509 0.1283 0.0440). Each element in B indicates the appropriate level of this shared car site corresponds to the degree of membership of each level in the evaluation set. According to the principle of maximum membership, the second item has the largest index, indicating that the appropriate level for site P to establish a shared car is “fit”. The evaluation result obtained by the multi-level fuzzy comprehensive evaluation is a fuzzy vector B = (b1, b2, ∙∙∙, bn), that is, the membership vector of the evaluation object belonging to each evaluation level. When comparing and sorting multiple evaluation objects, it is necessary to calculate the comprehensive score of each evaluation object, and the defuzzification can be performed by the hierarchical parameter method [

There are many factors involved in the location of shared car sites, and the levels are also complex. This paper analyzes the influencing factors of the location of shared car sites, uses the analytic hierarchy process to establish an index evaluation system for the location of shared car sites, and determines the weights of each index, combined with fuzzy. The comprehensive evaluation model calculates the suitability membership vector B of the shared vehicle alternative site P, and uses the inverse fuzzy method to obtain the comprehensive score of the candidate site P, indicating that the multiple scores of each site can be selected according to the comprehensive score of each site, to choose the best. This paper verifies the practicability and feasibility of the method by using examples. It is believed that it can provide a more scientific and feasible method for the relevant decision-making departments to plan and locate the site at the car sharing site.

The authors declare no conflicts of interest regarding the publication of this paper.

Zhang, R.X. and Zhao, B. (2018) Research on Location Selection of Shared Vehicle Charging Station Based on Analytic Hierarchy Process and Fuzzy Evaluation Method. Open Access Library Journal, 5: e4899. https://doi.org/10.4236/oalib.1104899