Delivery intensity evaluation method based on entropy weight-TOPSIS method

Abstract

The invention provides a delivery intensity evaluation method based on an entropy weight-TOPSIS method. A deliveryman labor intensity evaluation system is established in six aspects of delivery duration, the number of delivery households, delivery mileage, the number of delivery boxes, a rural power grid customer proportion and a photographing signing proportion. Determining the value of the comprehensive evaluation index according to the actual delivery business index data; the weight of each evaluation index is determined by adopting an entropy weight method, and a multi-index weight basis is provided for comprehensive evaluation of the delivery intensity. According to a TOPSIS algorithm, a delivery strength comprehensive evaluation model is established, an optimal ideal solution and a worst ideal solution are calculated, then the distance from an index evaluation value vector to the ideal solution is calculated, and finally the relative closeness degree between the index evaluation value vector of each deliveryman and a negative ideal solution is calculated, and the relative closeness degree is used as a delivery strength comprehensive evaluation value. The method comprehensively considers the business actions and data in the actual delivery process, not only pays attention to the actual business data, but also considers other implicit indexes, and provides a scientific basis for comprehensively evaluating the labor intensity of the deliveryman.

Description

Technical Field

[0001] The present invention relates to the technical field of delivery strength evaluation of deliverymen, and in particular to a delivery strength evaluation method based on entropy weight-TOPSIS method. Background Art

[0002] With the rapid development of the modern logistics industry, logistics companies have gradually increased their attention to resource allocation. How to truly improve the quality, reduce costs and increase efficiency of logistics operations has become a common research goal for most logistics companies. For example, in the field of tobacco commercial logistics, with the continuous growth of business volume, the labor intensity assessment of delivery personnel has become the key to improving logistics efficiency and service quality. Traditional evaluation methods are often based on a single indicator, which makes it difficult to fully reflect the labor intensity of delivery personnel. Especially in the context of large differences in delivery mileage, uneven number of service customers, coexistence of urban and rural network customers, and diverse signing methods, the complexity of the evaluation is further increased, which also brings blindness to the optimal allocation of transportation resources and lacks scientific reference basis. There is an urgent need for a scientific and comprehensive delivery labor intensity assessment method to provide a theoretical basis for the optimization of transportation resources and the dispatch of delivery personnel.

[0003] As a comprehensive evaluation method that combines information entropy theory with the superior-inferior solution distance method, the entropy weight-TOPSIS method can objectively determine the weights of each evaluation index, effectively evaluate the labor intensity of deliverymen, and provide strong support for optimizing delivery routes and improving delivery efficiency. Therefore, it is particularly important to invent a delivery intensity evaluation method based on the entropy weight-TOPSIS method. Summary of the invention

[0004] The purpose of the present invention is to provide a delivery intensity evaluation method based on the entropy weight-TOPSIS method, which quantifies the labor intensity of deliverymen and ranks them by intensity, providing an important reference for research and application in the aspects of task allocation, personnel management, and performance calculation of delivery work.

[0005] According to the purpose of the present invention, the present invention provides a delivery intensity evaluation method based on entropy weight-TOPSIS method, comprising the following steps:

[0006] 1) Determine the type of evaluation indicator

[0007] Select different business indicator data in the delivery work as evaluation indicators, and determine the data type of each evaluation indicator, wherein the data types include: extremely large data type and extremely small data type;

[0008] 2) Positive standardization of evaluation indicators

[0009] Evaluation indicators are divided into positive indicators and negative indicators, among which extremely large data types are positive indicators and extremely small data types are negative indicators;

[0010] The delivery work data of multiple deliverymen are used as evaluation indicators to construct an evaluation decision matrix, the negative indicators in the decision matrix are converted into positive indicators, and all indicators are standardized;

[0011] 3) Calculate the probability matrix and information entropy

[0012] According to the standardized evaluation index data, the weight of the indicator value of a deliveryman under each indicator is calculated to obtain the probability matrix, and the information entropy and difference coefficient of each evaluation index are calculated to finally obtain the entropy weight of each indicator;

[0013] 4) Establish a TOPSIS evaluation model based on entropy weight

[0014] Using the entropy weights and the standardized matrix, a weighted normalized decision matrix is ​​constructed, and the optimal ideal solution and the worst ideal solution are calculated, and then the distance from the index evaluation value vector to the ideal solution is calculated. Finally, the relative closeness of the index evaluation value vector of each deliveryman to the negative ideal solution is calculated as the comprehensive evaluation value of the delivery intensity.

[0015] 5) Delivery intensity ranking

[0016] The labor intensity of each deliveryman is ranked according to the size of the comprehensive evaluation value. The larger the comprehensive evaluation value, the greater the labor intensity, and vice versa.

[0017] Furthermore, in step 1), the indicators for evaluating the deliveryman's labor intensity are: delivery time, number of households delivered, delivery mileage, number of delivery boxes, proportion of rural network customers, and proportion of photo signing.

[0018] Furthermore, in step 1), customers are divided into urban network customers and rural network customers. Urban network customers are close to the logistics center and are relatively concentrated, and the delivery difficulty is low, which can be regarded as an extremely small indicator. However, the present invention uses the proportion of rural network customers as an evaluation indicator, so all indicators are extremely large indicators.

[0019] Furthermore, in step 2), there are m delivery personnel and n business data indicators (i.e., evaluation indicators), then the delivery intensity evaluation set is G = (G1, G2, ..., G m ), the evaluation index set is B=(B1,B2,…,B m ),G i To B j The value of x ij (i=1,2,…,m;j=1,2,…,n), then the evaluation decision matrix is ​​as follows:

[0020]

[0021] Furthermore, in step 2), the negative indicators in the evaluation decision matrix are converted into positive indicators, and all indicators are normalized as follows:

[0022] For positive indicators:

[0023] For contrarian indicators:

[0024] In the formula, Where α=0.999.

[0025] Furthermore, in step 3), the weight of the indicator value of a deliveryman under each indicator is calculated and is shown as follows:

[0026]

[0027] In the formula, p ij is the proportion of this indicator of the i-th deliveryman under the j-th indicator.

[0028] Furthermore, in step 3), the information entropy of each indicator is calculated:

[0029]

[0030] In the formula, e j is the evaluation index information entropy,

[0031] Furthermore, in step 3), the coefficient of difference of each indicator is calculated:

[0032] h j =1-e j .

[0033] Furthermore, in step 3), the entropy weight of each indicator is calculated:

[0034]

[0035] in,

[0036] Furthermore, in step 4), a weighted normalized decision matrix is ​​constructed:

[0037] R=(r ij ) m×n =(w j y ij ) m×n ;

[0038] Further, in step 4), the optimal ideal solution S is calculated + and the worst ideal solution S - :

[0039]

[0040] Furthermore, in step 4), the distance from the index evaluation value vector to the ideal solution is calculated:

[0041] Calculate the evaluation value vector of each deliveryman to the positive ideal solution S + Distance and to the negative ideal solution S - Distance

[0042]

[0043] Generally, q=1 is taken as the Hamming distance, and q=2 is taken as the Euclidean distance. In the present invention, q=2 is taken.

[0044] Furthermore, in step 4), the relative closeness between the indicator evaluation value vector of each deliveryman and the negative ideal solution is calculated:

[0045]

[0046] In the formula, C i It is the comprehensive evaluation value of delivery intensity.

[0047] Further, in step 5), according to the comprehensive evaluation value C i The labor intensity of each deliveryman is ranked according to the size of i The larger the value, the greater the labor intensity of the deliveryman. i The smaller it is, the lower the labor intensity.

[0048] The technical solution of the present invention has innovative significance in the fields of delivery personnel scheduling, human resource management, etc., and by comprehensively considering the delivery time, number of delivery households, delivery mileage, number of delivery boxes, proportion of rural network customers and proportion of photo signature, it is possible to quantitatively evaluate the delivery labor intensity of each deliveryman, analyze the labor status of the delivery personnel, find reasonable delivery task division points, balance labor intensity, and also provide reliable management tools for performance distribution, more work more pay reward mechanism, personnel use and management, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 It is a specific implementation flow chart of the present invention;

[0051] Figure 2 It is a specific implementation effect diagram of the present invention. DETAILED DESCRIPTION

[0052] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0054] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0055] Example 1

[0056] like Figure 1 and Figure 2 As shown, the present invention provides a delivery intensity evaluation method based on entropy weight-TOPSIS method, comprising the following steps:

[0057] Step (1): Determine the delivery intensity evaluation index system

[0058] A simulation experiment was conducted based on the delivery business data of the delivery management department of a logistics center on a certain day. The delivery labor intensity of 30 deliverymen was evaluated based on six indicators, including delivery time, number of delivery households, delivery mileage, number of delivery boxes, proportion of rural network customers and proportion of photo signature.

[0059] Step (2): Determine the evaluation index data type

[0060] The six indicators of delivery time, number of households delivered, delivery mileage, number of delivery boxes, proportion of rural power grid customers and proportion of photo-taking and signature are all maximization indicators (positive indicators).

[0061] Step (3): Provide specific numerical data of evaluation indicators

[0062] The detailed numerical data of each evaluation index are shown in Table 1. It should be noted that since the actual business data is confidential, the simulation data is obtained by transforming the actual data according to the statistical distribution characteristics of the original data, and the statistical characteristics of the original data are still retained.

[0063] Table 1 Delivery business indicator data

[0064]

[0065]

[0066] Step (4): Forward normalization of evaluation index data

[0067] Suppose there are m deliverymen and n business data indicators (i.e. evaluation indicators), then the delivery intensity evaluation set is G = (G1, G2, ..., G m ), the evaluation index set is B=(B1,B2,…,B m ),G i To B j The value of x ij (i=1,2,…,m;j=1,2,…,n), then the evaluation decision matrix is ​​as follows:

[0068]

[0069] Furthermore, the negative indicators in the evaluation decision matrix are converted into positive indicators, and all indicators are normalized as follows:

[0070] For positive indicators:

[0071] For contrarian indicators:

[0072] In the formula, Where α=0.999.

[0073] The standardized evaluation matrix can be obtained by calculating the above formula, as shown in Table 2.

[0074] Table 2 Standardized evaluation matrix

[0075]

[0076]

[0077] Step (5): Calculate the index entropy weight W and weighted matrix R

[0078] Calculate the weight of each indicator for a deliveryman, and show it as follows:

[0079]

[0080] In the formula, p ij is the proportion of the indicator of the i-th deliveryman under the j-th indicator. Further, the information entropy of each indicator is calculated:

[0081]

[0082] In the formula, e j is the evaluation index information entropy,

[0083] Furthermore, the coefficient of variation of each indicator is calculated:

[0084]

[0085] Furthermore, the entropy weight of each indicator is calculated:

[0086]

[0087] in,

[0088] The information entropy and weight of the single evaluation index of labor intensity determined based on the entropy weight method are shown in the following table:

[0089] Table 3 Evaluation index information entropy and weight

[0090]

[0091] From the weights, it can be seen that the weights of the three major indicators, namely the number of delivery households, delivery mileage and the number of delivery boxes, are significantly higher than the other three, which is also consistent with the feedback on the actual delivery labor intensity.

[0092] Furthermore, a weighted normalized decision matrix is ​​constructed:

[0093] R=(r ij ) m×n =(w j y ij )m×n

[0094] The weighted normalized decision matrix R of delivery intensity evaluation determined based on the entropy weight method is shown in the following table:

[0095] Table 4 Weighted normalized decision matrix R

[0096]

[0097]

[0098] Step (6): Solve for positive and negative ideal solutions S

[0099] Calculate the optimal ideal solution S for the deliveryman + and the worst ideal solution S - :

[0100]

[0101] Step (7): Calculate the distance d from the index evaluation value vector to the ideal solution:

[0102] Calculate the evaluation value vector of each deliveryman to the positive ideal solution S + Distance and to the negative ideal solution S - Distance

[0103]

[0104] Here, q=2.

[0105] Step (8): Calculate the comprehensive evaluation value C of labor intensity and sort it:

[0106] Calculate the relative closeness of the indicator evaluation value vector of each deliveryman to the negative ideal solution:

[0107]

[0108] In the formula, C i That is the comprehensive evaluation value of delivery intensity. According to the comprehensive evaluation value C i The labor intensity of each deliveryman is ranked according to the size of i The larger the value, the greater the labor intensity of the deliveryman. i The smaller the value, the lower the labor intensity. The results are shown in Table 5.

[0109] Table 5 Comprehensive evaluation results of deliveryman's labor intensity

[0110]

[0111] The results show that the deliveryman 7 has the highest delivery labor intensity, with a score of 68.587. Further checking the business indicator data of the deliveryman, the number of households delivered (140 households), delivery mileage (112 kilometers) and number of boxes delivered (33.11 boxes) are significantly higher than most other deliverymen, and the labor intensity assessment weights of these three indicators are the highest, so his score is high, which is also in line with the actual situation. Compared with the labor intensity of the deliveryman ranked 30, the number of households delivered (120 households), delivery mileage (56 kilometers) and number of boxes delivered (22.53 boxes) are much lower than those of deliveryman 7, which further shows that the delivery intensity assessment method based on the entropy weight-TOPSIS method has a good intensity quantification effect and can be used as an application method for delivery intensity assessment.

[0112] In summary, the entropy weight-TOPSIS method is used to evaluate the labor intensity of delivery. The evaluation results are objective and accurate, which can better reflect the actual situation and provide a theoretical basis for delivery scheduling. At the same time, this method can be extended to other tasks with similar indicators.

[0113] The present invention establishes a labor intensity assessment model for deliverymen based on six aspects: delivery time, number of delivery households, delivery mileage, number of delivery boxes, proportion of rural network customers, and proportion of photo signing. First, a labor intensity assessment index system is established based on the delivery business indicator data, and negative indicators are turned positive; secondly, the entropy weight method is used to calculate the weight coefficient of each indicator to provide a weight basis for comprehensive evaluation; then, a delivery intensity comprehensive evaluation model is established based on the TOPSIS algorithm, and the distance from the indicator value vector of each deliveryman to the positive ideal solution and the negative ideal solution is calculated, and the relative closeness of the indicator evaluation value vector to the positive ideal solution is used as the comprehensive evaluation value of delivery intensity, and finally the labor intensity ranking of the deliverymen is obtained by sorting. The present invention comprehensively considers the labor manifestation in the delivery process, not only focusing on aspects such as delivery mileage and number of delivery boxes, but also taking into account potential influencing factors such as the proportion of urban and rural customers, and the difference between swiping cards and taking photos to sign. The beneficial effects produced are as follows:

[0114] The present invention quantifies the labor intensity of deliverymen and ranks them, providing an important reference for research and application in the areas of delivery task allocation, personnel management, performance calculation, etc.

[0115] The delivery intensity evaluation method based on the entropy weight-TOPSIS method of the present invention has innovative significance in the fields of delivery personnel scheduling, human resource management, etc., and through comprehensive consideration of delivery time, number of delivery households, delivery mileage, number of delivery boxes, proportion of rural network customers and proportion of photo signature, it can quantitatively evaluate the delivery labor intensity of each deliveryman, analyze the labor status of the delivery personnel, find reasonable delivery task division points, balance labor intensity, and also provide reliable management tools for performance distribution, more work more pay reward mechanism, personnel use and management, etc.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A delivery intensity evaluation method based on entropy weight-TOPSIS method, characterized in that: The following steps are involved: 1) Determine the type of evaluation indicator Select different business indicator data in the delivery work as evaluation indicators, and determine the data type of each evaluation indicator, wherein the data types include: extremely large data type and extremely small data type; 2) Positive standardization of evaluation indicators Evaluation indicators are divided into positive indicators and negative indicators, among which extremely large data types are positive indicators and extremely small data types are negative indicators; The delivery work data of multiple deliverymen are used as evaluation indicators to construct an evaluation decision matrix, the negative indicators in the decision matrix are converted into positive indicators, and all indicators are standardized; 3) Calculate the probability matrix and information entropy According to the standardized evaluation index data, the weight of the indicator value of a deliveryman under each indicator is calculated to obtain the probability matrix, and the information entropy and difference coefficient of each evaluation index are calculated to finally obtain the entropy weight of each indicator; 4) Establish a TOPSIS evaluation model based on entropy weight Using the entropy weights and the standardized matrix, a weighted normalized decision matrix is ​​constructed, and the optimal ideal solution and the worst ideal solution are calculated. Then, the distance from the index evaluation value vector to the ideal solution is calculated. Finally, the relative closeness between the index evaluation value vector of each deliveryman and the negative ideal solution is calculated, which is used as the comprehensive evaluation value of delivery intensity. 5) Delivery intensity ranking The labor intensity of each deliveryman is ranked according to the size of the comprehensive evaluation value. The larger the comprehensive evaluation value, the greater the labor intensity, and vice versa.

2. The delivery intensity evaluation method based on entropy weight-TOPSIS method according to claim 1 is characterized in that: In step 1), the indicators for evaluating the deliveryman’s labor intensity are: delivery time, number of households delivered, delivery mileage, number of boxes delivered, proportion of rural network customers, and proportion of photo signing.

3. The delivery intensity evaluation method based on entropy weight-TOPSIS method according to claim 1 is characterized in that: In step 1), customers are divided into urban network customers and rural network customers. Urban network customers are close to the logistics center and are relatively concentrated, with low delivery difficulty, and can be regarded as extremely small indicators.

4. The delivery intensity evaluation method based on entropy weight-TOPSIS method according to claim 1 is characterized in that: In step 2), there are m delivery personnel and n business data indicators, then the delivery intensity evaluation set is G = (G1, G2, ..., G m ), the evaluation index set is B=(B1,B2,…,B m ),G i To B j The value of x ij (i=1,2,…,m;j=1,2,…,n), then the evaluation decision matrix is ​​as follows: In step 2), the negative indicators in the evaluation decision matrix are converted into positive indicators, and all indicators are normalized as follows: For positive indicators: For contrarian indicators: In the formula, Where α=0.

999.

5. The delivery intensity evaluation method based on entropy weight-TOPSIS method according to claim 1 is characterized in that: In step 3), the weight of the indicator value of a deliveryman under each indicator is calculated and is shown as follows: In the formula, p ij is the proportion of this indicator of the i-th deliveryman under the j-th indicator.

6. The delivery intensity evaluation method based on entropy weight-TOPSIS method according to claim 1 is characterized in that: In step 3), the information entropy of each indicator is calculated: In the formula, e j is the evaluation index information entropy, In step 3), calculate the coefficient of difference of each indicator: h j =1-e j ; In step 3), the entropy weight of each indicator is calculated: in, 7. The delivery intensity evaluation method based on entropy weight-TOPSIS method according to claim 1 is characterized in that: In step 4), a weighted normalized decision matrix is ​​constructed: R=(r ij ) m×n =(w j y ij ) m×n ; In step 4), calculate the optimal ideal solution S + and the worst ideal solution S - :

8. The delivery intensity evaluation method based on entropy weight-TOPSIS method according to claim 1 is characterized in that: In step 4), the distance from the index evaluation value vector to the ideal solution is calculated: Calculate the evaluation value vector of each deliveryman to the positive ideal solution S + Distance and to the negative ideal solution S - Distance Generally, q=1 is taken as the Hamming distance and q=2 is taken as the Euclidean distance.

9. The delivery intensity evaluation method based on entropy weight-TOPSIS method according to claim 1 is characterized in that: In step 4), the relative closeness between the indicator evaluation value vector of each deliveryman and the negative ideal solution is calculated: In the formula, C i It is the comprehensive evaluation value of delivery intensity.

10. The delivery intensity evaluation method based on entropy weight-TOPSIS method according to claim 1 is characterized in that: In step 5), according to the comprehensive evaluation value C i The labor intensity of each deliveryman is ranked according to the size of i The larger the value, the greater the labor intensity of the deliveryman. i The smaller it is, the lower the labor intensity.

Images