A method for evaluating matching degree of logistics resources and demand in a sharing mode

Abstract

The application provides a method for evaluating matching degree of logistics resources and demand in a sharing mode, and relates to the field of information technology.The method first determines the characteristics of logistics resources according to the key resources of each link of express logistics and the resources required by logistics tasks, then classifies the resource characteristics according to the nature, divides them into fuzzy characteristics and containing characteristics, determines the fuzzy characteristic boundary by using normal distribution, then calculates the combination value of different logistics resource characteristics by using Euclidean distance, Manhattan distance and Chebyshev distance, and puts different characteristics into the same measure, and finally comprehensively evaluates the matching of shared logistics resources and demand by using function matching degree, capacity matching degree and transaction matching degree.The method realizes the integration of different logistics links and different function parameters, can evaluate the whole logistics process or a certain link in the logistics process, realizes comprehensive analysis of the matching of logistics resource supply and demand, and is beneficial to improving the operation efficiency of the logistics network.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a method for evaluating the matching degree of logistics resources and demands in a sharing mode. BACKGROUND

[0002] The express logistics industry is an important industry supporting the development of modern economy and society, and logistics resource sharing is an important way to promote the transformation of the circulation mode and promote consumption upgrading. In the sharing of express logistics resources, one of the problems that has attracted much attention is how to effectively and fully utilize these logistics resources to meet the appropriate amount of logistics tasks and maximize the utilization of shared resources. Some current logistics resource sharing modes take a rough management approach to the matching of resource supply and demand. If the demand is less than the supply, then normal distribution is carried out, and it is difficult to fully utilize the remaining resources. If the demand is greater than the supply, then the distribution speed is slowed down, and the express is temporarily stored for a few days to be slowly digested. This management mode leads to several problems: first, the utilization rate of logistics resources is not high, and logistics resources are largely idle when the demand is low; second, it affects the efficiency of express flow, and insufficient resources in a certain link will delay the delivery time of the express, and it is not possible to take remedial measures in time.

[0003] Currently, express logistics resource sharing needs to be managed in a more detailed manner to improve sharing efficiency, and the matching degree evaluation of logistics resources and demands is a powerful method to improve sharing efficiency. The matching degree evaluation of logistics resources and demands can reflect the use of logistics resources in real time. If the matching degree is high, it means that the logistics resources are fully utilized and no additional operations are needed to adjust; if the matching degree is low, it means that the resource demand needs to be adjusted, and the business pressure of a certain link can be appropriately increased or reduced, thereby improving the operation efficiency of the entire logistics network and reducing problems such as express flow stagnation and express loss caused by excessive business pressure in a certain link. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a method for evaluating the matching degree of logistics resources and demands in a sharing mode to solve the above-mentioned problems of the prior art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present application is: a method for evaluating the matching degree of logistics resources and demands in a sharing mode, comprising the following steps:

[0006] Step 1, determining the characteristics of logistics resources according to the key resources of each link of express logistics and the resources required by logistics tasks;

[0007] The logistics links include a warehouse link, a transportation link, a network entry warehouse link and a distribution link; wherein the warehouse link includes sorting time consumption, available space, sorting cost, the transportation link includes transportation cost, transportation time consumption, transportation space, loading quality and transportation destination, the network entry warehouse link includes sorting time consumption, available space and sorting cost, and the distribution link includes distribution cost, distribution time consumption, vehicle space, loading quality and distribution destination;

[0008] Step 2, the logistics resource features are divided into fuzzy features and contained features according to the nature, and the boundary value of each logistics resource feature is determined, then the distance between each logistics resource feature and the boundary value is calculated to determine whether the single logistics resource feature meets the limit of the corresponding boundary value;

[0009] The contained feature is that the accurate boundary value thereof can be calculated in real time according to the existing logistics data, and the fuzzy feature is that the boundary value thereof is estimated according to the historical logistics record data;

[0010] For each fuzzy feature x, the historical mean μ and the historical standard deviation σ of the fuzzy feature are determined according to the historical logistics order data, the normal distribution formula is substituted to obtain the probability distribution function f(x) of the fuzzy feature value, and then the range boundary where the fuzzy feature should be located is determined according to the probability requirement of the fuzzy feature;

[0011]

[0012] In the formula, e represents a natural number e, and π represents a circular constant;

[0013] Step 3, the correlation relationship and feature combination mode of different logistics resource features are determined, and the two logistics resource features are combined into one dimension;

[0014] (1) The correlation relationship between all logistics resource features is calculated;

[0015] Two logistics resource feature data are extracted from the historical logistics order data, and then the Pearson correlation coefficient r of the two logistics resource features is calculated, and the calculation method is as follows:

[0016]

[0017] Wherein, r is the Pearson correlation coefficient of the two logistics resource features, n is the total data amount of the logistics order data, is the historical average value of the first logistics resource feature, is the historical average value of the second logistics resource feature, x i is the i-th data of the first logistics resource feature, y iThe i th data of the second logistics resource feature;

[0018] (2) According to the correlation coefficient between two logistics resource features, the correlation of the logistics resource features is determined, and then the combination mode of the features is determined;

[0019] When |r|≤0.3, it is determined that there is no correlation between the two logistics resource features, and the Manhattan distance is used to calculate the combination of the two logistics resource features, as shown in the following formula:

[0020]

[0021] Wherein, ρ m represents the Manhattan distance of the two logistics resource features;

[0022] When 0.3<|r|<0.7, it is determined that there is an ordinary correlation between the two logistics resource features, and the Euclidean distance is used to calculate the combination of the two logistics resource features, as shown in the following formula:

[0023]

[0024] Wherein, ρ o represents the Euclidean distance of the two logistics resource features;

[0025] When |r|≥0.7, it is determined that there is a high correlation between the two logistics resource features, and the Chebyshev distance is used to calculate the combination of the two logistics resource features, as shown in the following formula:

[0026] ρ b = max({|x1-y1|,…,|x n -y n |})

[0027] Wherein, ρ b represents the Chebyshev distance of the two logistics resource features;

[0028] Step 4, according to the characteristic value and boundary value of the logistics resource feature and the combination of the logistics resource feature, the function matching degree, the ability matching degree and the transaction matching degree between the logistics resource and the demand are determined, and then the comprehensive matching evaluation of the logistics resource and the demand is realized;

[0029] The function matching degree is used to determine whether the logistics resource meets the basic demand of the logistics task; if the value of the function matching degree is equal to 0, the current shared logistics resource cannot meet the basic express delivery; the index used for function matching degree calculation is called function satisfaction index, which is calculated in the following way:

[0030] v1=(d 1,1 >0)&(d 1,2 >0)&…&(d1,j >0)

[0031] Wherein, v1 represents the function meeting index, d represents the distance between the logistics resource characteristic value and the boundary value, j represents the total number of characteristics used for function matching degree calculation, d 1，j represents the distance between the jth logistics resource characteristic value and the boundary value used for function matching degree calculation.

[0032] The capability matching degree reflects the current logistics resource bearing capacity for demand; the index used for capability matching degree calculation is referred to as resource utilization index, and the calculation mode is shown in the following formula:

[0033] v2 = |d 2，1 | + |d 2，2 | + … + |d 2，k |

[0034] Wherein, v2 represents the resource utilization index, and k represents the total number of characteristics used for capability matching degree calculation.

[0035] The transaction matching degree: used for describing the control ability of logistics transaction cost; the index used for transaction matching degree calculation is referred to as cost optimization index, and the calculation mode is shown in the following formula:

[0036] v3 = max (d 3，1， d 3，2 , …, d 3，m )

[0037] Wherein, v3 represents the cost optimization index, and m represents the total number of characteristics used for transaction matching degree calculation.

[0038] Finally, the comprehensive logistics resource and demand matching degree evaluation (v1, v2, v3) is formed.

[0039] The beneficial effects generated by the above technical scheme are that: the logistics resource and demand matching degree evaluation method provided by the application starts from the whole link of express logistics, deeply analyzes four key links of warehouse, transfer, transportation and distribution, realizes the integration of different logistics links and different function parameters, so that the method can evaluate the whole logistics process and also can evaluate a certain link in the logistics process. In addition, the evaluation method also performs comprehensive matching degree evaluation from three dimensions of function matching degree, capability matching degree and transaction matching degree, can comprehensively analyze the matching situation of logistics resource supply and demand, improve the operation efficiency of the logistics network, and provide good support for express logistics resource sharing. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 The flowchart of the logistics resource and demand matching degree evaluation method in the sharing mode provided by the embodiment of the application;

[0041] Figure 2 a logistics resource feature map of each link in an express logistics process provided for an embodiment of the present application;

[0042] Figure 3 a schematic diagram of a classification manner of each feature and a processing method of a fuzzy feature provided for an embodiment of the present application;

[0043] Figure 4 a schematic diagram of a comprehensive matching degree evaluation composed of functions, capabilities and transaction matching degrees provided for an embodiment of the present application. DETAILED DESCRIPTION

[0044] The specific embodiments of the present application will be further described in detail below in combination with the drawings and embodiments. The following embodiments are used to illustrate the present application, but not to limit the scope of the present application.

[0045] This embodiment takes a logistics company as an example, and uses the matching degree evaluation method of logistics resource and demand in a sharing mode of the present application to evaluate the matching degree of logistics resource and demand of the company.

[0046] In this embodiment, a matching degree evaluation method of logistics resource and demand in a sharing mode, as shown in Figure 1 , includes the following steps:

[0047] Step 1, determining logistics resource features according to key resources of each link in express logistics and resources required by logistics tasks;

[0048] The links include a warehouse link, a transportation link, a network warehouse link and a distribution link; wherein, the warehouse link includes features such as sorting time consumption, available space and sorting cost; the transportation link includes features such as transportation cost, transportation time consumption, transportation space, loading quality and transportation destination; the network warehouse link includes features such as sorting time consumption, available space and sorting cost; the distribution link includes features such as distribution cost, distribution time consumption, vehicle space, loading quality and distribution destination; more features of each link can also be added according to actual logistics conditions;

[0049] In this embodiment, resource feature analysis is performed according to existing shared logistics resources and resources required by logistics tasks of the company, and link feature analysis is as shown in Figure 2 , and detailed description of each feature is as follows:

[0050] (1) Warehouse link

[0051] Sorting time consumption T s : represents sorting time of express in warehouse process, which is calculated from warehouse entry to warehouse exit.

[0052] Available space V s: represents the remaining space available for the item to be stored when the warehouse accepts the package.

[0053] Sorting cost C s : represents the actual monetary cost incurred by the warehouse to sort the package according to the shipping demand. In cases where the cost of a single package is not easily calculated, the average cost of multiple similar packages can be used as a substitute.

[0054] (2) Transportation link

[0055] Transportation cost C t : represents the actual monetary cost incurred by the transportation vehicle to transport the package from the origin to the destination. In cases where the cost of a single package is not easily calculated, the average cost of multiple similar packages can be used as a substitute.

[0056] Transportation time T t : represents the time required for the package to be transported, starting from the moment the transportation vehicle departs and ending when it arrives at the package's destination.

[0057] Transportation space V t : represents the remaining space available for the package to be stored when the transportation vehicle accepts the package.

[0058] Loading mass W t : represents the remaining loading mass available for the package to be stored when the transportation vehicle accepts the package.

[0059] Transportation destination D t : represents the final destination of the package's current transportation, which is determined by the logistics company's existing algorithm at the time of package dispatch.

[0060] (3) Warehouse entry link

[0061] Sorting time T o : represents the sorting time of the package during the warehouse entry process, starting from the moment the package enters the warehouse and ending when it exits.

[0062] Available space V o : represents the remaining space available for the item to be stored when the warehouse accepts the package.

[0063] Sorting cost C o : represents the actual monetary cost incurred by the warehouse to sort the package according to the shipping demand. In cases where the cost of a single package is not easily calculated, the average cost of multiple similar packages can be used as a substitute.

[0064] (4) Delivery link

[0065] Delivery cost C d: represents the actual monetary cost of transporting the parcel from the origin to the destination in the parcel delivery process. In the case of a single parcel cost is not easy to calculate, the average cost of multiple similar parcels can be used as a substitute.

[0066] Delivery time T d : represents the time required for the parcel in the delivery process, starting from the departure of the transport vehicle and ending when the transport vehicle arrives at the destination of the parcel.

[0067] Vehicle space V d : represents the remaining space available for the storage of the parcel when the transport vehicle is loaded.

[0068] Loading mass W d : represents the remaining loading mass available for the storage of the parcel when the transport vehicle is loaded.

[0069] Delivery destination D d : represents the final delivery destination of the parcel, which is determined when the parcel is sent out.

[0070] Step 2, according to the nature, the logistics resource characteristics are divided into fuzzy characteristics and containing characteristics, and the boundary value of each logistics resource characteristic is determined, then the distance between each logistics resource characteristic and the boundary value is calculated, and it is judged whether the single logistics resource characteristic meets the limit of the corresponding boundary value;

[0071] The containing characteristics are: the accurate boundary value of the containing characteristics can be calculated in real time according to the existing logistics (vehicle, manpower, storage) data; the fuzzy characteristics are: the boundary value of the fuzzy characteristics needs to be estimated according to the historical logistics (vehicle, manpower, storage) record data;

[0072] For each fuzzy characteristic x, according to the historical logistics order data, the historical mean μ and the historical standard deviation σ of the fuzzy characteristic are determined, the normal distribution formula is substituted, the probability distribution function f(x) of the fuzzy characteristic value is obtained, and then according to the probability requirement of the fuzzy characteristic, the range boundary where the fuzzy characteristic should be located is determined;

[0073]

[0074] In the formula, e represents the natural number e, and π represents the circular constant;

[0075] In this embodiment, according to Figure 3 The logistics resource characteristics obtained in step 1 are divided into fuzzy characteristics and containing characteristics according to the characteristic nature, and the boundaries of the fuzzy characteristics are determined by using normal distribution. The results of classifying each characteristic into fuzzy characteristics and containing characteristics are as follows:

[0076] Table 1 Classification results of logistics resource characteristics

[0077]

[0078] Because the importance and assessability of each logistics resource feature have different effects on logistics tasks, the boundary value ranges of each fuzzy feature are also different, as shown in Table 2:

[0079] Table 2 Boundary value ranges of each fuzzy feature

[0080]

[0081] Step 3, determine the correlation of different logistics resource features and the combination mode of the features, and combine the two logistics resource features into one dimension;

[0082] (1) Calculate the correlation between each pair of logistics resource features;

[0083] Extract two logistics resource feature data from historical logistics order data, and then calculate the Pearson correlation coefficient r of the two logistics resource features, as follows:

[0084]

[0085] Wherein, r is the Pearson correlation coefficient of the two logistics resource features, n is the total data amount of the logistics order data, is the historical average value of the first logistics resource feature, is the historical average value of the second logistics resource feature, x i is the i-th data of the first logistics resource feature, y i is the i-th data of the second logistics resource feature;

[0086] (2) According to the correlation coefficient between the two logistics resource features, determine the correlation of the logistics resource features, and further determine the combination mode of the features;

[0087] When |r|≤0.3, it is determined that there is no correlation between the two logistics resource features, and the Manhattan distance is used to calculate the combination of the two logistics resource features, as shown in the following formula:

[0088]

[0089] Wherein, ρ m represents the Manhattan distance of the two logistics resource features;

[0090] When 0.3<|r|<0.7, it is determined that there is an ordinary correlation between the two logistics resource features, and the Euclidean distance is used to calculate the combination of the two logistics resource features, as shown in the following formula:

[0091]

[0092] Where, ρ o Euclidean distance representing the characteristics of two logistics resources;

[0093] When |r|≥0.7, the two logistics resource characteristics are considered to be highly correlated. The Chebyshev distance is used to calculate the combination of these two logistics resource characteristics, as shown in the following formula:

[0094] ρ b =max({|x1-y1|,…,|x n -y n |})

[0095] Where, ρ b Chebyshev distance represents the distance between two logistics resource characteristics;

[0096] In this embodiment, based on the correlation between various features and the properties of Euclidean distance, Manhattan distance, and Chebyshev distance, the following combination calculation method for each pair of features can be obtained:

[0097] Table 3. Combination methods of two logistics resource characteristics

[0098] Feature combinations Distance type Time-cost Euclidean distance Time-destination Euclidean distance Time-space Manhattan distance Time-quality Manhattan distance Space-quality Manhattan distance Space-destination Manhattan distance Quality-destination Manhattan distance Cost-destination Chebyshev distance Cost-space Chebyshev distance Cost-quality Chebyshev distance

[0099] Step 4: Determine the functional matching degree, capacity matching degree, and transaction matching degree between logistics resources and demand based on the characteristic values ​​and boundary values ​​of logistics resource features, as well as the combination of logistics resource features. Figure 4 As shown, this enables a comprehensive matching and evaluation of logistics resources and demand;

[0100] The functional matching degree is used to determine whether logistics resources meet the basic requirements of logistics tasks; if the value of the functional matching degree is equal to 0, the current shared logistics resources cannot meet the most basic express delivery needs; the index used to calculate the functional matching degree is called the functional satisfaction index, and it is calculated in the following way:

[0101] v1=(d 1,1 >0)&(d 1,2 >0)&…&(d 1,j >0)

[0102] Where v1 represents the functional satisfaction index, d represents the distance between the logistics resource feature value and the boundary value, j represents the total number of features used for functional matching degree calculation, and d 1，j This represents the distance between the j-th logistics resource feature value used in the functional matching degree calculation and the boundary value;

[0103] The capability matching degree is superimposable, and reflects the capability of the current logistics resource to meet the demand; the greater the capability matching value, the more abundant the logistics resource, and the higher the demand for express logistics transportation or the more express transportation demand can be met; the index used for capability matching degree calculation is referred to as a resource utilization index, and the calculation method is shown in the following formula:

[0104] v2 = |d 2，1 | + |d 2，2 | + … + |d 2，k |

[0105] Note that the capability matching degree is calculated using the combined value of two features, wherein v2 represents the resource utilization index, and k represents the total number of features used for capability matching degree calculation;

[0106] The transaction matching degree is of the cost optimization type: used to describe the capability of cost control of logistics transaction; the higher the transaction matching degree, the stronger the cost control capability of the current express logistics, and the better the utilization rate of the resource; the index used for transaction matching degree calculation is referred to as a cost optimization index, and the calculation method is shown in the following formula:

[0107] v3 = max (d 3，1 , d 3，2 , …, d 3,m )

[0108] Note that the transaction matching degree is calculated using the combined value of two features, wherein v3 represents the cost optimization index, and m represents the total number of features used for transaction matching degree calculation;

[0109] Finally, the comprehensive logistics resource and demand matching degree (v1, v2, v3) is formed; if the value of v1 is equal to 0, then the current express logistics transportation is difficult to proceed, and more shared resources need to be allocated; if the value of v2 is close to 0, it means that appropriate resources are allocated for the current express logistics transportation, and no resource waste is caused; if the value of v3 is close to 0, it means that the cost control of the current express logistics is good, and the operation mode of the transportation line is worth promoting.

[0110] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope defined by the claims of the present application.

Claims

1. A method for evaluating the matching degree between logistics resources and demand under a shared model, characterized in that: Includes the following steps: Step 1: Determine the characteristics of logistics resources based on the key resources in each stage of express logistics and the resources required for the logistics task; Step 2: Divide the logistics resource features into two categories according to their properties: fuzzy features and inclusion features. Determine the boundary value for each type of logistics resource feature. Then calculate the distance between each logistics resource feature and the boundary value to determine whether a single logistics resource feature meets the constraint of its corresponding boundary value. The included feature is that its accurate boundary value can be calculated in real time based on existing logistics data; the fuzzy feature is that its boundary value needs to be estimated based on historical logistics record data. For each fuzzy feature x, based on historical logistics order data, determine the historical mean μ and historical standard deviation σ of the fuzzy feature, substitute them into the following normal distribution formula, obtain the probability distribution function f(x) of the fuzzy feature value, and then determine the range boundary where the fuzzy feature should be based on the probability requirements of the fuzzy feature. In the formula, e represents the natural number e, and π represents pi; Step 3: Determine the correlation and combination of different logistics resource characteristics, and combine these two logistics resource characteristics into one dimension; (1) Calculate the pairwise correlations between all logistics resource characteristics; Two logistics resource feature data points are extracted from historical logistics order data, and then the Pearson correlation coefficient r between these two logistics resource features is calculated as follows: Where r is the Pearson correlation coefficient between the two logistics resource characteristics, and n is the total amount of logistics order data. This is the historical average value of the first logistics resource characteristic. x is the historical average of the second logistics resource characteristic. i For the i-th data point of the first logistics resource feature, y i This is the i-th data point representing the second logistics resource characteristic; (2) Determine the correlation between two logistics resource characteristics based on the correlation coefficient between them, and then determine the combination of the characteristics; When |r|≤0.3, it is determined that there is no correlation between the two logistics resource features. The Manhattan distance is used to calculate the combination of the two logistics resource features, as shown in the following formula: Where, ρ m The Manhattan distance represents the characteristics of two logistics resources; When 0.3 < |r| < 0.7, it is determined that there is a common correlation between the two logistics resource characteristics. The Euclidean distance is used to calculate the combination of the two logistics resource characteristics, as shown in the following formula: Where, ρ o Euclidean distance representing the characteristics of two logistics resources; When |r|≥0.7, the two logistics resource characteristics are considered to be highly correlated. The Chebyshev distance is used to calculate the combination of these two logistics resource characteristics, as shown in the following formula: ρb=max({|x1-y1|,…,|x n -y n |}) Where, ρ b Chebyshev distance represents the distance between two logistics resource characteristics; Step 4: Determine the functional matching degree, capability matching degree, and transaction matching degree between logistics resources and demand based on the characteristic values ​​and boundary values ​​of logistics resource characteristics and the combination of logistics resource characteristics, thereby realizing a comprehensive matching evaluation of logistics resources and demand; The functional matching degree is used to determine whether logistics resources meet the basic requirements of logistics tasks; if the value of the functional matching degree is equal to 0, the current shared logistics resources cannot meet the most basic express delivery needs; the index used to calculate the functional matching degree is called the functional satisfaction index, and it is calculated in the following way: v1=(d 1,1 >0)&(d 1,2 >0)&…&(d 1,j >0) Where v1 represents the functional satisfaction index, d represents the distance between the logistics resource feature value and the boundary value, j represents the total number of features used for functional matching degree calculation, and d 1,j This represents the j-th logistics resource feature value used for calculating the functional matching degree; The capacity matching degree reflects the current ability of logistics resources to meet demand; the indicators used to calculate the capacity matching degree are called resource utilization indicators, and the calculation method is shown in the following formula: v2=|d 2,1 |+|d 2,2 |+…+|d 2,k | Where v2 represents the resource utilization index, and k represents the total number of features used for capability matching degree calculation; The transaction matching degree describes the ability to control logistics transaction costs. The indicator used to calculate the transaction matching degree is called the cost optimization indicator, and the calculation method is shown in the following formula: v3=max(d 3,1 ,d 3,2 ,…,d 3,m ) Where v3 represents the cost optimization index, and m represents the total number of features used for transaction matching degree calculation; Ultimately, a comprehensive evaluation of the matching degree between logistics resources and demand is formed (v1, v2, v3).

2. The method for evaluating the matching degree of logistics resources and demand under a shared model according to claim 1, characterized in that: The logistics process includes warehousing, transportation, network point receiving, and delivery. Warehousing is characterized by sorting time, available space, and sorting cost. Transportation is characterized by transportation cost, transportation time, transportation space, loading capacity, and transportation destination. Network point receiving is characterized by sorting time, available space, and sorting cost. Delivery is characterized by delivery cost, delivery time, vehicle space, loading capacity, and delivery destination.

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