Comprehensive optimization design method for low-carbon cold chain distribution network based on hybrid motorcade

Abstract

The invention provides a low-carbon cold chain distribution network comprehensive optimization design method based on a hybrid motorcade, and the method comprises the steps: making a low-carbon cold chain distribution network optimization design framework from the perspective of global optimization, and achieving the comprehensive design scheme of the hybrid motorcade, a distribution route, a distribution center, a charging plan and customer service time. Diversified replacement requirements of current cold-chain logistics enterprises are met; under the mutual coordination effect of carbon emission reduction policies (including battery technology, carbon quota, carbon tax, subsidy incentive and the like), from the economic perspective of cost saving of logistics enterprises and the service perspective of customer satisfaction improvement, the carbon emission of vehicles and the carbon emission of refrigeration equipment are considered at the same time; a low-carbon cold-chain distribution network comprehensive optimization model with the minimum total operation cost as the design target is provided, and by accurately analyzing the influence of implementation of all strategies on cold-chain distribution network optimization, the steps of emission reduction, consumption reduction and low-carbon transformation of the cold-chain logistics industry are accelerated.

Description

Technical Field

[0001] The present application relates to the field of cold chain distribution, and in particular to a method for comprehensive optimization design of a low-carbon cold chain distribution network based on a mixed fleet. Background Art

[0002] At present, the research on low-carbon optimization of cold chain logistics by using pure electric refrigerated trucks is relatively new at home and abroad, and there are relatively few existing achievements in the optimization of cold chain distribution networks. Most of these studies analyze cold chain distribution route planning, refrigerated truck type selection, charging station site selection and distribution center site selection as separate problems, and do not formulate a low-carbon cold chain distribution network comprehensive optimization design framework considering mixed fleets from the perspective of global optimization. From the perspective of theoretical model, when constructing the calculation objectives of the low-carbon cold chain distribution network optimization design problem, the existing research not only fails to consider the carbon emissions generated during the distribution process of refrigerated trucks and the refrigeration process of the cold storage of the distribution center, but also fails to comprehensively consider the optimization research of cold chain logistics networks under the coordinated effect of different carbon emission reduction policies. From the perspective of computational solution, the calculation methods proposed by existing studies in solving the low-carbon cold chain distribution network optimization design problem have not been proven to be applicable to solving the actual scale cold chain distribution network optimization design problem, and the calculation advantages are not yet clear.

[0003] Existing studies on low-carbon cold chain logistics networks mainly consider three aspects. First, the carbon emission cost of traditional fuel vehicles is introduced. For example, Leng et al., Song et al., Zhang et al., Fang Wenting et al., and Ren Teng et al., when conducting research on cold chain logistics network optimization, converted the carbon emission factors affecting cold chain logistics into carbon emission costs and included them in the operating costs, providing a reference for the low-carbon transformation and development of cold chain logistics companies. However, the above studies only considered the carbon emissions of fuel vehicles, and did not consider the implementation of government carbon emission policies. Second, consider the use of pure electric refrigerated trucks. For example, Feng Jie and Shi Li established a fresh product distribution route optimization model based on pure electric refrigerated trucks. The results show that compared with traditional refrigerated trucks, pure electric refrigerated trucks can reduce greenhouse gas emissions and enjoy government subsidies, which is more in line with the needs of suppliers. Wang Jiayue and Shi Li established a pure electric refrigerated truck route optimization model, and obtained the optimal charging strategy for pure electric refrigerated trucks by analyzing the various costs of different charging upper limits. Zhao et al. established a fresh product distribution route optimization model in the urban cold chain with the goal of minimizing the sum of economic costs and fresh goods loss costs. Gan Junwei et al. considered energy consumption, time window and product freshness in a coordinated manner, and constructed a pure electric refrigerated truck route problem model in two stages. The third is to combine carbon emission reduction policies. For example, Wang et al. established a low-carbon cold chain site selection path problem model and introduced carbon tax policies. By analyzing the impact of carbon tax on total cost and carbon emissions, it was proved that the implementation of carbon tax policies can effectively reduce carbon emissions in cold chain logistics networks. Ge Xianlong et al. established a multi-model vehicle path model with time window considering the new carbon emission mechanism, and used a hybrid genetic algorithm to solve it. The results showed that the fluctuation of relevant parameters in the carbon trading mechanism cannot be ignored in the impact of logistics distribution costs. Zhang Siying et al. constructed a two-layer low-carbon cold chain logistics distribution system optimization decision model. The upper model starts from the perspective of the cold chain logistics department, with the unit new energy refrigerated truck purchase subsidy rate and carbon quota as decision variables, and the lower model starts from the perspective of cold chain logistics enterprises, with the minimum total cost as the goal. The results show that subsidy policies and carbon emission rights trading policies can achieve corporate carbon emission reduction better than implementing either one alone. Recently, Chen et al. proposed a multi-distribution center and multi-model urban cold chain vehicle routing problem with time windows for the mixed delivery scenario of pure electric vehicles and fuel vehicles, and proposed an improved variable neighborhood search algorithm to solve the problem, but did not consider the location problem of charging stations. Summary of the invention

[0004] An embodiment of the present invention provides a method for comprehensive optimization design of a low-carbon cold chain distribution network based on a mixed fleet, which is used to solve the problems existing in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A low-carbon cold chain distribution network optimization method based on a mixed fleet is provided, the method comprising the steps of:

[0007] S1 constructs the objective function of minimizing the total operating cost from the economic perspective of cost savings for logistics enterprises and the service perspective of improving customer satisfaction, taking into account both the carbon emissions of the vehicle itself and the carbon emissions of the refrigeration equipment;

[0008] S2 is based on the objective function and combined with constraints to establish a comprehensive optimization design model for low-carbon cold chain distribution network;

[0009] S3 uses the artificial fish swarm algorithm to solve the comprehensive optimization design model of the low-carbon cold chain distribution network. First, according to the decision variables of the comprehensive optimization design model of the low-carbon cold chain distribution network, the initial cold chain distribution network design scheme is obtained;

[0010] Based on the current cold chain distribution network design, S4 executes fish school behavior and removes and inserts operators to update the service customer sequence and refrigerated truck type of each distribution route, as well as the construction location and number of distribution centers in the entire network;

[0011] S5 executes the charging station location optimization program to update the construction location and number of charging stations in the entire network;

[0012] S6 evaluates the solution of the comprehensive optimization design model of the low-carbon cold chain distribution network based on the objective function of minimizing the total operating cost, and obtains an optimized low-carbon cold chain distribution network design solution;

[0013] S7 determines whether the iteration of the artificial fish swarm algorithm reaches a preset termination condition. If so, the low-carbon cold chain distribution network optimization design model is solved and the optimal low-carbon cold chain distribution network design solution is output; otherwise, step S4 is executed.

[0014] It can be seen from the technical solutions provided by the embodiments of the present invention that the method for comprehensive optimization design of a low-carbon cold chain distribution network based on a mixed fleet provided by the present invention is based on the goal of comprehensive optimization design of a low-carbon cold chain distribution network, with cold chain distribution path planning, mixed fleet configuration, charging station site selection and distribution center site selection as the entry point. The present invention will apply modeling optimization means for optimization design to achieve the overall design process. Under the mutual coordination of carbon emission reduction policies (including battery technology, carbon quotas, carbon taxes, subsidy incentives, etc.), from the economic perspective of cost savings for logistics companies and the service perspective of improving customer satisfaction, considering the carbon emissions of the vehicle itself and the carbon emissions of refrigeration equipment, a low-carbon cold chain logistics network comprehensive optimization model with the minimum total operating cost as the design goal is proposed, and given constraints such as paths, distribution centers, charging, energy consumption, and time windows. Its main purpose is to obtain reasonable refrigerated vehicle types, distribution routes, the location and number of distribution centers, the location and number of charging stations, the charging plan of pure electric refrigerated vehicles, and customer service time. In addition, the present invention proposes an artificial fish swarm intelligence algorithm to solve the problem of optimizing the design of a low-carbon cold chain distribution network, and introduces the removal and insertion operators in the adaptive large neighborhood search algorithm into the algorithm to avoid the shortcomings of slow convergence speed and poor fish swarm diversity in the later stage of the algorithm. The present invention can serve as a theoretical method basis for the optimization design of urban cold chain logistics distribution networks, and can provide an effective reference for the engineering practice of optimizing the design of urban cold chain logistics distribution networks.

[0015] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 It is a processing flow chart of the method for comprehensive optimization design of low-carbon cold chain distribution network based on mixed fleet provided by the present invention;

[0019] Figure 2 It is a processing flow chart of a preferred embodiment of the method for comprehensive optimization design of a low-carbon cold chain distribution network based on a mixed fleet provided by the present invention;

[0020] Figure 3It is a schematic diagram showing the state of an artificial fish in the method for comprehensive optimization design of a low-carbon cold chain distribution network based on a mixed fleet provided by the present invention;

[0021] Figure 4 It is a schematic diagram of the artificial fish advancing rule of the method for comprehensive optimization design of a low-carbon cold chain distribution network based on a mixed fleet provided by the present invention. DETAILED DESCRIPTION

[0022] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0023] The present invention provides a method for comprehensive optimization design of a low-carbon cold chain distribution network based on a mixed fleet, aiming to solve the following problems existing in the prior art:

[0024] There are few studies on the optimization design of low-carbon cold chain distribution networks, and most of these studies analyze cold chain distribution route planning, refrigerated truck type site selection, charging station site selection, and distribution center site selection as separate issues. Although some studies have simultaneously conducted optimization design studies on cold chain distribution route planning based on mixed vehicles and distribution center site selection, they have not considered the optimization of charging station site selection, making it difficult for the optimization design of low-carbon cold chain distribution networks to meet the diverse replacement needs of current cold chain logistics companies, and thus difficult to achieve the overall optimal design solution.

[0025] Although existing studies have conducted research on the optimization of low-carbon cold chain logistics networks combined with carbon quotas, carbon taxes or subsidy policies when establishing design goals for the optimization design of low-carbon cold chain distribution networks, they have not comprehensively considered the optimization of cold chain logistics networks under the coordinated effects of different carbon emission reduction policies, and have not simultaneously considered the carbon emissions generated during the distribution process of refrigerated trucks and the refrigeration process of cold storage in distribution centers.

[0026] Since it includes four sub-problems, namely cold chain distribution path planning, mixed fleet configuration, charging station location selection and distribution center location selection, the low-carbon cold chain distribution network optimization design problem is discrete, nonlinear, non-convex and combinatorially complex, which makes the analytical quantification of the overall problem difficult. In addition, the accuracy of the solution to this problem is high, so an efficient and reasonable calculation method is needed to solve it. Although existing studies have proposed heuristic algorithms such as ant colony optimization and particle swarm optimization algorithms to solve the low-carbon cold chain distribution network optimization design problem, these algorithms have not been proven to be able to be used to solve the actual scale of low-carbon cold chain distribution network optimization design problems, and their calculation advantages are not yet clear. As a swarm intelligence heuristic optimization algorithm proposed in recent years, the artificial fish swarm algorithm has a strong ability to jump out of local extreme values, has low requirements on initial values ​​and has the advantages of fast optimization speed. It has been successfully applied to solve complex practical problems in various fields, but has not yet been applied to solve the pure electric bus line network optimization design problem.

[0027] See also Figure 1 and 2 The method for comprehensive optimization design of a low-carbon cold chain distribution network based on a mixed fleet provided by the present invention comprises:

[0028] S1 constructs the objective function of minimizing the total operating cost from the economic perspective of cost savings for logistics enterprises and the service perspective of improving customer satisfaction, taking into account both the carbon emissions of the vehicle itself and the carbon emissions of the refrigeration equipment;

[0029] S2 is based on the objective function and combined with constraints to establish a comprehensive optimization design model for low-carbon cold chain distribution network;

[0030] S3 uses the artificial fish swarm algorithm to solve the comprehensive optimization design model of the low-carbon cold chain distribution network. First, according to the decision variables of the comprehensive optimization design model of the low-carbon cold chain distribution network, the initial cold chain distribution network design scheme is obtained;

[0031] Based on the current cold chain distribution network design, S4 executes fish school behavior and removes and inserts operators to update the service customer sequence and refrigerated truck type of each distribution route, as well as the construction location and number of distribution centers in the entire network;

[0032] S5 executes the charging station location optimization program to update the construction location and number of charging stations in the entire network;

[0033] S6 evaluates the solution of the comprehensive optimization design model of the low-carbon cold chain distribution network based on the objective function of minimizing the total operating cost, and obtains an optimized low-carbon cold chain distribution network design solution;

[0034] S7 determines whether the iteration of the artificial fish swarm algorithm reaches a preset termination condition. If so, the low-carbon cold chain distribution network optimization design model is solved and the optimal low-carbon cold chain distribution network design solution is output; otherwise, step S4 is executed.

[0035] In the embodiment provided by the present invention, the refrigerated truck operation data includes historical operation data and condition data set to achieve a predetermined goal. The above step S1 specifically includes:

[0036] The comprehensive optimization problem of cold chain logistics network with soft time windows for multiple models under the synergy of carbon emission reduction policies is studied. The specific description of the proposed optimization problem is as follows: define a completely directed graph Ω = (V, E), where the point set V contains the customer set C, the distribution center set D and the charging station set S, and E is the edge set. The cargo demand of customer i is d i , service time is st i The soft time window required by the customer to arrive is [e i ,l i ], where e i and l i are respectively the earliest and latest allowed service times for customer i, that is, the vehicle must pay the corresponding waiting or delay penalty fee if it arrives outside this time period. The needs, location and soft time window of each customer are known in advance. The refrigerated truck set K includes a set of traditional refrigerated trucks and a set of pure electric refrigerated trucks. All refrigerated trucks have the same maximum cargo load CQ. And only one type of cold chain product is considered for delivery. Each refrigerated truck only serves one delivery route; each delivery route is only served by one type of refrigerated truck. Each customer can only be served once. The battery capacity of the pure electric refrigerated truck is BQ. Subject to the range constraint, if charging is required during the delivery process, the pure electric refrigerated truck can choose any charging station s∈S for charging, and the charging time is ct s . Each pure electric refrigerated truck starts charging immediately upon arrival at the charging station and does not consume power while serving customers. Each charging station can be visited multiple times or not at all as needed. And the charging rate of all charging stations is assumed to be the same. During the delivery process, the temperature in the refrigerated compartment remains unchanged. At the beginning of the delivery work, refrigerated truck k leaves distribution center j to serve customers and arrives at its specified time window DTW j The maximum number of pure electric refrigerated trucks and traditional refrigerated trucks in the distribution center are MV EV and MV CV Logistics companies choose to use the distribution centers and charging stations that are planned and deployed by government departments in advance. This study does not consider traffic congestion, that is, the roads are unobstructed during the distribution period.

[0037] The objective function of minimizing the total operating cost includes:

[0038] minZ=min(C1+C2+C3+C4+C5+C6+C7+C8) (1)

[0039] In the formula, C1 is the fixed cost of the distribution center, C2 is the vehicle purchase cost, C3 is the refrigeration cost, C4 is the cargo damage cost, C5 is the time penalty cost, C6 is the energy consumption cost, C7 is the charging cost, and C8 is the carbon emission cost;

[0040] Formula C1=∑ i∈D hc i ×y i (2) To calculate the fixed operating costs of the distribution center;

[0041] In the formula, y j 1 means that distribution center j is selected, otherwise it is 0; hc i is the fixed cost of distribution center i converted into one day (yuan / day).

[0042] formula Used to calculate vehicle purchase costs, including traditional vehicle purchase costs and pure electric refrigerated truck purchase costs; considering the government's subsidy incentive policy for pure electric refrigerated trucks, the purchase cost of pure electric refrigerated trucks is equal to the purchase cost of pure electric refrigerated trucks minus the government's subsidy cost;

[0043] In the formula, 1 means that the pure electric refrigerated truck passes through arc (i, j), otherwise it is 0; 1 means the conventional refrigerated truck passes through arc (i, j), otherwise 0; pc CV is the purchase cost of a traditional refrigerated truck converted into one day (yuan / vehicle / day), pc EV is the purchase cost of pure electric refrigerated trucks converted into one day (yuan / vehicle / day), sc EV It is the government subsidy cost per day for a pure electric refrigerated truck (yuan / vehicle / day).

[0044] formula Used to calculate the refrigeration cost during refrigerated truck delivery, usually the cost of refrigerant consumed to maintain the temperature in the truck compartment;

[0045] In the formula, rc1 is the refrigeration cost during transportation (yuan / h), rc2 is the refrigeration cost during unloading (yuan / h), t ij is the travel time (h) of the refrigerated truck between (i, j).

[0046] formula

[0047] To calculate the cost of cargo damage, including the cost of damage during transportation and the cost of damage during the opening process when the goods are delivered to the customer;

[0048] In the formula, p is the unit price of cold chain products (yuan / kg), α is the deterioration rate of cold chain products during transportation, β is the deterioration rate of cold chain products during unloading, Φ i is the set of customers that the refrigerated truck has served before serving customer i;

[0049] Formula C5 = ∑ i∈C (wc×max{0,e i -at i}+pc×max{0,at i -l i})(6) used to calculate time penalty costs, which refers to the additional costs incurred due to failure to meet the customer's time window, including the waiting cost of the refrigerated truck arriving at the customer before the expected time and the delay cost of arriving at the customer after the expected time;

[0050] In the formula, at i is the time when the refrigerated truck arrives at point i, wc is the waiting cost rate (yuan / h) caused by the refrigerated truck arriving at the customer point in advance, and pc is the delay cost rate (yuan / h) caused by the refrigerated truck arriving at the customer point late.

[0051] formula

[0052]

[0053] and To calculate energy consumption costs, including the fuel consumption costs of traditional refrigerated trucks when driving and cooling, and the electricity consumption costs of pure electric refrigerated trucks when driving and cooling;

[0054] In the formula, c e is the electricity price (yuan / kWh), c f is the oil price (yuan / L), v is the driving speed (km / h), ρ0 is the fuel consumption per unit distance when the vehicle is unloaded (L / km), ρ * is the fuel consumption per unit distance when the vehicle is fully loaded (L / km), CQ ij is the cargo load of the refrigerated truck between (i, j) (kg), M is the empty mass of the refrigerated truck (kg), α ij is the slope, g is the gravitational constant (km / h 2 ), c r is the rolling friction coefficient, c d is the air resistance coefficient, ρ is the air density (kg / km 3 ), A is the front (km 2 ), λ ele is the regression coefficient, η ele is the energy efficiency of pure electric refrigerated truck, η fuelis the combustion thermal efficiency of the traditional refrigerated truck engine, and μ is the refrigeration equipment power of the refrigerated truck (kW).

[0055] The present invention establishes a comprehensive optimization design model for a low-carbon cold chain distribution network with the minimum total operating cost as the design goal. The model is established by combining the above objective function with constraints. In the embodiment provided by the present invention, the constraints include distribution path constraints, distribution center constraints and charging plan constraints.

[0056] The delivery path constraints include:

[0057] pass Ensure that each customer is served once; flow conservation constraint and

[0058] pass and Ensure that each customer is delivered by only one refrigerated truck;

[0059] Load constraints for refrigerated trucks and

[0060] Time window constraints and

[0061] Service time relationship between customers

[0062] Where Ψ is a sufficiently large number;

[0063] The distribution center constraints include:

[0064] Hard time window constraints of distribution centers

[0065] pass and Ensure that each refrigerated truck departs from the distribution center and arrives at the customer point where delivery is required, and returns to the same distribution center after completing the delivery work;

[0066] pass Ensure that the total customer demand assigned to a distribution center does not exceed the capacity of the distribution center;

[0067] pass Ensure that each customer must be served by one and only one distribution center;

[0068] pass and (29) Ensure that if a distribution center is selected, it serves at least one customer;

[0069] pass and Ensure that the number of vehicles sent out from the distribution center is equal to the number of vehicles returning to the distribution center, and the total number is no greater than the maximum number of vehicles served by the distribution center;

[0070] The charging plan constraints include:

[0071] Logical relationship constraints between charging stations and charging activities and In the formula is the battery capacity of the pure electric refrigerated truck when it leaves point i (kWh);

[0072] pass Ensure that the time when the pure electric refrigerated truck arrives at point j is no earlier than the time when it leaves the charging station s plus the driving time during transportation;

[0073] pass Ensure that each pure electric refrigerated truck has enough power to visit the remaining customer points and charging stations and return to the distribution center, where is the battery capacity of the pure electric refrigerated truck when it arrives at point i (kWh), h cr is the power consumption rate, which is assumed to be proportional to the driving distance;

[0074] pass Ensure that the pure electric refrigerated truck is fully charged when it leaves the distribution center;

[0075] pass Ensure that the load level of the battery must always be positive and not allowed to overcharge.

[0076] Combined with the constructed objective function of minimizing the total operating cost and the distribution path constraints, distribution center constraints and charging plan constraints, the comprehensive optimization design model of the low-carbon cold chain distribution network is finally established.

[0077] In the embodiment provided by the present invention, the comprehensive optimization design model of the low-carbon cold chain distribution network is constructed based on the artificial fish swarm intelligent algorithm. The algorithm was proposed by Li Xiaolei et al. in 2002. Its basic idea comes from imitating the natural behavior of fish foraging activities. It can speed up the optimization speed by executing each fish swarm behavior in parallel, and the initial solution requirement is not high. However, the artificial fish swarm intelligent algorithm is developed for continuous optimization problems, and the proposed cold chain logistics network comprehensive optimization problem has discreteness, nonlinearity, non-convexity and combinatorial complexity. In addition, the artificial fish swarm intelligent algorithm has the disadvantages of poor diversity of fish swarms and slow convergence speed in the later stage of the algorithm. In this regard, the present invention proposes an improved artificial fish swarm intelligent algorithm, that is, the encoding method, initialization method and fish swarm behavior in the proposed algorithm are discretized, and at the same time, the removal and insertion operators in the adaptive large neighborhood search algorithm are introduced into the algorithm, that is, when the value recorded on the bulletin board does not change after a given number of iterations, the removal and insertion operators will be executed to increase the diversity of artificial fish individuals, thereby accelerating the convergence speed of the algorithm.

[0078] Figure 2 The process of solving the comprehensive optimization design model of the low-carbon cold chain distribution network, obtaining the integrated result of the low-carbon cold chain distribution network, and optimizing the integrated result in some preferred embodiments is shown, including the following sub-steps:

[0079] S31 sets algorithm parameters for solving the comprehensive optimization design model of the low-carbon cold chain distribution network;

[0080] S32 constructs an initial artificial fish school based on the algorithm parameters and the decision variables of the comprehensive optimization design model of the low-carbon cold chain distribution network.

[0081] Furthermore, in sub-step S31, the algorithm parameters include: fish school size N Pop , maximum number of iterations Perception distance Visual, crowding factor δ, number of attempts try_N, maximum number of iterations when the bulletin board does not change Probability p c .

[0082] Sub-step S32 includes initializing the solution of the low-carbon cold chain distribution network comprehensive optimization design model, specifically: each artificial fish individual represents a complete solution, that is, it contains a set of distribution routes, and each route is a complete travel route, that is, it is equipped with the same distribution center at both ends. In addition, in order to improve the solution efficiency of the proposed algorithm, the encoding method of the Block is designed to consider the problem characteristics of different types of refrigerated trucks. Each Block contains the type of service refrigerated truck, the corresponding service route (that is, the service customer sequence), the distribution center and the charging station, such as Figure 3 shown.

[0083] First, each customer is assigned to the distribution center closest to him / her according to the distance from nearest to farthest; if the capacity of the distribution center reaches the upper limit, the customer is assigned to the distribution center with the next closest distance, and so on. d To store the customers assigned to distribution center d; then, select a distribution center d, assuming that the customer is first served by a pure electric refrigerated truck, and thus construct a list d | delivery routes and put them into the list table EV,r Then, based on the insertion heuristic algorithm proposed by Solomon, the list is merged EV,r The delivery routes in the list d Select seed client i * (i.e. the customer with the earliest allowed service time) and delete it, then remove it from the list d Select customer c and insert the current seed customer i * The best feasible insertion position is the position that minimizes the weighted combination of distance and time insertion, see Where α1, α2 and α3 are non-negative weight values, α1+α2+α3=1, is the new arrival time at point j. If the route after inserting customer c meets the vehicle capacity limit, then d Delete customer c and check whether the newly constructed delivery route meets the range constraint. If not, select a charging station from the charging station set S and determine its optimal feasible insertion position based on formula (38). If it meets the constraint, continue to select a charging station from the list d Select Insert Customer until list d The new delivery route is placed in the table f_list. EV,r If the delivery route after inserting customer c violates the vehicle capacity limit, the insertion is stopped and the newly constructed delivery route is put into the table f_list EV,r Then from the list d Reselect a seed customer to merge routes until the list d is an empty set; then, based on formula (1), f_list is calculated EV,r The total cost of each delivery route in f_list is calculated, and the delivery routes are sorted from large to small, and then the delivery routes are sorted from f_list EV,r Select the first | f_list EV,r | / 2 and convert them into delivery routes served by traditional refrigerated trucks; finally, copy the initial solution generated in the above steps to N Pop Thus, the initial artificial fish swarm is formed, and the number of iterations is n Gen =1,n noc =1.

[0084] In this embodiment, the low-carbon cold chain distribution network layout plan includes distribution path and design and distribution center location. By executing fish school behavior and removal and insertion operators, the service customer sequence and refrigerated truck type of each distribution path, as well as the construction location and number of distribution centers in the entire network are updated.

[0085] The execution process of the school behavior includes:

[0086] The best artificial fish individual in the fish swarm is retained, and then N Pop Random selection, each time select a worker fish individual AFS a The improved foraging and chasing behaviors are performed sequentially to achieve the optimal design of delivery routes, distribution centers and refrigerated truck types; when two artificial fish individuals AFS a and AFS b When the distance between (see formula (39)) is greater than Visual, it is called AFS b Located in AFS a The domain set N(AFS a ) within the artificial fish individuals;

[0087] (1) Improved foraging behavior

[0088] Foraging behavior refers to AFS a In N(AFS a ) selects a better artificial fish individual AFS through try_N random searches b The operator for learning; the execution rules of the improved foraging behavior are as follows Figure 3 As shown, specifically: First, in AFS a and AFS b A learning point is randomly generated in AFS b Blocks after the mid-learning point replaces AFS a Blocks after the learning point in the learning process are generated, thereby generating a child solution; then the duplicated service customers are deleted from the child solution; then it is checked whether the child solution contains all the customers in the network at this time, and the missing customers, i.e., 2 and 9, are reinserted into the child solution according to the minimum insertion cost calculated by formula (40); finally, the child artificial fish individual AFS is obtained. off ; In addition, if the best feasible insertion location cannot be found, a new delivery route will be opened, and it is stipulated that this route is served by pure electric refrigerated trucks, and they are allocated to distribution centers in order of distance from short to long, while taking into account the capacity constraints of the distribution centers; Formula (40) not only considers the additional distance and time, but also considers the energy consumption cost, charging cost and carbon emission cost of the delivery route after inserting the new customer point, as well as the capacity constraint and range constraint.

[0089]

[0090] Where N C is the number of customers in a solution, R is the maximum number of vehicles in the system, c k is a customer point on a delivery route, sc a,r For AFS a Customer service sequence AFS on route r a , sc a,r =sc b,r′ Indicates that they belong to AFS a and AFS b The two routes r and r′ are exactly the same, η∈[0,1], s w is the battery effectiveness at point w on route r, that is, if but Otherwise w =0,W r and W r ′ are the point sets on route r before and after the point is inserted, C r The set of customers on route r after point insertion.

[0091] (2) Improved rear-end behavior

[0092] Rear-end collision is a kind of N (AFS a ) in the optimal artificial fish individual AFS best The learned operator; the improved execution rule of the rear-end collision behavior is: if AFS best With a high food concentration and a less crowded environment, AFS a Based on the probability p c AFS best Move, based on probability (1-p c ) to N(AFS a )A random solution AFS b A step forward; specifically, first in AFS best (AFS b ) and AFS a The same distribution center is identified in the network, and the routes served by the distribution center are exchanged to generate a sub-solution; the duplicated customers are then deleted from the sub-solution; then, it is checked whether the sub-solution contains all the customers in the network, and the missing customers are reinserted into the sub-solution according to the minimum insertion cost calculated by formula (40), and finally the sub-solution AFS is obtained. off Similarly, if the best feasible insertion position cannot be found, a new delivery route will be opened; with probability value pc and N (AFS a ) to avoid embedding the local optimal solution;c ) and N(AFS a ) to learn any solution, which can enhance the algorithm's exploration ability;

[0093] The execution process of the remove and insert operators includes:

[0094] like Execute S5. Repeat the following sub-steps until N Pop / 2 times, record the new artificial fish state, and reset n noc =1, then execute S5.

[0095] Firstly, the best artificial fish individual in the artificial fish swarm is retained, and an artificial fish individual is randomly selected from the remaining artificial fish;

[0096] Then, the removal operator is executed, that is, the customer removal operator, distribution center removal operator and charging station removal operator are selected based on the roulette selection mechanism. The customer removal operator refers to the removal of the customer points that have certain correlation with other customer points according to certain rules. customers, where μ1, μ2 and μ3 are all random numbers, and μ1, μ2∈(0,1), μ1≤μ2, μ3∈[0,1]. First, a customer is randomly removed; then, in each iteration, a customer is randomly selected from the removed customers, and the customer is randomly selected according to Calculate the correlation R(i,j) between all customers that have not been removed and this customer, and sort them in ascending order; then remove the first customer in the list. customers, where size is the list size, χ shaw The distribution center removal operator randomly selects an open distribution center and closes it, and then deletes all customers it serves. The charging station removal operator removes the charging station with the largest contribution value. This is done by removing routes that visit more charging stations and fewer customers until N r Customers were deleted, of which, and are the number of customers and charging stations on route r, respectively, and τ is a predefined parameter.

[0097] Then, the insert operator is executed, that is, the customers deleted in the above remove operator are reinserted into the AFS based on the minimum insertion cost calculated by formula (40) a middle.

[0098] In this embodiment, the low-carbon cold chain distribution network layout plan also involves a charging station site selection optimization program to update the construction location and number of charging stations in the entire network, which is specifically implemented through the following sub-steps:

[0099] S51: Let n Pop= 1, select an artificial fish individual from the fish school, then extract the route set R served by the pure electric refrigerated truck from the selected artificial fish individual, and create an empty route set R * .

[0100] S52: Select a route r∈R and delete the charging stations contained in r while keeping the service order of customers unchanged. Let r be the current optimal route r * , and create a route set TS, and put r into TS. Let R|=|R|-1.

[0101] S53: Select and delete a route r' in TS. Calculate the remaining battery power E at each point in r' w , and let E min =min{E w}. If E min <0, then r′ is invalid, identify the first point w on r′ that cannot be served by a pure electric refrigerated truck * Then, based on the minimum insertion cost, find the appropriate charging station insertion location from CIL, where CIL is point w * The previous candidate insertion position set. Then let r″←r′, and put r″ into TS. If E min ≥ 0, the label-based comparison mechanism compares the label (fs′, tc′, ns′, vb′, fl′) of route r′ with the current optimal route r * Tags (fs * ,tc * ,ns * ,vb * ,fl * ); where fs′ is a 0-1 variable, 1 indicates that the delivery route is valid, otherwise it is 0, tc′ is the travel cost, ns′ is the number of charging stations on the delivery route, vb′ is the amount of battery level violated, and fl′ is the location of the first customer point on the delivery route that violates the battery range constraint; if (fs′, tc′, ns′, vb′, fl′) is better than (fs * ,tc * ,ns * ,vb * ,fl * ), then let r * ←r′.

[0102] S54: If TS is a non-empty set, execute S53; otherwise, * Put in R * , execute S55.

[0103] S55: If R|=0, generate a new delivery route set R * , otherwise execute S52.

[0104] S56: If n Pop <N Pop , return to step 7-1; if n Pop =N Pop , the iteration ends, and the distribution route set R after all solutions for the fish school are completed and the charging station selection is optimized is output * , go to S53.

[0105] In the embodiment provided by the present invention, the above step S6 specifically includes:

[0106] The food concentration of each artificial fish is calculated based on formula (1) and compared with the value recorded in the bulletin board. If it is better, the value in the bulletin board is updated to the new artificial fish state, and n is reset. noc =1; otherwise, keep the value in the bulletin board and record n noc =n noc +1. Since the established objective function is a minimum problem, the higher the food concentration of the artificial fish, the lower the corresponding objective function value.

[0107] In the embodiment provided by the present invention, the above step S7 specifically includes:

[0108] like Let n Gen =n Gen +1, execute S4; otherwise, terminate the iteration, terminate the solution of the low-carbon cold chain distribution network optimization design model, and output the optimal low-carbon cold chain distribution network design solution.

[0109] Through the continuous optimization of the low-carbon cold chain distribution network results by the above-mentioned artificial fish swarm solving algorithm, a reasonable cold chain distribution route layout, mixed fleet configuration, and the construction location and number of distribution centers and charging stations are obtained, which reduces the operating costs of cold chain logistics companies while meeting the diverse replacement needs of current cold chain logistics companies.

[0110] In summary, the present invention provides a low-carbon cold chain distribution network optimization method based on a mixed fleet. Most of the existing studies analyze cold chain distribution path planning, refrigerated truck type selection, charging station site selection and distribution center site selection as separate problems, and do not start from the perspective of global optimization. A low-carbon cold chain distribution network comprehensive optimization design framework considering mixed fleets is formulated to achieve a comprehensive design plan for cold chain distribution path layout, mixed fleet configuration, distribution center and charging site selection. In view of the fact that existing studies have not comprehensively considered the optimization of cold chain logistics networks under the mutual coordination of different carbon emission reduction policies, and have not simultaneously considered the current situation of carbon emissions generated during the distribution process of refrigerated trucks and the refrigeration process of cold storage in distribution centers, under the mutual coordination of different carbon emission reduction policies, from the economic perspective of cost savings for logistics companies and the service perspective of improving customer satisfaction, the carbon emissions of the vehicles themselves and the carbon emissions of refrigeration equipment are considered at the same time, and a low-carbon cold chain distribution network comprehensive optimization model with the minimum total operating cost as the design goal is proposed. By accurately analyzing the impact of the implementation of each strategy on the optimization of the cold chain distribution network, the pace of emission reduction and consumption reduction and low-carbon transformation of the cold chain logistics industry can be accelerated.

[0111] In view of the current situation that there is a lack of efficient and reasonable solutions to the optimization design problem of low-carbon cold chain distribution networks in existing research, an artificial fish swarm intelligence algorithm is proposed to solve this problem. The removal and insertion operators in the adaptive large neighborhood search algorithm are introduced into the algorithm to avoid the shortcomings of slower convergence speed and poor fish swarm diversity in the later stage of the algorithm.

[0112] The present invention introduces a new low-carbon cold chain distribution network comprehensive optimization design framework to realize the comprehensive design of cold chain distribution route planning, mixed fleet configuration, charging station site selection and distribution center site selection, effectively reducing the operating costs of cold chain logistics companies; and analyzes the theoretical methodological basis for realizing the vehicle replacement needs of cold chain logistics companies under the mutual coordination of carbon emission reduction policies, so as to accelerate the pace of emission reduction and consumption reduction and low-carbon transformation of the cold chain logistics industry.

[0113] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0114] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0115] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0116] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A low-carbon cold chain distribution network optimization method based on a mixed fleet, characterized in that: The method comprises the steps of: S1 constructs the objective function of minimizing the total operating cost from the economic perspective of cost savings for logistics enterprises and the service perspective of improving customer satisfaction, taking into account both the carbon emissions of the vehicle itself and the carbon emissions of the refrigeration equipment; S2 is based on the objective function and combined with constraints to establish a comprehensive optimization design model for low-carbon cold chain distribution network; S3 uses the artificial fish swarm algorithm to solve the comprehensive optimization design model of the low-carbon cold chain distribution network. First, according to the decision variables of the comprehensive optimization design model of the low-carbon cold chain distribution network, the initial cold chain distribution network design scheme is obtained; Based on the current cold chain distribution network design, S4 executes fish school behavior and removes and inserts operators to update the service customer sequence and refrigerated truck type of each distribution route, as well as the construction location and number of distribution centers in the entire network; S5 executes the charging station location optimization program to update the construction location and number of charging stations in the entire network; S6 evaluates the solution of the comprehensive optimization design model of the low-carbon cold chain distribution network based on the objective function of minimizing the total operating cost, and obtains an optimized low-carbon cold chain distribution network design solution; S7 determines whether the iteration of the artificial fish swarm algorithm reaches a preset termination condition. If so, the low-carbon cold chain distribution network optimization design model is solved and the optimal low-carbon cold chain distribution network design solution is output; otherwise, step S4 is executed.

2. The low-carbon cold chain distribution network optimization method based on mixed fleet according to claim 1 is characterized in that: From the economic perspective of cost savings for logistics enterprises and the service perspective of improving customer satisfaction, taking into account both the carbon emissions of the vehicle itself and the carbon emissions of the refrigeration equipment, the objective function of minimizing the total operating cost includes the following steps: The objective function of minimizing the total operating cost includes: minZ=min(C1+C2+C3+C4+C5+C6+C7+C8) (1) In the formula, C1 is the fixed cost of the distribution center, C2 is the vehicle purchase cost, C3 is the refrigeration cost, C4 is the cargo damage cost, C5 is the time penalty cost, C6 is the energy consumption cost, C7 is the charging cost, and C8 is the carbon emission cost; Formula C1=Σ i∈D hc i ×y i (2) To calculate the fixed operating costs of the distribution center; In the formula, y j 1 means that distribution center j is selected, otherwise it is 0; hc i is the fixed cost of one day for distribution center i; formula Used to calculate vehicle purchase costs, including traditional vehicle purchase costs and pure electric refrigerated truck purchase costs; the purchase cost of pure electric refrigerated trucks is equal to the purchase cost of pure electric refrigerated trucks minus the government subsidy cost; In the formula, 1 means that the pure electric refrigerated truck passes through arc (i, j), otherwise it is 0; 1 means the conventional refrigerated truck passes through arc (i, j), otherwise it is 0; pc CV The purchase cost of a traditional refrigerated truck converted into one day, pc EV The purchase cost of a pure electric refrigerated truck converted into one day, sc EV The government subsidy cost for a pure electric refrigerated truck converted into one day; formula Used to calculate the refrigeration cost during refrigerated truck delivery, usually the cost of refrigerant consumed to maintain the temperature in the truck compartment; Where rc1 is the refrigeration cost during transportation, rc2 is the refrigeration cost during unloading, and t ij is the travel time of the refrigerated truck between (i, j); formula To calculate the cost of cargo damage, including the cost of damage during transportation and the cost of damage during the opening process when the goods are delivered to the customer; In the formula, p is the unit price of cold chain products, α is the deterioration rate of cold chain products during transportation, β is the deterioration rate of cold chain products during unloading, Φ i is the set of customers that the refrigerated truck has served before serving customer i; Formula C5=Σ i∈C (wc×max{0,e i -at i }+pc×max{0,at i -l i })(6) used to calculate time penalty costs, which refers to the additional costs incurred due to failure to meet the customer's time window, including the waiting cost of the refrigerated truck arriving at the customer before the expected time and the delay cost of arriving at the customer after the expected time; In the formula, at i is the time when the refrigerated truck arrives at point i, wc is the waiting cost rate generated by the refrigerated truck arriving at the customer point in advance, and pc is the delay cost rate generated by the refrigerated truck arriving at the customer point late; official and To calculate energy consumption costs, including the fuel consumption costs of traditional refrigerated trucks when driving and cooling, and the electricity consumption costs of pure electric refrigerated trucks when driving and cooling; In the formula, c e is the electricity price, c f is the oil price, v is the driving speed, ρ0 is the fuel consumption per unit distance when the vehicle is unloaded, ρ * CQ is the fuel consumption per unit distance when the vehicle is fully loaded. ij is the cargo load of the refrigerated truck between (i, j), M is the empty mass of the refrigerated truck, α ij is the slope, g is the gravitational constant, c r is the rolling friction coefficient, c d is the air resistance coefficient, ρ is the air density, A is the front, λ ele is the regression coefficient, η ele is the energy efficiency of pure electric refrigerated truck, η fuel is the combustion thermal efficiency of the traditional refrigerated truck engine, and μ is the refrigeration equipment power of the refrigerated truck.

3. The method according to claim 1, characterized in that The constraints include delivery path constraints, delivery center constraints and charging plan constraints; The delivery path constraints include: pass Ensure that every customer is served once; Flow conservation constraints and pass and Ensure that each customer is delivered by only one refrigerated truck; Load constraints for refrigerated trucks and Time window constraints and Service time relationship between customers Where Ψ is a sufficiently large number; The distribution center constraints include: Hard time window constraints of distribution centers pass and Ensure that each refrigerated truck departs from the distribution center and arrives at the customer point where delivery is required, and returns to the same distribution center after completing the delivery work; pass Ensure that the total customer demand assigned to a distribution center does not exceed the capacity of the distribution center; pass Ensure that each customer must be served by one and only one distribution center; pass and (29) Ensure that if a distribution center is selected, it serves at least one customer; pass and Ensure that the number of vehicles sent out from the distribution center is equal to the number of vehicles returning to the distribution center, and the total number is no greater than the maximum number of vehicles served by the distribution center; The charging plan constraints include: Logical relationship constraints between charging stations and charging activities and In the formula is the battery capacity of the pure electric refrigerated truck when it leaves point i; pass Ensure that the time when the pure electric refrigerated truck arrives at point j is no earlier than the time when it leaves the charging station s plus the driving time during transportation; pass Ensure that each pure electric refrigerated truck has enough power to visit the remaining customer points and charging stations and return to the distribution center, where is the battery capacity of the pure electric refrigerated truck when it arrives at point i, h cr is the power consumption rate, which is assumed to be proportional to the driving distance; pass Ensure that the pure electric refrigerated truck is fully charged when it leaves the distribution center; pass Ensure that the load level of the battery must always be positive and not allowed to overcharge.

4. The method according to claim 3, characterized in that The artificial fish swarm algorithm is used to solve the low-carbon cold chain distribution network comprehensive optimization design model. First, according to the decision variables of the low-carbon cold chain distribution network comprehensive optimization design model, the initial cold chain distribution network design scheme is obtained, including: S31 sets algorithm parameters for solving the comprehensive optimization design model of the low-carbon cold chain distribution network; S32 constructs an initial artificial fish school based on the algorithm parameters and the decision variables of the comprehensive optimization design model of the low-carbon cold chain distribution network.

5. The method according to claim 3, characterized in that: In step S31, the algorithm parameters include: fish school size N Pop , maximum number of iterations Perception distance Visual, crowding factor δ, number of attempts try_N, maximum number of iterations when the bulletin board does not change and probability p c ; The step S32 comprises: Initializing the solution of the low-carbon cold chain distribution network comprehensive optimization design model, and obtaining the state of each artificial fish according to the decision variables of the low-carbon cold chain distribution network comprehensive optimization design model, which is used to represent a solution of the low-carbon cold chain distribution network comprehensive optimization design model, including the state of each artificial fish: the service customer sequence of all distribution routes; the type of refrigerated trucks of each distribution route in all distribution routes; the digital number of the service distribution center of each distribution route in all distribution routes; the digital number of the service charging station of each distribution route in all distribution routes; Assign each customer to the distribution center closest to him / her based on the distance from nearest to farthest; list d To store the customers assigned to distribution center d; select a distribution center d, assuming that the customer is first served by a pure electric refrigerated truck, and thus construct |list d | delivery routes and put them into the list table EV,r Medium; Based on the insertion heuristic algorithm, merge the list EV,r The delivery route in the list d Select seed client i * (i.e. the customer with the earliest allowed service time) and delete it, then remove it from the list d Select customer c and insert the current seed customer i * The best possible insertion position is determined by the formula Determine, where α1, α2 and α3 are non-negative weight values, α1+α2+α3=1, is the new arrival time at point j; if the route after inserting customer c meets the vehicle capacity limit, then d Delete customer c and check whether the newly constructed delivery route meets the range constraint. If not, select a charging station from the charging station set S and determine its best feasible insertion position based on formula (38). If it meets the constraint, continue to select another charging station from the list. d Select Insert Customer until list d The new delivery route is placed in the table f_list. EV,r If the delivery route after inserting customer c violates the vehicle capacity limit, the insertion is stopped and the newly constructed delivery route is put into the table f_list EV,r Then from the list d Reselect a seed customer to merge routes until the list d is an empty set; Calculate f_list based on the objective function EV,r The total cost of each delivery route in f_list is calculated, and the delivery routes are sorted from large to small, and then the delivery routes are sorted from f_list EV,r Select the first f_list EV,r , and convert them into delivery routes served by traditional refrigerated trucks, thus generating an initial solution; Finally, the generated solution of the low-carbon cold chain distribution network comprehensive optimization design model is copied into N Pop , constitute the initial artificial fish swarm; record the number of iterations n Gen =1,n noc =1.

6. The method according to claim 4, characterized in that The above-mentioned design scheme based on the current cold chain distribution network, executing the fish school behavior and removing and inserting operators, updating the service customer sequence and refrigerated truck type of each distribution path, and the construction location and number of distribution centers in the entire network include: S41 retains the best artificial fish individual in the fish school, and then performs N Pop Random selection, each time select a worker fish individual AFS a The improved foraging and tailgating behaviors are sequentially executed to achieve the optimal design of delivery routes, distribution centers, and refrigerated truck types; when AFS a and AFS b The distance between When it is greater than Visual, it is called AFS b Located in AFS a The domain set N(AFS a ) within the artificial fish individuals; S42 Foraging behavior refers to AFS a In N(AFS a ) selects a better artificial fish individual AFS through try_N random searches b Operators for learning; specifically: first, in AFS a and AFS b A learning point is randomly generated in AFS b Blocks after the mid-learning point replaces AFS a Blocks after the learning point in the learning process are generated to generate a child solution; then the customers with duplicate services are deleted from the child solution; then the child solution is checked to see if it contains all the customers in the network, and the missing customers are added according to the formula The calculated minimum insertion cost is used to reinsert the missing customers into the offspring solution; finally, the offspring artificial fish individual AFS is obtained. off ; In addition, if the best feasible insertion location cannot be found, a new delivery route will be opened, and it is stipulated that this route is served by pure electric refrigerated trucks and they are allocated to distribution centers in order of distance from shortest to longest, while taking into account the capacity constraints of the distribution centers; S43 Rear-end collision is a type of collision that is directed towards N (AFS a ) in the optimal artificial fish individual AFS best The learned operator; the improved execution rule of the rear-end collision behavior is: if AFS best With a high food concentration and a less crowded environment, AFS a Based on the probability p c AFS best Move, based on probability (1-p c ) to N(AFS a )A random solution AFS b A step forward; specifically, first in AFS best (AFS b ) and AFS a The same distribution center is identified in the network, and the routes served by the distribution center are exchanged to generate a sub-solution; the duplicated customers are then deleted from the sub-solution; then, it is checked whether the sub-solution contains all the customers in the network, and the missing customers are reinserted into the sub-solution according to the minimum insertion cost calculated by formula (40), and finally the sub-solution AFS is obtained. off Similarly, if the best feasible insertion position cannot be found, a new delivery route will be opened; with probability value p c With N(AFS a ) to avoid embedding the local optimal solution; c ) and N(AFS a ) to learn any solution, which can enhance the algorithm's exploration ability; S44 If the number of consecutive no-changes on the bulletin board does not reach the preset value, execute step S5; if the number of consecutive no-changes on the bulletin board does not reach the preset value, first execute removal, that is, select and execute the customer removal operator, the distribution center removal operator and the charging station removal operator based on the roulette selection mechanism; then execute the insertion operator based on the minimum insertion cost calculated by formula (40); repeat this sub-step N Pop / 2 times, record the new artificial fish state, and then execute step S5.

7. The method according to claim 5, characterized in that The execution of the charging station location optimization program to update the construction location and number of charging stations in the entire distribution network includes: The label-based comparison mechanism compares the labels of the selected route (fs′, tc′, ns′, vb′, fl′) with the current optimal route r * Tags (fs * ,tc * ,ns * ,vb * ,fl * ); where fs′ is a 0-1 variable, 1 indicates that the delivery route is valid, otherwise it is 0, tc′ is the travel cost, ns′ is the number of charging stations on the delivery route, vb′ is the amount of battery level violated, and fl′ is the location of the first customer point on the delivery route that violates the battery range constraint; if (fs′, tc′, ns′, vb′, fl′) is better than (fs * ,tc * ,ns * ,vb * ,fl * ), then let r * ←r′; Output the distribution route set R after all solutions for the fish school complete the optimization of charging station selection * .

8. The method according to claim 6, characterized in that The objective function based on the minimum total operating cost is used to evaluate the solution of the comprehensive optimization design model of the low-carbon cold chain distribution network, and obtain the optimized low-carbon cold chain distribution network design scheme, including: The food concentration of each artificial fish in the initial artificial fish school is calculated based on the objective function, and compared with the solution recorded in the bulletin board of the artificial fish school algorithm. If it is better than the solution recorded in the bulletin board, the solution recorded in the bulletin board is updated, and the number of times the bulletin board is not continuously updated is reset to 1; otherwise, the solution recorded in the bulletin board is retained, and the number of times the bulletin board is not continuously updated is increased by 1.

9. The method according to claim 7, characterized in that: The step S7 includes: determining whether the number of iterations of the low-carbon cold chain distribution network optimization design model solved by the artificial fish swarm algorithm meets the preset maximum number of iterations; if so, ending the solution of the low-carbon cold chain distribution network optimization design model and outputting the optimal low-carbon cold chain distribution network design plan; otherwise, executing the step S4.

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