Low-carbon supply chain management method

Abstract

The invention discloses a low-carbon supply chain management method, relates to the technical field of supply chain management, and solves the problem that an existing cold-chain logistics scheme does not consider carbon emission and enterprise development demands. Calculating user dissatisfaction, carbon emission and total cost; determining a constraint function by taking user dissatisfaction, carbon emission and total cost minimization as objective functions, and constructing a low-carbon logistics model; solving the low-carbon logistics model according to an improved hybrid genetic algorithm to obtain an excellent logistics scheme set; obtaining a target function weight matrix, adaptively adjusting the target function weight matrix according to the target function mean value, and screening through the adjusted target function weight matrix to obtain an optimal logistics scheme; and building a low-carbon logistics model by comprehensively considering user dissatisfaction, carbon emission and total cost, and screening an optimal logistics scheme meeting enterprise development requirements from an excellent logistics scheme set based on a set target function weight matrix.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain management, and more specifically, to a low-carbon supply chain management method. Background Art

[0002] Cold chain logistics is an important part of logistics supply chain management. Cold chain logistics has strict requirements on delivery time and has higher carbon emissions than normal temperature transportation. Current research generally determines cold chain logistics solutions based on user satisfaction and transportation costs, with less analysis of carbon emissions, which is not conducive to low-carbon supply chain management; and traditional cold chain logistics solutions based on multi-objective solutions obtain a set of optional logistics solutions, without considering the development needs of enterprises, and cannot obtain an exact logistics solution that meets the development needs of enterprises. Summary of the invention

[0003] The purpose of this application is to provide a low-carbon supply chain management method to solve the problem that the existing cold chain logistics solutions do not take into account carbon emissions and the needs of enterprise development. This application comprehensively considers user dissatisfaction, carbon emissions and total costs to construct a low-carbon logistics model, and obtains a set of excellent logistics solutions that take into account user dissatisfaction, carbon emissions and total costs based on an improved hybrid genetic algorithm solution model, and selects the optimal logistics solution that meets the needs of enterprise development from the set of excellent logistics solutions based on the set objective function weight matrix.

[0004] The above-mentioned technical objectives of the present application are achieved through the following technical solutions: including: S1, obtaining relevant parameters, including: user data, truck data and cargo data; S2, calculating user dissatisfaction, carbon emissions and total cost; S3, taking user dissatisfaction, carbon emissions and total cost minimization as objective functions, determining constraint functions, and constructing a low-carbon logistics model; S4, solving the low-carbon logistics model according to an improved hybrid genetic algorithm to obtain a set of excellent logistics solutions; S5, obtaining the objective function weight matrix, adaptively adjusting the objective function weight matrix according to the mean of the objective function, and screening the optimal logistics solution from the set of excellent logistics solutions through the adjusted objective function weight matrix.

[0005] By adopting the above technical scheme, user dissatisfaction, carbon emissions and total cost are calculated, and a multi-objective low-carbon logistics model is constructed with the goal of minimizing user dissatisfaction, carbon emissions and total cost. A set of excellent logistics solutions is obtained based on the improved hybrid genetic algorithm solution model, and the excellent logistics solution set includes a variety of logistics solutions; then, based on the objective function weight matrix representing the needs of the enterprise and the mean of the objective function, the optimal logistics solution that meets the development needs of the enterprise is selected from the excellent logistics solution set; on the one hand, this scheme fully considers user dissatisfaction, carbon emissions and total cost to solve the excellent logistics solution set, and on the other hand, considers the development needs of the enterprise to solve the optimal logistics solution, and balances the contribution of each objective function through the mean of the objective function, and obtains a cold chain logistics solution that adapts to the development needs of different enterprises and balances user satisfaction, carbon emissions and total cost, which can be used to guide the cold chain logistics of enterprises.

[0006] In a possible implementation manner, the user dissatisfaction is: ; in, For user dissatisfaction, ɑ 1 Assign coefficient to user dissatisfaction, i For truck labels, I is the total number of trucks, n For user tags, N is the total number of users, Q n i For the i The truck is n The value is 1 when the user is serving, and 0 otherwise. X is the total demand, x n For the n The demand of users, User dissatisfaction caused by delivery time, Customer dissatisfaction caused by the freshness of goods, t n i For the i Truck delivered to n The time of users, t n1 For the n The earliest delivery time acceptable to users, t n2 For the n The earliest delivery time agreed by users, t n3 For the n The latest delivery time agreed by users, t n4 For the nThe latest delivery time acceptable to users, t no For the n The freshness lock time of user's goods, α n For the n The deterioration coefficient of the user's goods.

[0007] In a possible implementation, the carbon emissions are: ; in, PCO2 is carbon emissions, n For user tags, N is the total number of users, m Different from n User tags, i For truck labels, I is the total number of trucks, W nm i For the i The truck from n User transported to m The value is 1 when the user is a user, and 0 otherwise. PCO2 1 ( d nm ) is the carbon emission caused by fuel consumption, PCO2 2 ( d nm ) is the carbon emissions caused by refrigeration, d nm For the n Users to m Delivery distance per user, h 1 is the unit carbon emission coefficient of fuel consumption, PCO2 min is the fuel consumption per unit distance when the truck is unloaded, Z nm For trucks from n User transported to m Cargo capacity per user-hour, Z max is the maximum cargo capacity of the truck, PCO2 max is the fuel consumption per unit distance when the truck is fully loaded, h 2 is the unit carbon emission coefficient of refrigeration.

[0008] In a possible implementation, the total cost is: ; in,COST is the total cost, COST 1 is the fixed cost of the truck, COST 2 is the carbon emission cost, COST 3 is the dissatisfaction cost, i For truck labels, I is the total number of trucks, E i For trucks i 1 when participating in delivery, 0 otherwise. c i For the i The cost of a truck, n , m For user tags, N is the total number of users, W nm i For the i The truck from n User transported to m The value is 1 when the user is a user, and 0 otherwise. c 1 is the fuel cost per unit distance of transportation, d nm For n User transported to m Delivery distance per user, c 2 is the unit cooling cost, D i For the i The heat load of a truck, t i For the i The transportation time of a truck, ɑ 2 is the waiting cost for early delivery, ɑ 3 Compensation costs for delayed delivery, ɑ 4 Compensation costs for product corruption, t n2 For the n The earliest delivery time agreed by users, t n i For the i Truck delivered to n The time of users, t n3 For the n The latest delivery time agreed by users, t no For the nThe freshness lock time of user's goods.

[0009] In a possible implementation, the objective function is: ; in, F is the objective function, is the user dissatisfaction function, PCO2 is the carbon emission function, COST is the total cost function.

[0010] In a possible implementation, the constraint function is: ; Among them, formula 1 means that one user can only be delivered by one truck, and formula 2 means that the truck has to deliver from the distribution center. n =0 departure, and finally return to the distribution center m =0, Formula 3 indicates that the total number of users is N , Formula 4 indicates that the delivery weight of each vehicle does not exceed the maximum cargo capacity.

[0011] In a possible implementation, step S4 includes: S41, according to the total number of users N and total number of trucks I generate R non-repeating natural numbers, R = N + I - 1 ,according to R non-repeating natural numbers are randomly arranged to generate multiple chromosomes; S42, selecting from multiple chromosomes according to the constraint function S valid chromosomes to form a chromosome population, and set the number of iterations; S43, perform fast non-dominated sorting and crowding calculation on each chromosome according to the objective function, obtain the fast non-dominated sorting and crowding of each chromosome, sort each chromosome according to the fast non-dominated sorting and crowding of each chromosome, and select the top s chromosomes as excellent individuals; S44, perform crossover and mutation operations on the excellent individuals to generate multiple first child individuals, and randomly generate multiple second child individuals, delete duplicated child individuals and screen the first child individuals and the second child individuals according to the constraint function to obtain valid child individuals, add the valid child individuals to the chromosome population, and obtain the next generation chromosome population; S45, repeat steps S43-S44 until the number of iterations is met, and decode the excellent individuals to form a set of excellent logistics solutions.

[0012] In a possible implementation manner, the number of valid sub-individuals is S, the first sub-individuals account for 90% of the valid sub-individuals, and the second sub-individuals account for 10% of the valid sub-individuals.

[0013] In a possible implementation, step S5 includes: S51, analyzing the objective function value of each excellent individual in the set of excellent logistics solutions to form an objective function value matrix of the excellent individuals; S52, correcting the obtained objective function weight matrix according to the total objective function mean of the excellent individuals in the set of excellent logistics solutions and the mean of each objective function of the excellent individuals in the set of excellent logistics solutions; S53, calculating the optimal solution value of the excellent individuals according to the corrected objective function weight matrix and the objective function value matrix of the excellent individuals, and selecting the excellent individual with the largest optimal solution value to be decoded as the optimal logistics solution.

[0014] In a possible implementation, the optimal solution value of the excellent individual is: ; Among them, β r For the r The optimal solution value of excellent individuals, [ f r1 ,f r2 ,f r3 ] is the first r The objective function value matrix of excellent individuals, f r1 ,f r2 ,f r3 For the r The objective function value of an excellent individual, [ w1 , w2 , w3 ] is the objective function weight matrix, w1 , w2 , w3 is the objective function f r1 ,f r2 ,f r3 The weight value of is the mean of the total objective function of the excellent individuals in the set of excellent logistics solutions, is the mean of the objective functions of the excellent individuals in the set of excellent logistics solutions.

[0015] Compared with the prior art, the present application has the following beneficial effects: by calculating user dissatisfaction, carbon emissions and total cost, a low-carbon logistics model is constructed with the goal of minimizing user dissatisfaction, carbon emissions and total cost, and a set of excellent logistics solutions is solved. The excellent logistics solution set provides a cold chain logistics solution with low user dissatisfaction, carbon emissions and total cost, which is helpful for low-carbon supply chain management; the development needs of the enterprise are considered through the objective function weight matrix, and the contribution of each objective function is balanced by combining the mean of the objective function to solve the optimal logistics solution, which provides the enterprise with a cold chain logistics solution that meets the development needs; in addition, the effects of delivery time, freshness of goods and proportion of goods are considered in the calculation of user dissatisfaction, the effects of fuel consumption carbon emissions and refrigeration carbon emissions are considered in the calculation of carbon emissions, and the effects of fixed costs, carbon emission costs and dissatisfaction costs are considered in the calculation of total costs to improve the calculation accuracy; an improved hybrid genetic algorithm is used in the solution of the low-carbon logistics model, and sub-individuals are generated by combining genetic algorithms and random algorithms to avoid falling into local optimality. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 This is a flow chart of the low-carbon supply chain management method. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with examples and drawings. The illustrative implementation scheme of the present application and its description are only used to explain the present application and are not intended to limit the present application.

[0018] See also Figure 1 As shown, Figure 1 The figure is a flow chart of the low-carbon supply chain management method. The low-carbon supply chain management method includes: S1, obtaining relevant parameters, including: user data, truck data and cargo data; S2, calculating user dissatisfaction, carbon emissions and total cost; S3, taking user dissatisfaction, carbon emissions and total cost minimization as the objective function, determining the constraint function, and constructing a low-carbon logistics model; S4, solving the low-carbon logistics model according to the improved hybrid genetic algorithm to obtain a set of excellent logistics solutions; S5, obtaining the objective function weight matrix, adaptively adjusting the objective function weight matrix according to the objective function mean, and selecting the optimal logistics solution from the set of excellent logistics solutions through the adjusted objective function weight matrix.

[0019] Specifically, firstly, the relevant parameters for objective function calculation are obtained, and the relevant parameters can be generally divided into user data, truck data and cargo data. User data may include: the total number of users, the cargo required by each user, the demand of each user, the delivery time agreed by each user, the acceptable delivery time for each user, the location of each user, etc.; truck data may include: the total number of trucks, the unit carbon emission coefficient of truck fuel consumption, the unit carbon emission coefficient of truck refrigeration, the fuel consumption per unit distance when the truck is empty, the fuel consumption per unit distance when the truck is fully loaded, the maximum cargo capacity of the truck, etc.; cargo data may include: the type of cargo, the fresh lock time of the cargo, the deterioration coefficient of the cargo, etc. Then, based on the above user data, truck data and cargo data, the user dissatisfaction, carbon emissions and total cost are calculated, and the multi-objective low-carbon logistics model is constructed with the minimum user dissatisfaction, carbon emissions and total cost functions as the goal. Then, the low-carbon logistics model is solved based on the improved hybrid genetic algorithm, the logistics plan is encoded on the chromosome, and the excellent logistics plan set is solved through multiple iterations of selection, crossover and mutation. Finally, the enterprise development needs are reflected through the objective function weight matrix, and the contribution of each objective function is balanced through the objective function mean to solve the optimal logistics solution.

[0020] The improvement of this scheme is that it calculates user dissatisfaction, carbon emissions and total cost, and constructs a multi-objective low-carbon logistics model with the goal of minimizing user dissatisfaction, carbon emissions and total cost. According to the improved hybrid genetic algorithm solution model, a set of excellent logistics solutions is obtained. The various logistics solutions in the set of excellent logistics solutions fully consider user dissatisfaction, carbon emissions and total cost; based on the objective function weight matrix representing the needs of the enterprise combined with the objective function mean, the optimal logistics solution that meets the development needs of the enterprise is selected from the set of excellent logistics solutions. The objective function weight matrix considers the development needs of the enterprise, and the objective function mean balances the contribution of each objective function, and obtains a cold chain logistics solution that adapts to the development needs of different enterprises and balances user satisfaction, carbon emissions and total cost. It can be used to guide the cold chain logistics of enterprises.

[0021] In a possible implementation, the calculation of user dissatisfaction takes into account dissatisfaction caused by delivery time and dissatisfaction caused by the freshness of goods, and uses the proportion of demanded goods as the dissatisfaction weight of each user to solve the user dissatisfaction. User dissatisfaction is: ; in, For user dissatisfaction, ɑ 1 Assign coefficient to user dissatisfaction, i For truck labels, I is the total number of trucks, n For user tags, N is the total number of users, Q ni For the i The truck is n The value is 1 when the user is serving, and 0 otherwise. X is the total demand, x n For the n The demand of users, User dissatisfaction caused by delivery time, Customer dissatisfaction caused by the freshness of goods, t n i For the i Truck delivered to n The time of users, t n1 For the n The earliest delivery time acceptable to users, t n2 For the n The earliest delivery time agreed by users, t n3 For the n The latest delivery time agreed by users, t n4 For the n The latest delivery time acceptable to users, t no For the n The freshness lock time of user's goods, α n For the n The deterioration coefficient of the user's goods.

[0022] Specifically, the user dissatisfaction distribution coefficient a 1 A constant between 0 and 1, used to adjust the ratio of delivery time and goods freshness in the calculation of user dissatisfaction. n Demand per user x n and total demand X , which is used to assign higher weights to users with large demands and give priority to satisfying users with large demands.

[0023] Customer dissatisfaction caused by delivery time In the calculation of the earliest / latest delivery time acceptable to users, t n2 , t n3 The earliest / latest delivery time agreed with the user t n1 , t n4During actual delivery, users can agree on the earliest / latest delivery time, such as 10:00-11:00. At the same time, users have an acceptable earliest / latest delivery time, such as 9:00-11:30. t n2 - t n3 For delivery within 100 km, the user dissatisfaction caused by delivery time is considered to be 0. t n2 - t n3 The farther away, the higher the user dissatisfaction caused by delivery time, until the earliest / latest acceptable delivery time t n2 , t n3 , at this time, the user dissatisfaction caused by delivery time is 1.

[0024] Customer dissatisfaction caused by product freshness In the calculation of the freshness of the goods, the freshness-locking time and the deterioration coefficient of the goods are taken into account. t no At this time, the goods can be considered fresh and not corrupted, and the user dissatisfaction caused by the freshness of the goods is 0. As time goes by, the freshness of the goods increases according to the deterioration coefficient α n It is declining exponentially, leading to growing user dissatisfaction.

[0025] In a possible implementation, the carbon emissions are calculated by taking into account the impact of fuel consumption carbon emissions and refrigeration carbon emissions. The carbon emissions are: ; in, PCO2 is carbon emissions, n For user tags, N is the total number of users, m Different from n User tags, i For truck labels, I is the total number of trucks, W nm i For the i The truck from n User transported to m The value is 1 when the user is a user, and 0 otherwise. PCO2 1 ( d nm ) is the carbon emission caused by fuel consumption, PCO2 2 ( d nm) is the carbon emissions caused by refrigeration, d nm For the n Users to m Delivery distance per user, h 1 is the unit carbon emission coefficient of fuel consumption, PCO2 min is the fuel consumption per unit distance when the truck is unloaded, Z nm For trucks from n User transported to m Cargo capacity per user-hour, Z max is the maximum cargo capacity of the truck, PCO2 max is the fuel consumption per unit distance when the truck is fully loaded, h 2 is the unit carbon emission coefficient of refrigeration.

[0026] Specifically, the carbon emissions of cold chain transportation are mainly caused by transportation fuel consumption and transportation refrigeration. Therefore, the carbon emissions caused by fuel consumption are calculated first. PCO2 1 ( d nm ), the fuel consumption per unit distance of a truck is linearly related to the weight of the truck. The larger the truck load, the greater the fuel consumption per unit distance. PCO2 min , Fuel consumption per unit distance when the truck is fully loaded PCO2 max And the maximum cargo capacity of the truck Z max , we can find the current truck load Z nm (Trucks from n User transported to m The relationship between the fuel consumption per unit distance and the cargo capacity per user. n Users to m For example, based on the current cargo volume of the truck, Z nm The corresponding unit distance fuel consumption, n Users to m Delivery distance per user d nm and unit carbon emission coefficient of fuel consumption h 1 Then we can solve the problem of the truck from n Users to m Carbon emissions caused by fuel consumption during the period of each user PCO2 1 ( dnm ). The total carbon emissions caused by fuel consumption are obtained by summing up the distribution routes.

[0027] Next, calculate the carbon emissions caused by refrigeration PCO2 2 ( d nm ). Similarly, the truck from n Users to m For example, the unit carbon emission coefficient of refrigeration h 2 Refers to the carbon emissions per unit distance driven by a truck while maintaining cooling (excluding carbon emissions caused by fuel consumption). h 2 and n Users to m Delivery distance per user d nm , we can solve the problem of the truck from n Users to m Carbon emissions from cooling during the period of use PCO2 2 ( d nm ). The total carbon emissions caused by refrigeration are obtained by summing up the distribution routes.

[0028] In a possible implementation, the influence of fixed cost, carbon emission cost and unsatisfactory cost is considered in the calculation of total cost. The total cost is: ; in, COST is the total cost, COST 1 is the fixed cost of the truck, COST 2 is the carbon emission cost, COST 3 is the dissatisfaction cost, i For truck labels, I is the total number of trucks, E i For trucks i 1 when participating in delivery, 0 otherwise. c i For the i The cost of a truck, n , m For user tags, N is the total number of users, W nm i For the i The truck from n User transported tom The value is 1 when the user is a user, and 0 otherwise. c 1 is the fuel cost per unit distance of transportation, d nm For n User transported to m Delivery distance per user, c 2 is the unit cooling cost, D i For the i The heat load of a truck, t i For the i The transportation time of a truck, ɑ 2 is the waiting cost for early delivery, ɑ 3 Compensation costs for delayed delivery, ɑ 4 Compensation costs for product corruption, t n2 For the n The earliest delivery time agreed by users, t n i For the i Truck delivered to n The time of users, t n3 For the n The latest delivery time agreed by users, t no For the n The freshness lock time of user's goods.

[0029] Specifically, the total cost COST In the calculation of c i Calculating Fixed Costs COST 1 . Secondly, calculate the carbon emission costs associated with carbon emissions COST 2 , the cost of carbon emissions COST 2 Including fuel consumption cost and refrigeration cost. Fuel consumption cost is calculated by the fuel cost per unit distance of transportation. c 1 Delivery distance d nm Multiplying together, the cooling cost is calculated by the heat load of the truck. D i , Transportation time t i And the unit cooling cost c2 Finally, calculate the dissatisfaction cost caused by user dissatisfaction COST 3 . Dissatisfaction cost COST 3 It is to compensate for early delivery, delayed delivery, and also to compensate for the spoilage of goods due to exceeding the freshness-locking time.

[0030] Based on the above calculated user dissatisfaction, carbon emissions and total costs, multiple objective functions are constructed: ; in, F is the objective function, is the user dissatisfaction function, PCO2 is the carbon emission function, COST is the total cost function.

[0031] At the same time, a constraint function is constructed based on the actual situation: ; Among them, formula 1 means that one user can only be delivered by one truck, and formula 2 means that the truck has to deliver from the distribution center. n =0 departure, and finally return to the distribution center m =0, Formula 3 indicates that the total number of users is N , Formula 4 indicates that the delivery weight of each vehicle does not exceed the maximum cargo capacity.

[0032] A multi-objective low-carbon logistics model is constructed through the above scheme, and then the low-carbon logistics model is solved in combination with an improved hybrid genetic algorithm.

[0033] In a possible implementation, step S4 includes: S41, according to the total number of users N and total number of trucks I generate R non-repeating natural numbers, R = N + I - 1 ,according to R non-repeating natural numbers are randomly arranged to generate multiple chromosomes; S42, selecting from multiple chromosomes according to the constraint function S valid chromosomes to form a chromosome population, and set the number of iterations; S43, perform fast non-dominated sorting and crowding calculation on each chromosome according to the objective function, obtain the fast non-dominated sorting and crowding of each chromosome, sort each chromosome according to the fast non-dominated sorting and crowding of each chromosome, and select the top schromosomes as excellent individuals; S44, perform crossover and mutation operations on the excellent individuals to generate multiple first child individuals, and randomly generate multiple second child individuals, delete duplicated child individuals and screen the first child individuals and the second child individuals according to the constraint function to obtain valid child individuals, add the valid child individuals to the chromosome population, and obtain the next generation chromosome population; S45, repeat steps S43-S44 until the number of iterations is met, and decode the excellent individuals to form a set of excellent logistics solutions.

[0034] Specifically, the logistics plan is first encoded on the chromosome, and one chromosome represents one logistics plan. N and total number of trucks I generate R non-repeating natural numbers. For example, when the total number of users is 8 and the total number of trucks is 3, 8+3-1=10 non-repeating natural numbers are generated. Taking 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 as examples, 2 natural numbers are selected as separator interpolation, such as 9 and 10. The natural numbers are randomly arranged to generate chromosomes. For example, 83412591067 means that the first truck delivers 0-8-3-4-1-2-5-0, the second truck does not participate in the delivery, and the third truck delivers 0-6-7-0. For another example, 12598341067 means that the first truck delivers 0-1-2-5-0, the second truck delivers 0-8-3-4-0, and the third truck delivers 0-6-7-0. Here, 0 represents the distribution center. Refer to the above method for R A number of non-repeating natural numbers are randomly arranged to generate multiple chromosomes. Then, according to the logistics solutions represented by each chromosome and the constraint function, meaningless chromosomes are filtered out, and finally S The chromosome population is composed of valid chromosomes, and the number of iterations is set to K。

[0035] Then calculate the objective function value (including user dissatisfaction, carbon emissions and total cost) corresponding to the logistics plan represented by each chromosome, and determine the order of each chromosome according to the fast non-dominated sorting algorithm and the congestion algorithm. When sorting, give priority to chromosomes with low non-dominated levels, followed by chromosomes with high congestion, and select the chromosomes with the highest congestion level. s Chromosomes as excellent individuals.

[0036] Based on the previous s The chromosomes are crossed and mutated to produce multiple first sub-individuals, and multiple second sub-individuals are randomly generated at the same time, ensuring the inheritance of excellent individuals while avoiding falling into the local optimum. The repeated chromosomes in the sub-individuals are screened out, and the chromosomes with meaningless encoding are screened out according to the constraint function. The screened sub-individuals are merged into the chromosome population to obtain the next generation chromosome population. K = K + 1.

[0037] Furthermore, the number of effective sub-individuals is S , the first sub-individual accounts for 90% of the valid sub-individuals, and the second sub-individual accounts for 10% of the valid sub-individuals.

[0038] Repeat the selection, crossover, and mutation of the next generation chromosome population, and iterate and update the chromosome population in combination with random generation until the set number of iterations is met. K At this time, the most recently selected excellent individuals are decoded to form a set of excellent logistics solutions.

[0039] In a possible implementation, step S5 includes: S51, analyzing the objective function value of each excellent individual in the set of excellent logistics solutions to form an objective function value matrix of the excellent individuals; S52, correcting the obtained objective function weight matrix according to the total objective function mean of the excellent individuals in the set of excellent logistics solutions and the mean of each objective function of the excellent individuals in the set of excellent logistics solutions; S53, calculating the optimal solution value of the excellent individuals according to the corrected objective function weight matrix and the objective function value matrix of the excellent individuals, and selecting the excellent individual with the largest optimal solution value to be decoded as the optimal logistics solution.

[0040] Specifically, the excellent logistics solution set includes r Excellent individuals (logistics solutions) F 1 ,F 2 ,…,F r}, based on the user dissatisfaction function, carbon emission function and total cost function, calculate the objective function value of each logistics plan: {( f 11 ,f 12 , f 13 ) , ( f 21 ,f 22 , f 23 ), …, ( f r1 ,f r2 , f r3 )}; Calculate the mean of the total objective function of the excellent individuals in the set of excellent logistics solutions : ; Calculate the mean of each objective function of the excellent individuals in the excellent logistics solution set ：

[0041] The objective function weight matrix representing the enterprise development needs is obtained through artificial setting or expert evaluation method [ w1 , w2 , w3 ], w1 , w2 , w3 is the objective function f r1 ,f r2 , f r3 The weight value indicates the preference for each objective function in the development needs of the enterprise. When selecting the optimal logistics method from the set of excellent logistics solutions, the optimal solution value β can be calculated by multiplying the objective function value and the corresponding weight value, and the logistics solution with the largest optimal solution value is selected as the optimal logistics solution. At the same time, considering that each objective function itself has different contributions to the optimal solution value, the average of the total objective function is used to calculate the optimal solution value. And the mean of each objective function The ratio of the corrected weight w1 , w2 , w3 .

[0042] In a possible implementation, the optimal solution value of an excellent individual is: ; Among them, β r For the r The optimal solution value of excellent individuals, [ f r1 ,f r2 ,f r3 ] is the first r The objective function value matrix of excellent individuals, f r1 ,f r2 ,f r3 For the r The objective function value of an excellent individual, [ w1 , w2 , w3 ] is the objective function weight matrix, w1 , w2 , w3 is the objective function f r1 ,f r2 ,f r3 The weight value of is the mean of the total objective function of the excellent individuals in the set of excellent logistics solutions, is the mean of the objective functions of the excellent individuals in the set of excellent logistics solutions.

[0043] Finally, the best individual decoding with the largest optimal solution value is selected as the optimal logistics solution.

[0044] Compared with the existing technology, this scheme has the following beneficial effects: by calculating user dissatisfaction, carbon emissions and total cost, a low-carbon logistics model is constructed with the goal of minimizing user dissatisfaction, carbon emissions and total cost, and a set of excellent logistics solutions is solved. The excellent logistics solution set provides a cold chain logistics solution with low user dissatisfaction, carbon emissions and total cost, which is helpful for low-carbon supply chain management; the development needs of the enterprise are considered through the objective function weight matrix, and the contribution of each objective function is balanced by combining the mean of the objective function to solve the optimal logistics solution, which provides the enterprise with a cold chain logistics solution that meets the development needs; in addition, the effects of delivery time, freshness of goods and proportion of goods are considered in the calculation of user dissatisfaction, the effects of fuel consumption carbon emissions and refrigeration carbon emissions are considered in the calculation of carbon emissions, and the effects of fixed costs, carbon emission costs and dissatisfaction costs are considered in the calculation of total costs to improve the calculation accuracy; an improved hybrid genetic algorithm is used in the solution of the low-carbon logistics model, and sub-individuals are generated by combining genetic algorithms and random algorithms to avoid falling into local optimality.

[0045] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A low-carbon supply chain management method, characterized in that: include: S1. Obtain relevant parameters, including: user data, truck data and cargo data; S2, calculate user dissatisfaction, carbon emissions and total costs; S3. Taking user dissatisfaction, carbon emissions and total cost minimization as the objective function, determine the constraint function and build a low-carbon logistics model; S4. Solving the low-carbon logistics model according to an improved hybrid genetic algorithm to obtain a set of excellent logistics solutions; S5. Obtain an objective function weight matrix, adaptively adjust the objective function weight matrix according to the objective function mean, and screen the optimal logistics solution from the set of excellent logistics solutions through the adjusted objective function weight matrix.

2. A low-carbon supply chain management method according to claim 1, characterized in that: The user dissatisfaction is: ； in, For user dissatisfaction, ɑ 1 Assign coefficient to user dissatisfaction, i For truck labels, I is the total number of trucks, n For user tags, N is the total number of users, Q n i For the i The truck is n The value is 1 when the user is serving, and 0 otherwise. X is the total demand, x n For the n The demand of users, User dissatisfaction caused by delivery time, Customer dissatisfaction caused by the freshness of goods, t n i For the i Truck delivered to n The time of users, t n1 For the n The earliest delivery time acceptable to users, t n2 For the n The earliest delivery time agreed by users, t n3 For the n The latest delivery time agreed by users, t n4 For the n The latest delivery time acceptable to users, t no For the n The freshness lock time of user's goods, α n For the n The deterioration coefficient of the user's goods.

3. A low-carbon supply chain management method according to claim 1, characterized in that: The carbon emissions are: ； in, PCO2 is carbon emissions, n For user tags, N is the total number of users, m Different from n User tags, i For truck labels, I is the total number of trucks, W nm i For the i The truck from n User transported to m The value is 1 when the user is a user, and 0 otherwise. PCO2 1 ( d nm ) is the carbon emission caused by fuel consumption, PCO2 2 ( d nm ) is the carbon emissions caused by refrigeration, d nm For the n Users to m Delivery distance per user, h 1 is the unit carbon emission coefficient of fuel consumption, PCO2 min is the fuel consumption per unit distance when the truck is unloaded, Z nm For trucks from n User transported to m Cargo capacity per user-hour, Z max is the maximum cargo capacity of the truck, PCO2 max is the fuel consumption per unit distance when the truck is fully loaded, h 2 is the unit carbon emission coefficient of refrigeration.

4. A low-carbon supply chain management method according to claim 1, characterized in that: The total cost is: ； in, COST is the total cost, COST 1 is the fixed cost of the truck, COST 2 is the carbon emission cost, COST 3 is the dissatisfaction cost, i For truck labels, I is the total number of trucks, E i For trucks i 1 when participating in delivery, 0 otherwise. c i For the i The cost of a truck, n , m For user tags, N is the total number of users, W nm i For the i The truck from n User transported to m The value is 1 when the user is a user, and 0 otherwise. c 1 is the fuel cost per unit distance of transportation, d nm For n User transported to m Delivery distance per user, c 2 is the unit cooling cost, D i For the i The heat load of a truck, t i For the i The transportation time of a truck, ɑ 2 is the waiting cost for early delivery, ɑ 3 Compensation costs for delayed delivery, ɑ 4 Compensation costs for product corruption, t n2 For the n The earliest delivery time agreed by users, t n i For the i Truck delivered to n The time of users, t n3 For the n The latest delivery time agreed by users, t no For the n The freshness lock time of user's goods.

5. A low-carbon supply chain management method according to claim 1, characterized in that: The objective function is: ； in, F is the objective function, is the user dissatisfaction function, PCO2 is the carbon emission function, COST is the total cost function.

6. A low-carbon supply chain management method according to claim 1, characterized in that: The constraint function is: ； Among them, formula 1 means that one user can only be delivered by one truck, and formula 2 means that the truck has to deliver from the distribution center. n =0 departure, and finally return to the distribution center m =0, Formula 3 indicates that the total number of users is N , Formula 4 indicates that the delivery weight of each vehicle does not exceed the maximum cargo capacity.

7. A low-carbon supply chain management method according to claim 1, characterized in that: Step S4 includes: S41. According to the total number of users N and the total number of trucks I generate R non-repeating natural numbers, R=N+I-1 ,according to R Non-repeating natural numbers are randomly arranged to generate multiple chromosomes; S42. Screening from multiple chromosomes based on constraint functions S Valid chromosomes form a chromosome population and set the number of iterations; S43, performing fast non-dominated sorting and crowding calculation on each chromosome according to the objective function, obtaining the fast non-dominated sorting and crowding of each chromosome, sorting each chromosome according to the fast non-dominated sorting and crowding of each chromosome, and selecting the top s Chromosomes as excellent individuals; S44, performing crossover and mutation operations on the excellent individuals to generate multiple first child individuals, and randomly generating multiple second child individuals, deleting duplicated child individuals, and screening the first child individuals and the second child individuals according to the constraint function to obtain valid child individuals, and adding the valid child individuals to the chromosome population to obtain the next generation chromosome population; S45. Repeat steps S43-S44 until the number of iterations is met, and decode the excellent individuals to form an excellent logistics solution set.

8. A low-carbon supply chain management method according to claim 7, characterized in that: The number of valid sub-individuals is S, the first sub-individuals account for 90% of the valid sub-individuals, and the second sub-individuals account for 10% of the valid sub-individuals.

9. A low-carbon supply chain management method according to claim 1, characterized in that: Step S5 includes: S51, analyzing the objective function value of each excellent individual in the set of excellent logistics solutions to form an objective function value matrix of the excellent individuals; S52, correcting the obtained objective function weight matrix according to the total objective function mean of the excellent individuals in the excellent logistics solution set and the mean of each objective function of the excellent individuals in the excellent logistics solution set; S53. Calculate the optimal solution value of the excellent individuals according to the corrected objective function weight matrix and the objective function value matrix of the excellent individuals, and select the excellent individual decoding with the largest optimal solution value as the optimal logistics solution.

10. A low-carbon supply chain management method according to claim 9, characterized in that: The optimal solution value of the excellent individual is: ； Among them, β r For the r The optimal solution value of excellent individuals, [ f r1 ,f r2 ,f r3 ] is the first r The objective function value matrix of excellent individuals, f r1 ,f r2 ,f r3 For the r The objective function value of an excellent individual, [ w1 , w2 , w3 ] is the objective function weight matrix, w1 , w2 , w3 is the objective function f r1 ,f r2 ,f r3 The weight value of is the mean of the total objective function of the excellent individuals in the set of excellent logistics solutions, is the mean of the objective functions of the excellent individuals in the set of excellent logistics solutions.

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