Multi-commodity multi-source single-sink transportation network reliability evaluation method considering carbon emission constraint

Abstract

The invention relates to a multi-commodity multi-source single-sink transportation network reliability evaluation method considering carbon emission constraints, and belongs to the field of logistics transportation network reliability evaluation. The method aims at solving the problem that in the prior art, a multi-commodity multi-source single-sink logistics network reliability evaluation method considering the carbon emission constraint is lacked. The method comprises the following steps: inputting network and transportation data, solving a flow vector meeting multi-commodity requirements, calculating and verifying whether the total carbon emission meets a constraint condition or not, further converting a feasible flow vector into a candidate minimum capacity vector, searching a minimum capacity vector meeting a carbon emission constraint, and finally calculating the network reliability. According to the method, the completion degree of the transportation task and the environment target are considered, and the transportation service efficiency and the service quality of the multi-supplier logistics transportation network are effectively evaluated and verified.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of logistics transportation network reliability evaluation, and relates to a multi-commodity multi-source single-sink transportation network reliability evaluation method considering carbon emission constraints. BACKGROUND

[0002] With consumers putting forward higher requirements for the service quality, service capacity and transportation timeliness of the logistics industry, enterprises are increasingly concerned about the stability, timeliness and reliability of the logistics transportation network. However, the logistics transportation network is an open network affected by uncertain factors. Weather, traffic congestion, policies, construction and vehicle conditions and other factors will affect the transportation capacity of the logistics transportation network, that is, the transportation capacity of the network has randomness. Based on this random attribute, a large number of studies regard the logistics network as a multi-state random flow network, and each edge in the network has independent, limited integer random capacity, which is usually based on the historical data of the operator and subject to a certain probability distribution.

[0003] The operation scenario of modern logistics involves concurrent and mixed transportation of multiple commodities, which constitutes the main source of complexity and reality basis for reliability evaluation. Whether it is a large e-commerce warehouse processing concurrent orders of different demand quantities of commodities to the destination, a manufacturing plant synchronously receiving diversified raw materials and parts from various suppliers to ensure accurate production, or a retail central warehouse distributing full-category commodities to numerous stores to meet differentiated replenishment needs, all of these reflect the universal and key logistics transportation mode of multi-commodity, multi-source and single sink.

[0004] However, there is currently no reliability evaluation method for multi-commodity multi-source single-sink logistics network under carbon emission constraints. Traditional logistics network reliability evaluation methods mainly focus on single commodity, single source or do not consider carbon emission, and are difficult to meet the dual requirements of reliability and carbon emission reduction in the context of green logistics. To fill this gap, the present application first proposes a method for evaluating the reliability of multi-commodity multi-source single-sink logistics transportation network, and introduces carbon emission constraints for multi-commodity transportation in the model, thereby balancing the completion of transportation tasks and environmental goals.

[0005] The reliability of multi-commodity multi-source single-sink logistics transportation network considering carbon emission constraints refers to the probability that the network can successfully transport m kinds of commodities with demand D=(d1, d2, d3,..., d m ) to the destination, and the carbon emissions generated during transportation do not exceed the given upper limit E L . This reliability index is represented by R D,EL . The present application constructs a minimum capacity vector (D, E L) - MCV, i.e. Multi-Commodity Minimal Capacity Vector (MCV) to solve R D,EL . The Multi-Commodity Minimal Capacity Vector (D, E L ) - MCV means that the network can transport the specified demand amount D of various commodities to the destination under the minimal capacity vector, and the carbon emission amount generated in the transportation process is not more than E L , and any other capacity vector less than the capacity vector in the network cannot simultaneously satisfy the above two conditions. For the obtained (D, E L ) - MCV, the reliability R D,EL can be calculated using the inclusion-exclusion method. Therefore, finding the Multi-Commodity Minimal Capacity Vector (D, E L ) - MCV is the main goal of the method. SUMMARY

[0006] Therefore, the purpose of the present application is to provide a Multi-Commodity Multi-Source Single-Sink Transportation Network Reliability Evaluation Method Considering Carbon Emission Constraints, which is used to evaluate and verify whether the transportation service efficiency and service quality of the multi-supplier logistics transportation network in real situation meet the reliability requirements.

[0007] To achieve the above purpose, the present application provides the following technical solutions:

[0008] A Multi-Commodity Multi-Source Single-Sink Logistics Transportation Network Reliability Evaluation Method Considering Carbon Emission Constraints, which comprises the following steps:

[0009] 1) Input basic network and transportation data: basic data includes minimal paths p1, p2,..., p λ from supply sites s to destination sites t, where λ represents the total number of minimal paths from each supply site to the destination site; the demand amount D of various commodities at the destination site is D = (d1, d2, d3,..., dm) m , where the demand amount of the rth commodity at the destination site is d r (1≤r≤m), m represents the total number of commodities transported in the network; the capacity distribution and transportation distance c l (1≤l≤n), where n represents the total number of edges in the network; u l represents the maximum capacity of edge a l , the capacity of the edge represents the number of vehicles that can pass through the path; w r represents the capacity consumed for transporting one unit of commodity r; the empty fuel consumption rate k 0 , the full fuel consumption rate k 1 ; the carbon emission factor e of fuel and the total carbon emission constraint E L ;

[0010] 2) Solve the flow vector F that satisfies the demand of multiple commodities: Assume the demand of each commodity at the destination is D = (d1, d2, d3,..., dm)T, and the flow vector F = (f1, f2, f3,..., fm)T is found by transporting m commodities from each supply s to the destination t according to the following three conditions: m 1 λ 1 2 λ 2 m λ m ):

[0011]

[0012] where fr represents the flow of commodity r through the ith minimum path; fr represents the total flow of commodity r through all minimum paths; C represents the total capacity consumed by all commodities through the minimum path p; C represents the total capacity consumed by all commodities through the edge a; min{u i | a e p} represents the maximum capacity of the minimum path p; and min{u r | a e a i} represents the maximum capacity of the edge a. i l l l i i

[0013] 3) Calculate the total carbon emissions E(F) of the transportation network corresponding to each F obtained in step 2): The unit fuel consumption rates of the vehicle when empty and full are k 0 and k 1 , respectively, the carbon emission factor of fuel is e, and the distance of the transportation path a l is c l . E(F) represents the total carbon emissions of the logistics transportation network under the current flow vector F, and E F (a l ) represents the carbon emissions generated by transporting all kinds of commodities on the edge a l . For each transportation path a l , the capacity consumed and the capacity consumed by transporting all commodities on the path are obtained through the flow vector F. Under the premise that the logistics service provider adopts full truck transportation and the vehicle type is consistent, the number of vehicles required for the path is , that is, the minimum integer greater than or equal to , then the calculation formula of E F (a l ) is:​​​​​​​​​​​​

[0014]

[0015] The total carbon emissions E(F) of the logistics transportation network under the current flow vector F are the sum of the carbon emissions of all transportation edges:

[0016]

[0017] 4) Verify whether the flow vector F obtained in step 3) satisfies the carbon emission constraint condition: E L Let F represent the upper limit of total carbon emissions in the network. If the flow vector F satisfies the following carbon emission constraints, then the flow vector F is a feasible flow vector:

[0018] E(F)≤E L (6)

[0019] If each flow vector F does not satisfy the above constraints, then let R D,EL =0, and end the algorithm.

[0020] 5) Transform each feasible flow vector F selected in step 4) into a corresponding candidate minimum capacity vector X: The feasible flow vector F is transformed into a candidate capacity vector X = (x1, x2, ..., x...) using the following relation. n ):

[0021]

[0022] 6) Find the multi-commodity minimum capacity vector (D, E) that satisfies carbon emission constraints. L -MCV: Verifying candidate minimum capacity vectors using a comparison method: Assume X is a candidate minimum capacity vector. If there is no other candidate minimum capacity vector Y such that X ≥ Y, then X is a minimum capacity vector. By verifying each candidate minimum capacity vector obtained in step 5) using the comparison method, all minimum capacity vectors (D, E) can be obtained. L )-MCV.

[0023] 7) Calculate the reliability R of a multi-commodity, multi-source, single-sink network under carbon emission constraints. D,EL Based on the obtained minimum capacity vector, the disjoint sum algorithm is used to calculate the logistics transportation network that can handle the demand D = (d1, d2, d3, ..., d4). m m types of goods were successfully transported from the supply location to the destination, and the carbon emissions generated during the transportation process did not exceed a given upper limit E. L The probability of this is called the network reliability R. D,EL .

[0024] The method for evaluating reliability of a multi-commodity multi-source single-sink logistics transportation network under carbon emission constraints has the advantages that the method fills the blank of the method for evaluating reliability of a multi-commodity multi-source single-sink logistics network under carbon emission constraints, and realizes the consideration of the transportation task completion degree and the environmental target.

[0025] Other advantages, objects, and features of the present application will be apparent to those skilled in the art upon reading the following specification, and will be more readily apparent when the following specification is taken in conjunction with the accompanying drawings. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to make the objects, technical solutions and advantages of the present application clearer, the preferred embodiments of the present application will be described in detail below with reference to the drawings, in which:

[0027] Figure 1 The method flow chart of the present application is shown in Figure 1.

[0028] Figure 2 The network diagram of the specific embodiment is shown in Figure 2. DETAILED DESCRIPTION

[0029] The embodiments of the present application are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure of the present specification. The present application can also be implemented or applied through other different embodiments, and the details in the present specification can be modified or changed in various ways based on different views and applications without departing from the spirit of the present application. It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0030] The drawings are only used for illustrative explanation, and the representation is only a schematic diagram, not a physical diagram, and should not be understood as a limitation of the present application; in order to better illustrate the embodiments of the present application, some components in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; it is understandable for those skilled in the art that some known structures and their descriptions in the drawings may be omitted.

[0031] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it is understood that if the orientations or positional relationships indicated by the terms "upper", "lower", "left", "right", "front", "back" and the like are based on the orientations or positional relationships shown in the drawings, they are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore the terms describing the positional relationship in the drawings are only used for exemplary illustration, and cannot be understood as a limitation on the present application, for those skilled in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0032] The present application provides a multi-commodity multi-source single-hub logistics transportation network reliability evaluation method considering carbon emission constraints, the method flow chart is as shown in Figure 1 The method comprises the following steps:

[0033] 1) Input basic network and transportation data.

[0034] The basic data includes the minimum paths p1, p2,..., p λ from the supply place s to the destination t, wherein λ represents the total number of minimum paths from each supply place to the destination; the demand D=(d1, d2, d3,..., d m of the destination for various commodities, wherein the demand d r of the destination for the rth commodity is d (1≤r≤m), m represents the total number of commodity types transported in the network; the capacity distribution and transportation distance c l of each edge of the network (1≤l≤n), wherein n represents the total number of edges in the network; u l represents the maximum capacity of the edge a l , the capacity of the edge represents the number of vehicles that can pass through the path; w r represents the capacity consumed by transporting one unit of commodity r; the empty fuel consumption rate k 0 , the full fuel consumption rate k 1 ; the carbon emission factor e of fuel and the total carbon emission constraint E L ;

[0035] 2) Solve the flow vector F that meets the multi-commodity demand.

[0036] Suppose that the network needs to successfully transport m kinds of commodities with demand D=(d1, d2, d3,..., d m from the supply place s to the destination t through different transportation centers. Therefore, it is now necessary to find all flow vectors that meet the demand D. The flow vector F=(f1 1 ,…,f λ 1 ,f1 2 ,…,fλ 2 ,…,f1 m ,…,f λ m ) is a vector of the flow composition of each commodity flowing through each minimal path in the network, and λ represents the total number of minimal paths from each supply to the destination. All the minimal paths in the network are p1, p2,..., p λ Each minimal path is composed of several edges, and each edge a l has an upper limit of capacity u l . The flow of commodity r flowing through the i-th minimal path is denoted by f i r The algorithm allocates commodity flow to each minimal path step by step according to the commodity demand, so that the total flow of all commodities satisfies the demand constraint, i.e., for each commodity r, there is:

[0037]

[0038] In each flow adjustment process, the total capacity consumed by the commodities flowing through the minimal path is subject to the maximum capacity of the minimal path, and the total capacity consumed by the commodities flowing through an edge is subject to the maximum capacity of the edge, so there are the following constraints:

[0039]

[0040] where f i r (1≤r≤m,1≤i≤λ) represents the flow of commodity r flowing through the i-th minimal path; represents the total flow of commodity r flowing through all minimal paths; represents the total capacity consumed by all commodities flowing through the minimal path p i ; represents the total capacity consumed by all commodities flowing through the edge a l ; min{u l |a l ∈p i} represents the maximum capacity of the minimal path p i . The final output flow vector F satisfies the following three conditions: ① the total flow of each commodity on all paths is equal to its demand, i.e., expression (1); ② the total capacity consumed by commodities flowing through each edge does not exceed the upper limit of the edge capacity, i.e., expression (2); ③ the total capacity consumed by commodities flowing through each minimal path does not exceed the upper limit of the path capacity, i.e., expression (3).

[0041] 3) Calculate the total carbon emissions E(F) of the transport network corresponding to each F obtained in step 2).

[0042] In order to determine the flow vector F = (f1 1 ,…,f λ1 f1 2 ,…,f λ 2 ,…,f1 m ,…,f λ m After that, the next step is to calculate the total carbon emissions E(F) generated by the entire transportation network under this flow vector. Let k 0 and k 1 These represent the fuel consumption rates when the vehicle is empty and fully loaded, respectively. h represents the maximum load capacity of the transport vehicle, and edge a in the transport path... l Length c l Given the carbon emission factor e of the fuel, the total carbon emissions E(F) can be calculated using the following steps:

[0043] Step 1: Calculate the carbon emissions per unit distance transported by each vehicle.

[0044]

[0045] in, For edge a l The actual transport weight of the εth vehicle is then... This can be considered as the vehicle's load factor, i.e., the ratio of actual load to maximum load; τ l Representing edge a l The total number of transport vehicles.

[0046] Step 2, calculate the distance each vehicle travels along the transport path a. l Total carbon emissions from driving

[0047]

[0048] Step 3: Calculate the total number of vehicles along transport path a. l Total carbon emissions from transporting goods E F (a l ):

[0049]

[0050] Assuming the logistics service provider uses full truckload transportation and all vehicles are of the same type, the transportation route a l The carbon emissions along a transport route can be determined by the number of vehicles used. Given that the transport route a... l The total capacity of goods consumed is but The number of vehicles required to complete the transportation along this route, i.e., greater than or equal to... The smallest integer. Based on this, all vehicles travel along transport path a. lTotal carbon emission of all shipments in the transportation network EF(a l ) can be converted to:

[0051]

[0052] Step 4, calculate the total carbon emission of the whole transportation network under feasible flow F, E(F):

[0053] The total carbon emission of the transportation network corresponding to each F is the accumulation of carbon emission of all shipments along all transportation paths, which can be calculated as follows:

[0054]

[0055] 4) Verify whether the flow vector F obtained in step 3) satisfies the carbon emission constraint condition.

[0056] Let E L represent the upper limit of the total carbon emission of the network, if the flow vector F satisfies the following carbon emission constraint, then the flow vector F is a feasible flow vector:

[0057] E(F)≤E L (9) E(F)≤E L , which means that under the flow vector F, the total carbon emission of the network cannot exceed the given carbon emission upper limit E L . If each flow vector F does not satisfy the above constraint condition, let R D,EL = 0, and end the algorithm.

[0058] 5) Convert each feasible flow vector F screened in step 4) into the corresponding candidate minimal capacity vector X.

[0059] According to the relationship between flow and capacity, the feasible flow vector F can be converted into the corresponding candidate capacity vector X = (x1, x2,..., xn) by the following relationship: n

[0060]

[0061] 6) Find the multi-commodity minimal capacity vector (D, E L ) that satisfies the carbon emission constraint, MCV.

[0062] The candidate minimal capacity vector converted from step 5) is not necessarily a minimal capacity vector, so each candidate minimal capacity vector needs to be verified. The verification of candidate minimal capacity vectors usually uses the comparison method: assuming X is a candidate minimal capacity vector, if there is no other candidate minimal capacity vector Y such that X ≥ Y, then X is a minimal capacity vector. By verifying each candidate minimal capacity vector obtained in step 5) using the comparison method, all minimal capacity vectors (D, E L ​)-MCV.

[0063] 7) Calculate the reliability R of a multi-commodity, multi-source, single-sink network under carbon emission constraints. D,EL .

[0064] Based on the obtained minimum capacity vector (D, E) L )-MCV, through the disjoint sum algorithm, calculates the logistics transportation network to achieve a demand of D = (d1, d2, d3, ..., d m m types of goods were successfully transported from the supply location to the destination, and the carbon emissions generated during the transportation process did not exceed a given upper limit E. L The probability of this is called the network reliability R. D,EL .

[0065] A specific implementation example Figure 2 As shown, an abstraction of a logistics transportation network yields... Figure 2 The network consists of 6 nodes and 6 transport edges. Nodes s1, s2, and s3 represent three supply locations, intermediate nodes 1 and 2 represent transshipment centers, and node t represents a demand location. Table 1 shows the capacity distribution and segment distances for each edge in the network. Figure 2 There are four minimal paths from the source to the sink: P1 = (a1, a5), P2 = (a2, a6), P3 = (a3, a6), and P4 = (a4, a6). Assume the network transports three goods (A, B, and C) with demands of 100, 200, and 100 (units: boxes), respectively. For ease of calculation, assume 100 standard boxes represent 1 demand unit, then the demand is D = (1, 2, 1), where d1 = 1, d2 = 2, and d3 = 1. The 100 boxes of goods A and B weigh 2 tons, 3 tons, and 4 tons, respectively. The vehicle's carrying capacity is 5 tons. Therefore, the capacity consumed by one unit of goods A, B, and C is w1 = 0.4, w2 = 0.6, and w3 = 0.8, respectively. Regarding the transport vehicle, assume its relevant parameters are k... 1 =0.5L / km, k 0 =0.23L / km, e=2.73kg / L.

[0066] Table 1 Figure 2 Capacity distribution and segment distance data in the middle and sides

[0067]

[0068] The following uses the method of the present invention to calculate the probability that the logistics transportation network can transport one unit of product A, two units of product B, and one unit of product C to the destination, respectively, and the total carbon emissions do not exceed the given carbon emission limit of 48 kg.

[0069] According to the method steps of this invention, the solution process is as follows:

[0070] 1) Input basic network and transportation data. Specifically, the minimal paths from supply s to destination t are P1 = (a1, a5), P2 = (a2, a6), P3 = (a3, a6), P4 = (a4, a6); the demand of various commodities is D = (1, 2, 1); the capacity probability distribution of each edge of the network and the transportation distance are as shown in Table 1; w1 = 0.4, w2 = 0.6, w3 = 0.8; the empty fuel consumption rate k 0 = 0.23 L / km, the full fuel consumption rate k 1 = 0.5 L / km; the carbon emission factor of fuel e = 2.73 kg / L; the total carbon emission constraint E L = 48 kg.

[0071] 2) Solve the flow vector F satisfying the multi-commodity demand.

[0072] Generate all flow vectors F satisfying the following conditions:

[0073] f1 1 +f2 1 +f3 1 +f4 1 = 1

[0074] f1 2 +f2 2 +f3 2 +f4 2 = 2

[0075] f1 3 +f2 3 +f3 3 +f4 3 = 1

[0076] 0 ≤ f1 1 × w1 + f1 2 × w2 + f1 3 × w 3≤ 1

[0077] 0 ≤ f2 1 × w1 + f2 2 × w2 + f2 3 × w3 ≤ 1

[0078] 0 ≤ f3 1 × w1 + f3 2 × w2 + f3 3 × w3 ≤ 1

[0079] 0 ≤ f4 1 × w1 + f4 2 × w2 + f4 3 × w3 ≤ 2

[0080] 0≤f1 1 ×w1+f1 2 ×w2+f1 3 ×w 3≤ 3

[0081] 0≤f2 1 ×w1+f2 2 ×w2+f2 3 ×w3≤1

[0082] 0≤f3 1 ×w1+f3 2 ×w2+f3 3 ×w3≤1

[0083] 0≤f4 1 ×w1+f4 2 ×w2+f4 3 ×w3≤2

[0084] 0≤f1 1 ×w1+f1 2 ×w2+f1 3 ×w 3≤ 1

[0085] 0≤(f2 1 +f3 1 +f4 1 )×w1+(f2 2 +f3 2 +f4 2 )×w2+(f2 3 +f3 3 +f4 3 )×w3≤4

[0086] A total of 44 flow vectors are generated based on the above conditions, as shown in column 1 of Table 2.

[0087] 3) Calculate the total carbon emissions of the transportation network corresponding to each F obtained in step 2). Taking F = (0,0,0,1,0,0,0,2,1,0,0,0) as an example, the carbon emissions are E(F) = E F (a1)+E F (a2)+E F (a3)+E F (a4)+E F (a5)+E F (a6) = 47.48562. The total carbon emissions of the transportation network corresponding to each F are shown in column 2 of Table 2.

[0088] 4) Verify the flow vector F obtained in step 3) whether it satisfies the carbon emission constraint condition. Determine whether the total carbon emission of the logistics transportation network under each flow vector F is not more than the upper limit of carbon emission 48 kg, i.e. E(F)≤48.

[0089] After step 4), all flow vectors with carbon emission less than or equal to the constraint condition are screened, and a total of 15 flow vectors meet the condition. The specific results are shown in column 3 of Table 2.

[0090] 5) Convert each feasible flow vector F screened in step 4) into a corresponding candidate minimal capacity vector X (see column 4 of Table 2 for details):

[0091]

[0092] 6) Find a multi-commodity minimal capacity vector ((1, 2, 1), 48)-MCV that satisfies the carbon emission constraint.

[0093] Compare the candidate minimal capacity vectors in step 5), and obtain 3 non-repeating minimal capacity vectors ((1, 2, 1), 48)-MCV. Refer to columns 4 and 5 of Table 2.

[0094] 7) Calculate the multi-commodity multi-source single-sink network reliability R under the carbon emission constraint (1,2,1),48 . Calculate the network reliability R (1,2,1),48 = 0.95861 by the inclusion-exclusion algorithm (RSDP), i.e. the network has a probability of 0.95861 to successfully transport 100 boxes of commodity A, 200 boxes of commodity B and 100 boxes of commodity C to the destination t to meet the demand, and the carbon emission generated during the transportation process is not more than 48 kg.

[0095] Table 2 Calculation results of minimal capacity vector ((1, 2, 1), 48)-MCV

[0096]

[0097]

[0098] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A reliability assessment method for multi-commodity, multi-source, single-sink transportation networks considering carbon emission constraints, characterized in that: Includes the following steps: Step 1): Input basic network and transportation data: including the minimum paths p1, p2, ..., p from the supply point s to the destination t. λ The demand for m types of goods at the destination is D = (d1, d2, d3, ..., dm). m ); the capacity distribution and transport distance c of each edge of the network. l , 1≤l≤n; edge a l Maximum capacity u l ;transportation The capacity w consumed by one unit of commodity r r Unloaded fuel consumption rate k0, fully loaded fuel consumption rate k1; carbon emission factor e of fuel and total carbon emission constraint E. L ; Step 2): Solve for the flow vector F that satisfies the demand for multiple goods: Based on the demand quantity D and the minimum path, find all flow vectors F = (f1) that satisfy the demand quantity. 1 ,…,f λ 1 f1 2 ,…,f λ 2 ,…,f1 m ,…,f λ m The flow vector F satisfies the following condition: Where r = 1, 2, ..., m Where i = 1, 2, ..., λ Where l = 1, 2, ..., n in, Let r represent the flow rate of goods r through the i-th minima, where 1 ≤ r ≤ m, 1 ≤ i ≤ λ; This represents the total flow of goods *r* through all minima. Indicates flow through minimal path p i The total capacity consumed by all goods; Indicates the flow through edge a l The total capacity consumed by all goods; min{u l |a l ∈p i } represents the minimum path p i Maximum capacity; Step 3): Calculate the total carbon emissions E(F) of the transport network corresponding to each flow vector F obtained in Step 2): E(F) is the sum of carbon emissions from all transport sides, and the formula for calculating E(F) is as follows: Among them, E F (a l ) indicates that on edge a l The carbon emissions generated from transporting all types of goods are calculated using the following formula: Where l = 1, 2, ..., n This indicates that the flow through edge a is greater than or equal to the value of the edge. l The smallest integer representing the total capacity consumed by all goods is the number of vehicles required to complete the transportation of that route. Step 4): Verify whether the flow vector F obtained in Step 3) satisfies the carbon emission constraints: If the flow vector F satisfies the following carbon emission constraints, then the flow vector F is a feasible flow vector: E(F)=E L If none of the flow vectors F satisfy the above constraints, then let the reliability R... D,EL =0, and end the algorithm; Step 5): Transform each feasible flow vector F selected in Step 4) into a corresponding candidate minimum capacity vector X = (x1, x2, ..., x...). n ): Where l = 1, 2, ..., n Step 6): Find the multi-commodity minimum capacity vector ((D, E) that satisfies the carbon emission constraint. L X(-MCV): The candidate minimum capacity vector is verified by comparison. If there is no other candidate minimum capacity vector Y such that X≥Y, then X is the minimum capacity vector. Step 7): Calculate the reliability R of a multi-commodity, multi-source, single-sink network under carbon emission constraints. D,EL Based on the obtained minimum capacity vector, the disjoint sum algorithm is used to calculate that the logistics transportation network can handle the demand D = (d1, d2, d3, ..., d...). m m types of goods were successfully transported from the supply location to the destination, and the carbon emissions generated during the transportation process did not exceed a given upper limit E. L The probability of.

2. The reliability assessment method for multi-commodity, multi-source, single-sink transportation networks considering carbon emission constraints as described in claim 1, characterized in that: In step 1), λ represents the total number of minimal paths from each supply point to the destination; d r Let m represent the demand for the r-th type of commodity at the destination, where 1 ≤ r ≤ m; m represents the total number of commodity types transported in the network; and n represents the total number of edges in the network.

3. The reliability assessment method for multi-commodity, multi-source, single-sink transportation networks considering carbon emission constraints as described in claim 1, characterized in that: In step 2), the total capacity consumed by the goods flowing through the minimum path is constrained by the maximum capacity of the minimum path, and the total capacity consumed by the goods flowing through a certain edge is constrained by the maximum capacity of that edge.

4. The reliability assessment method for multi-commodity, multi-source, single-sink transportation networks considering carbon emission constraints as described in claim 1, characterized in that: In step 3), the logistics service provider adopts full truckload transportation, and the vehicles are of the same type.

5. The reliability assessment method for multi-commodity, multi-source, single-sink transportation networks considering carbon emission constraints as described in claim 1, characterized in that: In step 6), the candidate minimum capacity vectors obtained in step 5) are verified one by one to obtain all the minimum capacity vectors ((D,E)). L )-MCV).

6. A reliability assessment system for multi-commodity, multi-source, single-sink transportation networks considering carbon emission constraints, characterized in that: include: The input module is used to input basic network and transportation data, which includes the minimum paths p1, p2, ..., p from the supply point s to the destination t. λ The demand for m types of goods at the destination is D = (d1, d2, d3, ..., dm). m ); the capacity distribution and transport distance c of each edge of the network. l , 1≤l≤n; edge a l Maximum capacity u l ;transportation The capacity w consumed by one unit of commodity r r Unloaded fuel consumption rate k0, fully loaded fuel consumption rate k1; carbon emission factor e of fuel and total carbon emission constraint E. L ; The flow vector solving module is used to find all flow vectors F = (f1) that satisfy the demand D and the minimum path. 1 ,…,f λ 1 f1 2 ,…,f λ 2 ,…,f1 m ,…,f λ m The flow vector F satisfies the following condition: Where r = 1, 2, ..., m 0≤∑ r f i r ×w r ≤min{u l |a l ∈P i } among them i=1,2,…,l Where l = 1, 2, ..., n The carbon emission calculation module is used to calculate the total carbon emissions E(F) of the transportation network corresponding to each flow vector F. E(F) is the sum of carbon emissions from all transportation edges, and the formula for calculating E(F) is: Among them, E F (a l ) indicates that on edge a l The carbon emissions generated from transporting all types of goods are calculated using the following formula: Where l = 1, 2, ..., n The feasibility assessment module is used to verify whether the flow vector F obtained by the carbon emission calculation module satisfies the carbon emission constraint condition E(F)≤E. L ; The vector transformation module is used to transform the feasible flow vector F selected by the feasibility judgment module into the corresponding candidate minimum capacity vector X = (x1, x2, ..., x...). n ): Where l = 1, 2, ..., n The minimum capacity vector search module is used to find the minimum capacity vector ((D,E) of multiple commodities that meets carbon emission constraints. L The minimum capacity vector finding module uses a comparison method to verify the candidate minimum capacity vector. If there is no other candidate minimum capacity vector Y such that X≥Y, then X is the minimum capacity vector. The reliability calculation module is used to calculate, based on the obtained minimum capacity vector, the ability of the logistics transportation network to handle the demand D = (d1, d2, d3, ..., d4) using a disjoint summation algorithm. m m types of goods were successfully transported from the supply location to the destination, and the carbon emissions generated during the transportation process did not exceed a given upper limit E. L The probability of this is called the network reliability R. D,EL .

7. The reliability assessment system for multi-commodity, multi-source, single-sink transportation networks considering carbon emission constraints as described in claim 6, characterized in that: In the input module, λ represents the total number of minimal paths from each supply point to the destination; d r Let m represent the demand for the r-th type of commodity at the destination, where 1 ≤ r ≤ m; m represents the total number of commodity types transported in the network; and n represents the total number of edges in the network.

8. The reliability assessment system for multi-commodity, multi-source, single-sink transportation networks considering carbon emission constraints as described in claim 6, characterized in that: In the flow vector solving module, the total capacity consumed by the goods flowing through the minimum path is constrained by the maximum capacity of the minimum path, and the total capacity consumed by the goods flowing through a certain edge is constrained by the maximum capacity of that edge.

9. The reliability assessment system for multi-commodity, multi-source, single-sink transportation networks considering carbon emission constraints as described in claim 6, characterized in that: In the carbon emission calculation module, the logistics service provider uses full-truckload transportation, and the vehicles are all of the same type.

10. The reliability assessment system for multi-commodity, multi-source, single-sink transportation networks considering carbon emission constraints as described in claim 6, characterized in that: In the minimum capacity vector finding module, by verifying the candidate minimum capacity vectors obtained in the vector transformation module one by one, all minimum capacity vectors ((D,E) can be obtained. L )-MCV).

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