A cold chain low-carbon transport vehicle scheduling method under fuzzy travel time

Abstract

The application discloses a cold-chain low-carbon transport vehicle scheduling method under fuzzy driving time, which comprises the following steps: main and auxiliary population initialization; fusing the main population and the auxiliary population into a temporary population; performing niche reservation operation on individuals in the temporary population according to multiple optimization target values in a scheduling model, replacing individuals in the main population with the reserved ps elite individuals in the temporary population, and obtaining an updated main population; determining a non-inferior solution set of the updated main population according to the multiple optimization target values in the scheduling model by using a Pareto non-dominated relationship; updating a pheromone matrix by using individuals in the non-inferior solution set of the updated main population, sampling the pheromone matrix to update an auxiliary population; interacting individuals in the updated main and auxiliary populations; performing local search on individuals in the non-inferior solution set of the main and auxiliary populations after the interaction; and judging a termination condition. By the method, a high-quality non-inferior solution set of the cold-chain low-carbon transport vehicle scheduling problem under fuzzy driving time can be obtained in a short time.

Description

Technical Field

[0001] The invention relates to a cold chain low-carbon transport vehicle scheduling method under fuzzy travel time, belonging to the field of cold chain low-carbon transport vehicle scheduling. Background Art

[0002] With the rapid development of industries like fresh food e-commerce and food processing, demand for cold chain foods is increasing. To facilitate the simultaneous delivery of goods at varying temperatures, multi-compartment vehicles with multiple, independently temperature-controlled compartments have emerged. Optimizing the routes of these vehicles can effectively improve the efficiency of cold chain transportation. Given that, in practice, uncertainties often arise due to weather, road conditions, vehicle status, and driver status, which often prevent vehicles from delivering goods on a specific timeframe, the rational scheduling of multi-compartment vehicles with fuzzy travel times is crucial.

[0003] Green development has become a global consensus, and countries are actively implementing relevant policies and measures to promote the development and transformation of green industries. In the transportation sector, according to statistics from the United Nations and the International Energy Agency (IEA), transportation accounts for approximately 15%-20% of global greenhouse gas emissions. Therefore, optimizing carbon emissions by factoring them into total transportation costs is imperative. Furthermore, to enhance a company's competitiveness and sustainable development, it is crucial to consistently adhere to the principle of "customer first" and effectively improve customer satisfaction, while also being considerate of employees, balancing driver workloads as much as possible, and striving to enhance employee well-being. Therefore, in addition to focusing on transportation costs, maintaining a balance between customer satisfaction and driver workload should also be a daily goal for companies. However, when optimizing these three objectives simultaneously, maintaining a diverse range of solutions becomes a challenge. Summary of the Invention

[0004] The present invention provides a cold chain low-carbon transport vehicle scheduling method under fuzzy driving time. On the one hand, a scheduling model is established with minimizing total transportation cost, maximizing customer satisfaction and minimizing driver workload imbalance as optimization objectives. On the other hand, a main-auxiliary population collaborative search mechanism is adopted to perform global search, and a local search operator is further constructed in combination with the characteristics of the problem, and a variable neighborhood descent algorithm is adopted to perform local search operations. Through the method of the present invention, a high-quality non-inferior solution set of the cold chain low-carbon transport vehicle scheduling problem under fuzzy driving time can be obtained in a short time, providing decision-making assistance for cold chain transportation and distribution companies.

[0005] The technical solution of the present invention is:

[0006] According to a first aspect of the present invention, a method for scheduling cold chain low-carbon transportation vehicles under fuzzy travel time is provided, comprising the following steps:

[0007] Step1, decimal coding is used for the collection center and the customer number; each vehicle starts from the collection center, returns to the collection center after completing the service, and the vehicle cabin load meets the capacity constraint;

[0008] Step2, set the main parameters;

[0009] Step3, main and auxiliary population initialization;

[0010] Step4, the main population and the auxiliary population are fused into a temporary population; according to the multiple optimization objective values in the scheduling model, the niche reservation operation is performed on the individuals in the temporary population, the ps elite individuals reserved in the temporary population are used to replace the individuals in the main population, and the updated main population is obtained; the non-inferior solution set is determined by using the Pareto non-dominance relationship according to the multiple optimization objective values in the scheduling model; the individuals in the non-inferior solution set of the updated main population are used to update the pheromone matrix, and the pheromone matrix is sampled to update the auxiliary population; the individuals in the updated main and auxiliary populations are interacted;

[0011] Step5, local search is performed on the individuals in the non-inferior solution set of the main population and the auxiliary population after interaction;

[0012] Step6, termination condition judgment: if the termination condition is met, the non-inferior solution set of the main population is output; otherwise, go to Step4 and continue to execute.

[0013] Further, the optimization objectives are to minimize the total transportation cost, maximize the customer satisfaction, and minimize the driver working time imbalance value, and the constraint conditions are constructed.

[0014] Further, the optimization objective is specifically:

[0015] The first optimization objective is to minimize the total transportation cost Z1, which is calculated by the following formula:

[0016] MinimizeZ1=Z economy_cost +Z emission_cost

[0017] In the formula, Z1 represents the total transportation cost, Z economy_cost represents the travel cost, Z environment_cost represents the carbon emission cost;

[0018] The second optimization objective is to maximize the customer satisfaction function, which is expressed as follows:

[0019]

[0020] In the formula, Z2 represents the customer satisfaction, n c represents the number of customers, and μ i represents the satisfaction of customer i.

[0021] The third optimization goal is to minimize the imbalance of drivers’ working hours, which is expressed as follows:

[0022]

[0023] Where Z3 represents the imbalance value of driver working time, represents the fuzzy travel time between customers, represents the decision variable, w i represents the waiting time that a vehicle takes to serve customer i, s i represents the service time required for customer i, K={1,2,...,n k} represents the vehicle set, n k Represents the total number of vehicles.

[0024] Furthermore, the initialization of the main and auxiliary populations is specifically as follows: individuals in the main population are initialized using a greedy rule and a random rule, and individuals in the auxiliary population are initialized using a random rule.

[0025] Furthermore, individuals in the main population are initialized using a greedy rule, specifically in the following steps:

[0026] 1) Construct a customer set to be served based on the customers who need services; use the collection center as the starting point for each vehicle;

[0027] 2) Select the next customer to be served by the vehicle from the set of customers to be served based on the current greedy goal;

[0028] 3) Determine whether the product required by the selected customer exceeds the rated load capacity of each compartment of the vehicle; if not, serve the customer and remove him from the set of customers to be served; otherwise, dispatch the next vehicle;

[0029] 4) Determine whether the set of customers to be served is empty. If it is empty, it means that all customer services have been completed; otherwise, return to 2) and continue execution.

[0030] Furthermore, minimizing the total transportation cost and maximizing customer satisfaction are taken as greedy objectives.

[0031] Furthermore, the local search is performed on the individuals in the non-inferior solution sets of the main population and the auxiliary population after the interaction, specifically: the "Insert", "Exchange", "2-Opt" and "Special-Insert" neighborhood operations are performed in sequence on the obtained non-inferior solution set individuals in the main and auxiliary populations, and a variable neighborhood descent strategy is adopted during the execution process.

[0032] Further, the Special-Insert neighborhood operation specifically comprises: for the current individual, randomly selecting a customer from the sub-path sequence with the longest driver working time of the individual and inserting the customer into any position of the sub-path sequence with the shortest driver working time, wherein the driver working time refers to the sum of the fuzzy travel time, the waiting time and the service time.

[0033] According to a second aspect of the present application, a processor is provided for running a program, wherein the program performs the cold-chain low-carbon transport vehicle scheduling method under fuzzy travel time according to any one of the above aspects when running.

[0034] According to a third aspect of the present application, a computer readable storage medium is provided, comprising a stored program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to perform the cold-chain low-carbon transport vehicle scheduling method under fuzzy travel time according to any one of the above aspects when the program runs.

[0035] The present application has the following advantages: firstly, the present application uses the greedy rule and the random rule for population initialization, thereby improving the convergence speed of the algorithm; secondly, the present application uses the mechanism of main and auxiliary population cooperative search, thereby improving the global search ability of the algorithm; finally, the present application uses the variable neighborhood descent algorithm with four neighborhood operators for local search, thereby enhancing the local search ability of the algorithm. The method can obtain a high-quality non-inferior solution set of the cold-chain food low-carbon transport process vehicle scheduling problem under fuzzy travel time in a short time, thereby providing a high-quality decision basis for transport enterprises. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 The figure is the overall algorithm flowchart of the present application.

[0037] Figure 2 The figure is the vehicle optimization scheduling diagram of the cold-chain food low-carbon transport process of the present application.

[0038] Figure 3 The figure is the PMX operation diagram of the present application.

[0039] Figure 4 The figure is the neighborhood operation "Insert", "Exchange", "2-Opt" and "Special-Insert" diagram of the present application. DETAILED DESCRIPTION

[0040] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other in any way.

[0041] Example 1: Figure 1-4 According to a first aspect of an embodiment of the present invention, a method for scheduling cold chain low-carbon transportation vehicles under fuzzy travel time is provided, comprising:

[0042] Step 1: Use decimal coding for the cargo collection center and the delivery customer number; each vehicle starts from the cargo collection center and returns to the cargo collection center after the service is completed. The load of each vehicle compartment meets the capacity constraint; for example, Figure 2 As shown, in each sub-path, the vehicle starts from the collection center. Due to the constraints of the vehicle cabin capacity, it must serve customers and return to the collection center under the premise of satisfying the constraints. Therefore, the driving path of each vehicle constitutes a sub-path, with the collection center number 0 as the boundary. For example, the solution (individual) formed by 10 customers is π m =[0,5,2,7,3,0,6,4,8,0,10,1,9,0], where customers 5, 2, 7, and 3 are served by the first car, and their sequence [0,5,2,7,3,0] is the first subpath; customers 6, 4, and 8 are served by the second car, and their sequence [0,6,4,8,0] is the second subpath; customers 10, 1, and 9 are served by the third car, and their sequence [0,10,1,9,0] is the third subpath.

[0043] Step 2. Set the main parameters: The main parameters include the main and auxiliary population sizes ps, the pheromone importance factor α, the heuristic function importance factor β and the pheromone volatility coefficient ρ;

[0044] Step 3, initialization of main and auxiliary populations;

[0045] Step 4: Merge the main population and the auxiliary population into a temporary population; perform niche preservation operations on the individuals in the temporary population based on multiple optimization target values ​​in the scheduling model, and use the p elite individuals retained in the temporary population to replace the individuals in the main population to obtain an updated main population; use the Pareto non-dominated relationship to determine the non-inferior solution set of the updated main population based on multiple optimization target values ​​in the scheduling model; use the individuals in the non-inferior solution set of the updated main population to update the pheromone matrix, sample the pheromone matrix to update the auxiliary population; interact with the individuals in the updated main and auxiliary populations;

[0046] Step 5: Perform local search on the individuals in the non-inferior solution set of the main population and auxiliary population after interaction;

[0047] Step 6. Termination condition judgment: If the termination condition is met, output the non-inferior solution set of the main population; otherwise, go to Step 4 to continue execution.

[0048] Furthermore, the optimization objectives are to minimize the total transportation cost, maximize customer satisfaction and minimize the imbalance of driver working hours, and the constraints are constructed.

[0049] Furthermore, the three optimization objective calculation methods are:

[0050] (1) Total transportation cost: Based on the travel cost Z economy_cost and carbon emission cost Z environment_cost The meaning of each cost is described in detail below.

[0051] Driving costs refer to the variable expenses incurred during transportation, mainly including various fuel costs, tire wear costs, and vehicle repair and maintenance costs incurred during vehicle use. The calculation formula for driving costs is as follows:

[0052]

[0053] Where C1 represents the driving cost per unit distance. In this example, C1 = 5. ij represents the distance between customer i and customer j, represents the decision variable (takes 0, 1; 1 means vehicle k goes from node i to node j; otherwise takes 0), K={1,2,...,n k} represents the vehicle set, n k Represents the total number of vehicles; node set V = V c ∪{0}, 0 represents the collection center, V c ={1,2,...,n c} represents the customer set; n c Represents the total number of customers.

[0054] The formula for calculating carbon emission costs is as follows:

[0055]

[0056] Where C2 represents the carbon emission cost per liter of fuel. In this example, C2 = 12.6. Represents the fuzzy fuel consumption of a vehicle traveling from node i to node j.

[0057] Furthermore, the is the fuzzy fuel consumption of the vehicle traveling from customer i to customer j, and the specific expression is:

[0058]

[0059] Among them, λ=ξ / κψ, γ=1 / 1000n tf η,α=τ+gsinθ+gC r cosθ,β=0.5C d ρ1A. Specific parameter descriptions and values ​​are detailed in Table 1. i is the cargo volume after the vehicle leaves customer i, is the fuzzy speed of the vehicle traveling between customers i and j (calculated based on the distance when the fuzzy travel time is known).

[0060] Table 1 Symbol definitions and values ​​of carbon emission related parameters

[0061] symbol Interpretation Value h Engine friction coefficient (kJ / rev / L) 0.2 M Engine speed (rev / s) 33 <![CDATA[V 发 ]]> Engine displacement (L) 5 ξ Fuel to air mass ratio 1 κ Diesel calorific value (kJ / g) 44 ψ Conversion factor (g / s to L / s) 737 <![CDATA[n tf ]]> Vehicle drivetrain efficiency 0.4 η Engine efficiency parameters 0.9 τ <![CDATA[加速度(m / s 2 )]]> 0 g <![CDATA[重力加速度(m / s 2 )]]> 9.81 θ Road slope 0 [C r ]]> Rolling resistance coefficient 0.01 w Vehicle curb weight (kg) 6350 <![CDATA[C d ]]> Air resistance coefficient 0.7 <![CDATA[ρ1]]> <![CDATA[空气密度(kg / m 3 )]]> 1.2014 A <![CDATA[迎风面积(m 2 )]]> 3.912

[0062] In summary, the minimum total transportation cost Z1 is calculated as follows:

[0063] MinimizeZ1=Z economy_cost +Z emission_cost (4)

[0064] (2) The function of maximizing customer satisfaction is expressed as follows:

[0065]

[0066] In the formula, Z2 represents customer satisfaction, n c Represents the number of customers, μ i Represents customer i’s satisfaction.

[0067] Exemplarily, the satisfaction function may be as follows:

[0068]

[0069] Where, The fuzzy start time of service for vehicle customer i, e i 、l i The lower and upper limits of the service time window for customer i, E i , L i are the lower and upper limits of the service time window that can be extended for customer i.

[0070] (3) The minimum driver working time imbalance value Z3 is expressed as follows:

[0071]

[0072] Where, represents the fuzzy travel time between customers, w irepresents the waiting time that a vehicle takes to serve customer i, s i The service time required to represent customer i.

[0073] Furthermore, the fuzzy travel time is expressed using triangular fuzzy numbers, and the expression of triangular fuzzy numbers is as follows:

[0074]

[0075] Where c l is the lower limit of the triangular fuzzy number, c m is the most likely value of the triangular fuzzy number, that is, the kernel of the triangular fuzzy number, c u is the upper limit of the triangular fuzzy number.

[0076] Furthermore, the constraints of the scheduling model include: first constraint: each customer can only be served by one vehicle and can only be served once (i.e., formula (9) and formula (10)); second constraint: each vehicle departs from the collection center and returns to the collection center after serving the customer (i.e., formula (11)); third constraint: flow balance, that is, after a vehicle enters a customer's location and completes the service, it must leave the location to visit another customer (i.e., formula (12)); fourth constraint: the current cabin load is equal to the sum of the corresponding product demand of all customers served by the vehicle plus the corresponding product demand of the current service customer (i.e., formula (13)); fifth constraint: the cargo capacity of each vehicle's cabin cannot exceed the rated load capacity of each cabin (i.e., formula (14)); sixth constraint: the collection center There is no waiting time and service time (i.e., formula (15)); the seventh constraint condition: the fuzzy arrival time of the vehicle to the current customer is equal to the fuzzy arrival time to the previous customer plus the waiting service time and service time of the previous customer, plus the fuzzy driving time from the previous customer to the current customer (i.e., formula (16)); the eighth constraint condition: the fuzzy starting service time of the vehicle serving the current customer is equal to the fuzzy starting service time of the vehicle to the current customer plus the waiting time (i.e., formula (17)); the ninth constraint condition: the value of the waiting time (i.e., formula (18)); the tenth constraint condition: the time window constraint (i.e., formula (19)); the eleventh constraint condition: used for sub-loop elimination (i.e., formula (20)); the twelfth constraint condition: the value of the decision variable (i.e., formula (21)).

[0077] Specifically, the expressions involved in the constraints are as follows:

[0078]

[0079] w0=s0=0(15)

[0080]

[0081] Where, represents the load of product p after vehicle k serves customer i, q ip represents the quantity of product p that customer i needs to deliver, and the product set P = {1,2,...,n p}, n p Represents the maximum number of product types required by customers (i.e., the number of cabins used in the vehicle); Q p Represents the maximum load of each cabin, w i represents the waiting time spent by the vehicle serving customer i, s i represents the service time required by customer i, w0 and s0 represent the waiting time and service time required by the vehicle at the collection center; represents the fuzzy time when the vehicle arrives at customer i, Respectively The lower limit, most likely value, and upper limit of represents the fuzzy travel time between customers; represents the fuzzy service start time at customer i; E i Represents the lower limit of the time window for customer i.

[0082] Exemplarily, the initialization of the main parameters includes: obtaining the main parameter values ​​through an orthogonal experimental design method. In the example of the present invention, the values ​​of the four main parameters are: ps=30, α=2, β=3, ρ=0.4.

[0083] Furthermore, the initialization of the main and auxiliary populations is specifically as follows: individuals in the main population are initialized using a greedy rule and a random rule, and individuals in the auxiliary population are initialized using a random rule.

[0084] Furthermore, the main population is initialized using both a greedy rule and a random rule. Specifically, a first preset number of individuals is initialized using the greedy rule, and a second preset number of individuals is initialized using the random rule. The sum of the first and second preset numbers is the main population size ps. For example, the first preset number is set to 2, and when ps = 30, the second preset number is set to 28.

[0085] Furthermore, individuals in the main population are initialized using a greedy rule, which is achieved through the following steps:

[0086] 1) Construct a customer set to be served based on the customers who need services; use the collection center as the starting point for each vehicle;

[0087] 2) Select the next customer to be served by the vehicle from the set of customers to be served based on the current greedy goal;

[0088] 3) Determine whether the product required by the selected customer exceeds the rated load capacity of each compartment of the vehicle; if not, serve the customer and remove him from the set of customers to be served; otherwise, dispatch the next vehicle;

[0089] 4) Determine whether the set of customers to be served is empty. If it is empty, it means that all customer services have been completed; otherwise, return to 2) and continue execution.

[0090] Furthermore, minimizing the total transportation cost and maximizing customer satisfaction are taken as greedy objectives.

[0091] Furthermore, the random rule initialization is achieved through the following steps:

[0092] S1. Build a customer set to be served based on the customers to be served; use the collection center as the starting point for each vehicle;

[0093] S2. Randomly select a customer from the set of customers to be served as the next customer to be served by the current vehicle;

[0094] S3. Determine whether the product required by the selected customer exceeds the rated load capacity of each compartment of the vehicle; if not, serve the customer and remove him from the set of customers to be served; otherwise, dispatch the next vehicle;

[0095] S4. Determine whether the set of customers to be served is empty. If it is empty, it means that all customer services have been completed; otherwise, return to S2 to continue execution.

[0096] Furthermore, the update of the pheromone matrix is ​​as follows:

[0097] Let fpFrontSet=(π1,...,π spf ) is the non-inferior solution set in the main population, and the updating method of the element "pheromone concentration" in the pheromone matrix is ​​shown in formula (22).

[0098] τ gen+1 (c i ,c j )=ρ×τ gen (c i ,c j )+Δ(c i ,c j )(twenty two)

[0099] Where, τ gen (c i ,c j ) and τ gen+1 (c i ,c j ) represents the pheromone concentration on the current road segment and the updated road segment, and the endpoint c of the road segment i 、c j For customers or collection centers; Δ(ci ,c j ) is c i to c j The newly accumulated pheromone concentration on the road segment is calculated as follows:

[0100]

[0101] Where, as well as Represents individual π m Total transportation cost, customer satisfaction and driver working time imbalance value; edge(c i ,c j )∈π m Indicates road section (c i ,c j ) belongs to the solution of π m ;

[0102] Furthermore, the pheromone matrix is ​​sampled to update the auxiliary population. During the auxiliary population update process, the transition probability between customers needs to be continuously calculated based on the pheromone matrix, and the next service customer needs to be continuously selected based on the transition probability to obtain the sampling individual, and the auxiliary population individuals are updated and replaced based on the sampling individual. The transition probability p(c i ,c j ) is calculated according to the following formula:

[0103]

[0104] Where N j represents the set of customers to be served, η(c i ,c j ) represents the heuristic function value, which is calculated as follows:

[0105]

[0106] Where, Z1(c i ,c j ) indicates that the vehicle is from c i Go to service c j The transportation cost incurred on the road section, Z2(c i ,c j ) indicates that the vehicle is from c i Go to service c j When customer c j satisfaction.

[0107] Furthermore, the individuals in the updated main and auxiliary populations interact with each other, specifically by performing PMX crossover between the individuals in the auxiliary population and the individuals in the non-inferior solution set in the main population. The sub-individuals generated after PMX crossover update (replace) the corresponding individuals in the auxiliary population that participated in the crossover. Figure 3In the PMX crossover process shown, a random portion of individuals in the main population is selected and retained, and the rest are supplemented by individuals in the auxiliary population to complete the crossover operation. It should be noted that the array subscript starts from 1 during the program writing process. Figure 3-Figure 4 In the example, 1 is used as the collection center number and 2 is used as the starting number for the customer number.

[0108] Furthermore, the local search is performed on the individuals in the non-inferior solution set of the main population and the auxiliary population after the interaction, specifically: for the non-inferior solution set individuals in the main and auxiliary populations obtained, the following operations are performed in sequence: Figure 4 The “Insert”, “Exchange”, “2-Opt” and “Special-Insert” neighborhood operations shown adopt a variable neighborhood descent strategy during execution, that is, when an individual becomes better when executing a certain neighborhood operation, the individual will re-execute from the “Insert” neighborhood operation.

[0109] Furthermore, the Special-Insert neighborhood operation is specifically as follows: for the current individual, a customer is randomly selected from the sub-path sequence with the longest driver working time and inserted into any position of the sub-path sequence with the shortest driver working time; wherein the driver working time refers to the fuzzy driving time Waiting time i , service time i The sum of the three; m =[0,5,2,7,3,0,6,4,8,0,10,1,9,0], for example, there are three subpaths, and the working time of three drivers is obtained. It should be noted that if there are multiple subpath sequences with the longest / shortest driver working time, one is arbitrarily selected to participate in the neighborhood operation.

[0110] In order to verify the effectiveness of the scheduling method proposed in this invention, an experimental comparison is conducted with the classic mainstream multi-objective optimization algorithms MOEAD, NSGAII and SPEA2.

[0111] To ensure fairness in the comparison, the four algorithms were terminated with the same running time and solved the RC series of examples in the VRPTW standard dataset. All algorithms used an Intel i5 processor (2.5 GHz), 16 GB of memory, a Windows 10 operating system, and a MATLAB 2022b programming environment. Each example was run 20 times independently and evaluated using two indicators, as shown in Equations (26) and (27):

[0112]

[0113] Among them, v HIt represents the volume of the hypercube from the Hth individual to the reference point on the Pareto front obtained by each algorithm, and S is the set of non-inferior solutions. H The pfNum represents the distance between the Hth individual on the Pareto front obtained by each algorithm and the individual on its nearest approximate Pareto front, and the pfNum represents the number of individuals on the approximate Pareto front. The approximate optimal Pareto front of the present invention is composed of the first frontier individual selected by combining the Pareto front individuals obtained by each algorithm.

[0114] The results are summarized in Table 2. The bold text in the table indicates the optimal value for each case. A larger HV index is better, and a smaller IGD index is better. As can be seen from Table 2, the proposed algorithm outperforms the other three comparison algorithms in all cases, thus verifying the effectiveness of EACO.

[0115] Table 2

[0116]

[0117] In addition, in order to verify the effectiveness of the collaborative global search of the main and auxiliary populations of the key operations in the enhanced ant colony optimization scheduling method proposed in this invention, it is compared with the variant EACO that does not utilize this collaborative evolution mechanism. dco A comparison was made. dco The rest of the method is identical to the present invention, except that it only uses a single population that follows the traditional ant colony optimization evolutionary rules. Table 3 shows a comparison of different examples. As can be seen from Table 3, the collaborative global search between the primary and auxiliary populations used in the present invention has a significant advantage over the competition, validating the effectiveness of the key operations of the present invention.

[0118] Table 3

[0119]

[0120] According to a second aspect of an embodiment of the present invention, a processor is provided, which is used to run a program, wherein when the program is running, the cold chain low-carbon transportation vehicle scheduling method under fuzzy driving time described above is executed.

[0121] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned cold chain low-carbon transportation vehicle scheduling methods under fuzzy driving time.

[0122] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0123] The specific embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.

Claims

1. A cold chain low-carbon transportation vehicle scheduling method under fuzzy travel time, characterized in that: The following steps are involved: Step 1: Use decimal encoding for the collection center and the customer ID. Each vehicle departs from the collection center and returns to the collection center after completing the service. The load of each vehicle compartment meets the capacity constraint. Step 2. Set the main parameters; Step 3, initialization of main and auxiliary populations; Step 4: Merge the main population and auxiliary population into a temporary population; According to multiple optimization target values ​​in the scheduling model, the individuals in the temporary population are subjected to niche preservation operations, and the individuals in the main population are replaced with the p elite individuals retained in the temporary population to obtain an updated main population; the Pareto non-dominated relationship is used to determine the non-inferior solution set for the updated main population according to multiple optimization target values ​​in the scheduling model; Use the individuals in the updated main population's non-inferior solution set to update the pheromone matrix, and sample the pheromone matrix to update the auxiliary population; Interact with individuals in the updated main and auxiliary populations; Step 5: Perform local search on the individuals in the non-inferior solution set of the main population and auxiliary population after interaction; Step 6, termination condition judgment: If the termination condition is met, output the non-inferior solution set of the main population; otherwise, go to Step 4 to continue execution; The scheduling model specifically takes minimizing total transportation costs, maximizing customer satisfaction, and minimizing the imbalance of driver working hours as optimization objectives, and constructs constraint conditions; The optimization objectives are specifically: The first optimization goal is to minimize the total transportation cost Z1, which is calculated as follows: MinimizeZ1=Z economy_cost +Z emission_cost Where Z1 represents the total transportation cost, Z economy_cost represents the driving cost, Z environment_cost represents the cost of carbon emissions; The second optimization goal is to maximize the customer satisfaction function, which is expressed as follows: In the formula, Z2 represents customer satisfaction, n c Represents the number of customers, μ i represents customer i’s satisfaction; The third optimization goal is to minimize the imbalance of drivers’ working hours, which is expressed as follows: Where Z3 represents the imbalance value of driver working time, represents the fuzzy travel time between customers, represents the decision variable, w i represents the waiting time that a vehicle takes to serve customer i, s i represents the service time required for customer i, K={1,2,...,n k } represents the vehicle set, n k Represents the total number of vehicles.

2. The cold chain low-carbon transportation vehicle scheduling method under fuzzy travel time according to claim 1 is characterized in that: The initialization of the main and auxiliary populations is specifically as follows: individuals in the main population are initialized using the greedy rule and the random rule, and individuals in the auxiliary population are initialized using the random rule.

3. The cold chain low-carbon transportation vehicle scheduling method under fuzzy travel time according to claim 2 is characterized in that: The individuals in the main population are initialized using the greedy rule. The specific steps are as follows: 1) Construct a customer set to be served based on the customers who need services; use the collection center as the starting point for each vehicle; 2) Select the next customer to be served by the vehicle from the set of customers to be served based on the current greedy goal; 3) Determine whether the product required by the selected customer exceeds the rated load capacity of each compartment of the vehicle; If not, serve the customer and remove him from the set of customers to be served; otherwise, send the next car; 4) Determine whether the set of customers to be served is empty. If it is empty, it means that all customer services have been completed; otherwise, return to 2) and continue execution.

4. The cold chain low-carbon transportation vehicle scheduling method under fuzzy travel time according to claim 3 is characterized in that: Minimizing the total transportation cost and maximizing customer satisfaction are set as greedy goals.

5. The cold chain low-carbon transportation vehicle scheduling method under fuzzy travel time according to claim 1 is characterized in that: The local search is performed on the individuals in the non-inferior solution sets of the main population and the auxiliary population after the interaction, specifically: the "Insert", "Exchange", "2-Opt" and "Special-Insert" neighborhood operations are performed on the obtained non-inferior solution set individuals in the main and auxiliary populations in sequence, and a variable neighborhood descent strategy is adopted during the execution process; The Special-Insert neighborhood operation is as follows: for the current individual, a customer is randomly selected from the sub-path sequence with the longest driver working time and inserted into any position in the sub-path sequence with the shortest driver working time; where the driver working time refers to the sum of the fuzzy travel time, waiting time, and service time.

6. A processor, characterized in that: The processor is used to run a program, wherein when the program is run, the cold chain low-carbon transportation vehicle scheduling method under fuzzy driving time described in any one of claims 1 to 5 is executed.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein, when the program is running, the device where the computer-readable storage medium is located is controlled to execute the cold chain low-carbon transportation vehicle scheduling method under fuzzy driving time according to any one of claims 1 to 5.

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