Multi-temperature co-allocation path planning method based on enhanced multi-strategy spider bee algorithm

Abstract

The invention discloses a multi-temperature co-distribution path planning method based on an enhanced multi-strategy spider bee algorithm, which is applied to the technical field of multi-temperature co-distribution distribution in the field of logistics and supply chain management, and comprises the following steps: obtaining vehicle resources and customer demands and position information of a distribution center, and establishing a refrigeration cost calculation function based on the change of environment temperature; on the basis of the refrigeration cost calculation function, a multi-temperature co-allocation path planning model based on the flexible compartment and the environment temperature with the aim of minimizing the total distribution cost is constructed, and constraint conditions of the multi-temperature co-allocation path planning model are constructed on the basis of vehicle resources and customer information; and an enhanced multi-strategy spider bee algorithm combining chaotic mapping, a self-adaptive elite strategy and a reciprocating population reduction strategy is used for target solution of the model, and the optimal departure time and distribution path are obtained. The energy efficiency utilization rate in the cold chain transportation process is effectively improved, and the load factor of the vehicles is improved while the number of the used vehicles is minimized.

Description

Technical Field

[0001] The present invention relates to the field of multi-temperature co-distribution and delivery technology in the field of logistics and supply chain management, and in particular to a multi-temperature co-distribution path planning method based on an enhanced multi-strategy spider bee algorithm. Background Art

[0002] With the acceleration of urbanization and the faster pace of life, consumers' demand for convenient food has driven the rapid development of the pre-prepared food industry. As a key support, cold chain logistics needs to optimize routes and reduce costs in multi-temperature distribution while ensuring food quality. However, traditional route optimization methods have limitations in processing time windows, order splitting, and temperature control complexity, making it difficult to meet the efficient needs of multi-temperature distribution.

[0003] Flexible compartment technology provides greater flexibility for multi-temperature distribution, and improves loading rate and delivery efficiency by dynamically adjusting the size of compartments. However, existing research focuses on specific types of goods and does not fully consider the diverse temperature requirements and environmental changes in pre-prepared food delivery. Optimizing vehicle departure time is also an important strategy to improve delivery efficiency, but most studies assume a fixed departure time and ignore the impact of long-term high temperature exposure on refrigeration energy consumption while considering the ambient temperature, which limits the optimization effect.

[0004] To this end, how to provide a multi-temperature co-distribution path optimization model that can comprehensively consider ambient temperature, flexible compartments, time windows and load factors, combine flexible compartments with departure time optimization, and design an improved multi-strategy spider bee optimization algorithm, through chaotic mapping, adaptive mutation and population reduction strategies, to improve the quality of the solution and convergence speed, so as to improve the energy efficiency utilization rate in the cold chain transportation process, and while minimizing the number of vehicles used, increase the vehicle's full load rate. The multi-temperature co-distribution path planning method based on the enhanced multi-strategy spider bee algorithm is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0005] In view of this, the present invention proposes a multi-temperature co-distribution path planning method based on an enhanced multi-strategy spider bee algorithm. First, the vehicle resources, customer demand and location information of the distribution center are obtained; secondly, the ambient temperature at time t is calculated, and a refrigeration cost calculation function is established based on the change in ambient temperature; finally, under the vehicle resource constraints of the distribution center, a multi-temperature co-distribution optimization model that minimizes the total distribution cost is constructed, and the enhanced multi-strategy spider bee algorithm combined with chaotic mapping, adaptive elite strategy and reciprocating population reduction strategy is used to solve the target of the multi-temperature co-distribution path planning model to obtain the optimal departure time and distribution path. The present invention combines the dynamic changes of flexible compartments and ambient temperature, and improves the energy efficiency utilization rate in the cold chain transportation process by adaptively optimizing the vehicle departure time, and while minimizing the number of vehicles used, it increases the vehicle's full load rate, providing enterprises with a more efficient energy-saving and quality assurance solution.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A multi-temperature co-distribution path planning method based on an enhanced multi-strategy spider bee algorithm, comprising: Step 1: Obtain the vehicle resources, customer demand and location information of the distribution center, and establish a cooling cost calculation function based on the change of ambient temperature; Step 2: Based on the cooling cost calculation function, a multi-temperature distribution path planning model based on flexible compartments and ambient temperature is constructed with the goal of minimizing the total distribution cost, and the constraints of the multi-temperature distribution path planning model are constructed based on vehicle resources and customer information; Step 3: Use the enhanced multi-strategy spider bee algorithm that combines chaotic mapping, adaptive elite strategy, and reciprocating population reduction strategy to solve the target of the multi-temperature co-distribution path planning model and obtain the optimal departure time and distribution path.

[0007] Optionally, in step 1, a cooling cost calculation function is established based on the change of ambient temperature, specifically: Divide the ambient temperature by hours, take each hour as a time zone, and calculate the ambient temperature of each time zone as follows: in, Indicates the ambient temperature of time zone m; m indicates the time of the time zone; Indicates the highest temperature of the day; Indicates the lowest temperature of the day; Represents hourly simulation Temperature value; is a fixed parameter; Assuming that the temperature is constant within each hour, the vehicle is considered to pass through one or more time zones. Suppose vehicle k departs from point i, and the departure time is expressed as , the next node visited is denoted as j, and the travel time from point i to point j is The service time at point j is expressed as , the heat transfer area of ​​the refrigerated and frozen compartments is as follows: in, represents the distance from node i to node j; v represents the speed of vehicle k; N represents the node set consisting of the customer point set and the distribution center, i,j∈N, when i=0, it represents the distribution center; p represents the p-type compartment of the distribution vehicle, p=1 represents the refrigerated type, and p=2 represents the frozen type; represents the demand for refrigerated goods at node j; represents the demand for type p cargo at node j; Z represents the total heat transfer area of ​​the carriage; represents the demand for refrigerated goods at node j; time The time zone m is represented by ,in, , Departure time The start time point and end time point corresponding to the time zone m to which it belongs, It also represents the start time of time zone m+1. Since the end time of time zone m coincides with the start time of time zone m+1, the end time of time zone m is represented by the start time of time zone m+1. The driving and service time is expressed as ,judge whether it is established; If established, then from From time to departure point j, the vehicles are at temperature The cooling cost during the period of driving is expressed as: in, represents the temperature in time zone m; Indicates the heat transfer coefficient of the compartment; Indicates the temperature of the refrigerated compartment; Indicates the temperature of the refrigerated compartment; represents the unit fuel supply level; K represents the set of delivery vehicles; represents the cooling cost; represents the heat generated by the refrigerator during the delivery vehicle k from node i to node j; E represents the heat generated by a unit of gasoline; If not, then in the time period Vehicle temperature Driving during this period Expressed as: renew , , re-judge Is it established to obtain the cooling cost.

[0008] Optionally, in step 2, based on the cooling cost calculation function, a multi-temperature distribution path planning model based on flexible compartments and ambient temperature is constructed with the goal of minimizing the total distribution cost, as follows: in, represents fixed cost; K represents the set of delivery vehicles; represents the fixed resource consumption of starting the delivery vehicle k; Indicates whether the vehicle departs from the distribution center; represents the transportation cost; N represents the node set consisting of the customer point set and the distribution center, i,j∈N, when i=0, it represents the distribution center; Indicates whether the delivery vehicle k travels from node i to the next node j; Indicates the capacity of the delivery vehicle; represents the initial quantity of cargo loaded when delivery vehicle k departs from the distribution center; Indicates the fuel consumption rate of the delivery vehicle when fully loaded; Indicates the fuel consumption rate of the delivery vehicle when it is unloaded; Represents the distance from node i to node j; Indicates the unit fuel supply level; represents the loss cost; p represents the p-type compartment of the delivery vehicle, p=1 represents the refrigerated compartment, and p=2 represents the frozen compartment; It represents the damage rate of p-type goods; L represents the unit price of goods per unit quantity; represents the time required to travel from node i to node j; represents the service time of node j; represents the demand for type p goods at node j; represents the compartment of vehicle k Whether the demand for product p of customer i is met; represents the time window penalty cost; represents the time window of customer point i, where represents the earliest arrival time required by node i for the delivery vehicle to arrive, It represents the latest arrival time that node i requires the delivery vehicle to arrive; represents the arrival time of delivery vehicle k to node i; , Respectively represent the penalty coefficients for early and late arrival of vehicles; Represents the cooling cost.

[0009] Optionally, in step 2, the constraints of the multi-temperature co-distribution path planning model are constructed based on vehicle resources and customer information, as follows: Where N represents the node set consisting of the customer point set and the distribution center, i,j∈N, when i=0, it represents the distribution center; Indicates whether to start from the distribution center and rush to node j; Indicates whether to return to the distribution center from a certain i node; K represents the set of distribution vehicles; Indicates whether the delivery vehicle k travels from node i to the next node j; Indicates whether the p-type compartment of vehicle k meets the p-type commodity demand of customer j; p represents the p-type compartment of the delivery vehicle, p=1 represents refrigerated compartment, and p=2 represents frozen compartment; Indicates whether vehicle k goes from node j to node i; represents the demand for type p goods at node i; Indicates the capacity of the delivery vehicle; represents the time when delivery vehicle k departs from point j; represents the time when delivery vehicle k departs from point i; represents the time required to travel from node i to node j; represents the service time of node j; S represents the set of path vertices; represents the departure time of delivery vehicle k from the distribution center; represents the delivery time window of the distribution center, represents the earliest time when the distribution center requires delivery vehicle k; represents the latest time; m represents the number of customers; Indicates whether the delivery vehicle k travels from node i to the next node j; Indicates whether the p-type compartment of delivery vehicle k meets the p-type commodity demand of customer i; Indicates the departure time from the last customer point on the path; Indicates the latest time to return to the distribution center.

[0010] Optionally, in step 3, an enhanced multi-strategy spider bee algorithm combining chaotic mapping, adaptive elite strategy and reciprocating population reduction strategy is used to solve the target of the multi-temperature co-allocation path planning model, specifically: Initialization parameters, including: population size G, minimum population size , upper bound ub, lower bound nb, number of iterations The trade-off rate TR, crossover rate Cr, number of mutations p_m of the current optimal individual, and number of mutations g_m of the global optimal individual are defined; and the current iteration count is initialized to T = 0. An initial spider wasp population is generated using the tent chaos mapping initialization strategy, and the generated population is encoded and decoded. The fitness value set of the initial population is calculated using the multi-temperature co-allocation path planning model. From the fitness value set of the initial population the minimum fitness value is selected The corresponding spider wasp individual is denoted as the optimal spider wasp individual of the initial population and the global optimal spider wasp individual The corresponding fitness value is the global optimal fitness value. ; Enter the main loop, generate a random number as the search agent index. If execute the prey behavior to update the spider wasp population; otherwise, execute the mating behavior to update the spider wasp population. When executing the prey behavior to update the spider wasp population, let When a random number p is generated. When the search behavior is used to update the individuals in the spider wasp population. When the follow and escape behaviors are used to update the individuals in the spider wasp population. When the nesting behavior is used to update the individuals in the spider wasp population, and random numbers and are generated. When the current optimal solution is used to update the individuals in the spider wasp population. When a spider wasp within the population is randomly selected and an additional step size is used to update the individuals in the spider wasp population. When executing the mating behavior to update the spider wasp population, randomly select spider wasp individuals and and to generate parameters and New spider wasp individuals are generated using the parameters and For each newly generated spider wasp individual in the population, a random number rand is generated. When rand < Cr, the corresponding individual in the population is updated to the new spider wasp individual; otherwise, it is not updated. The newly obtained spider wasp individuals are encoded and decoded, and the fitness value set of all individuals in the spider wasp population is calculated according to the multi-temperature co-allocation path planning model. , respectively compare the current collection The fitness value of the spider bee population before the update is compared with the fitness value of the corresponding individual in the spider bee population. If the current one is better, the corresponding individual in the updated population is the current spider bee individual. Otherwise, the original individual is retained and compared in turn to update the spider bee population. ; Encode and decode the new spider bee individuals currently obtained, calculate the fitness value set of all individuals in the updated spider bee population, and take the optimal fitness value And the global optimal fitness value For comparison, if the current fitness value is better, update the optimal spider bee individual and the optimal fitness value is the current spider bee individual and the corresponding fitness value; Let the current number of iterations , and use the adaptive elite mutation strategy to perform elite mutation p_m times on each individual in the current spider bee population, and check whether the new spider bee position exceeds the upper and lower bounds ub and nb of the search space. If so, correct it to the boundary; use the adaptive elite mutation strategy to perform elite mutation on the new population The best spider bee individual in the population undergoes elite mutation g_m times. If the fitness value of the individual after each mutation is better, the corresponding spider bee individual in the population is updated. and fitness value The current spider bee individual and its fitness value are used to obtain the new spider bee population after mutation. ; Compare and The size of Better, then update the optimal spider bee individual and the optimal fitness value The current spider bee individual and the corresponding fitness value are used, and the population size G is updated using the reciprocating population reduction strategy until the number of reciprocating cycles is reached, and it is determined whether the current iteration number T is greater than , then the output corresponds to the optimal fitness value and the optimal spider bee individual , otherwise return to the main loop to repeat the steps and update the solution.

[0011] Optionally, a tent chaotic map initialization strategy is used to generate an initial spider bee population, and the generated population is encoded and decoded, specifically: An initial matrix of 2M rows and G columns is randomly generated, and the initial matrix is ​​updated using tent chaotic mapping to generate the initial spider bee population, as follows: in, represents the mth value of the gth spider bee individual; chaos parameter ; Represents the mth value of the gth parameter in the initial matrix; Indicates the population size; represents a chaotic variable; rand represents a random number; ub and nb represent upper and lower bounds respectively; the generated initial spider bee population is as follows: Each row represents a spider wasp individual , ; Each spider bee individual represents a set of solutions, simplifying the spider bee population to ; For any individual spider bee , Indicates the departure time. represents the customer node; the position-order coding method is introduced to decode the delivery path of the last M nodes. Sort in ascending order, record the number and then convert it into a natural number code, which represents the customer point number; assign the route according to the vehicle capacity constraint, if the sum of the customer point demand is met, then add the customer point to the route, otherwise generate a new route until all nodes are assigned; for the vehicle departure time , then directly allocate them from front to back according to the number of generated paths.

[0012] Optionally, when executing the prey behavior to update the spider bee population, use the search behavior to update the individuals in the spider bee population, specifically: Generate random numbers , , ;when When , the parameters are generated , , where rn1 is a random number; two spider wasp individuals are randomly selected from the population , , use the following formula to update the parameters of each spider bee individual: when When , the parameters are generated , ,in , , , rand represents a random number; the following formula is used to update the parameters of each spider bee individual: The following and escaping behaviors are used to update individuals in the spider bee population, specifically: when When, randomly select spider wasp individuals from the population , generate parameter C, , update the parameters in each spider wasp individual using the following formula: When , generate the parameter matrix , , where , update the parameters in each spider wasp individual using the following formula: Update the individuals in the spider wasp population using the nesting behavior, specifically: When , use the global optimal spider wasp individual to update the spider wasp individual as follows: When , randomly select spider wasp individuals , , , and generate new spider wasp individuals using the following formula: where, represents the parameter generated by Levy flight.

[0013] Optionally, when performing mating behavior to update the spider wasp population, randomly select spider wasp individuals , , to generate parameters , , specifically: Calculate the matrix , randomly select a spider wasp individual , ; Calculate , randomly select spider wasp individuals , , ; Use the parameters , to generate new spider wasp individuals. For each newly generated spider wasp individual in the population, generate a random number rand. When rand < Cr, update the corresponding individual in the population to the newly generated spider wasp individual using the following formula, otherwise do not update; Boundary check: check whether the new spider bee position exceeds the upper and lower bounds ub and nb of the search space. If so, correct it to the boundary to obtain the updated spider bee population of the Tth generation. , .

[0014] Optionally, an adaptive elite mutation strategy is used to perform elite mutation on each individual in the current spider bee population, as follows: in, , Indicates randomly selecting a spider wasp individual from the population.

[0015] Optionally, the population size G is updated using a reciprocating population reduction strategy as follows: Among them, % is the remainder operator, Indicates the number of reciprocating cycles.

[0016] It can be seen from the above technical solutions that, compared with the prior art, the present invention proposes a multi-temperature co-distribution path planning method based on the enhanced multi-strategy spider bee algorithm. The present invention enriches the research content of flexible compartments in multi-temperature co-distribution path optimization, and proposes a dynamic adjustment method based on flexible compartments, making distribution path planning more flexible and efficient. Flexible compartments can adjust space allocation according to the temperature control requirements of different goods, improve loading efficiency and resource utilization in a multi-temperature zone co-distribution environment, and thus provide a more intelligent and adaptable solution for cold chain distribution. By optimizing the vehicle departure time strategy, the present invention effectively reduces energy consumption during transportation, especially in the face of long-distance, multi-temperature co-distribution cold chain transportation scenarios. The optimized departure time can effectively avoid high temperature periods and reduce the vehicle's stay time in a high temperature environment, thereby reducing the refrigeration burden. The optimized departure time not only improves transportation efficiency, but also greatly reduces energy consumption, enhancing the environmental protection and economy of cold chain transportation. The present invention provides a comprehensive route optimization and energy efficiency management solution for the distribution of cold chain products such as pre-prepared meals, combining flexible compartments with departure time optimization, which not only improves transportation efficiency, but also reduces refrigeration energy waste by precisely controlling the temperature control environment. By implementing this solution, enterprises can improve transportation efficiency and resource utilization while ensuring the quality of cold chain distribution, and promote the industry to develop in a more efficient and environmentally friendly direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention 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, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0018] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0019] Figure 2 It is a schematic diagram of the enhanced multi-strategy spider bee algorithm flow of the present invention.

[0020] Figure 3 It is a schematic diagram of the path optimization result of the present invention.

[0021] Figure 4 A schematic diagram comparing the number of vehicles dispatched with and without considering the flexible compartments of the present invention.

[0022] Figure 5 It is a schematic diagram comparing the vehicle full load factor with and without considering the flexible compartment of the present invention.

[0023] Figure 6 It is a schematic diagram comparing the total cost of the optimized and non-optimized departure time of the present invention.

[0024] Figure 7 It is a schematic diagram of iterative comparison between the algorithm of the present invention and other algorithms. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Embodiment 1: Embodiment 1 of the present invention discloses a multi-temperature co-distribution path planning method based on an enhanced multi-strategy spider bee algorithm, such as Figure 1 As shown, including: It is stipulated that each delivery vehicle starts from distribution center point 0 and eventually returns to distribution center point 0.

[0027] Step 1: Obtain the vehicle resources, customer demand and location information of the distribution center, and establish a cooling cost calculation function based on the change of ambient temperature.

[0028] The vehicle resources, customer demand and location information of the distribution center, specifically: the maximum load weight Q of the distribution vehicle, the heat transfer area of ​​the carriage, the location distribution of the distribution center and customer points, the demand of each customer point and the time window requirements.

[0029] A cooling cost calculation function is established based on the change of ambient temperature, specifically: The change of ambient temperature can be approximated as a sine curve. The ambient temperature is divided into hours, and each hour is regarded as a time zone. The ambient temperature of each time zone is calculated as follows: in, Indicates the ambient temperature of time zone m; m indicates the time of the time zone; Indicates the highest temperature of the day; Indicates the lowest temperature of the day; Represents hourly simulation Temperature value; is a fixed parameter, take 12; Assuming that the temperature is constant within each hour, the vehicle is considered to pass through one or more time zones. Suppose vehicle k departs from point i, and the departure time is expressed as , the next node visited is denoted as j, and the travel time from point i to point j is The service time at point j is expressed as , the heat transfer area of ​​the refrigerated and frozen compartments is as follows: in, represents the distance from node i to node j; v represents the speed of vehicle k; N represents the node set consisting of the customer point set and the distribution center, i,j∈N, when i=0, it represents the distribution center; p represents the p-type compartment of the distribution vehicle, p=1 represents the refrigerated type, and p=2 represents the frozen type; represents the demand for refrigerated goods at node j; represents the demand for type p cargo at node j; Z represents the total heat transfer area of ​​the carriage; represents the demand for refrigerated goods at node j; time The time zone m is represented by ,in, , Departure time The start time point and end time point corresponding to the time zone m to which it belongs, It also represents the start time of time zone m+1. Since the end time of time zone m coincides with the start time of time zone m+1, the end time of time zone m is represented by the start time of time zone m+1. The driving and service time is expressed as ,judge whether it is established; If established, then from From time to departure point j, the vehicles are at temperature The cooling cost during the period of driving is expressed as: in, represents the temperature in time zone m; Indicates the heat transfer coefficient of the compartment; Indicates the temperature of the refrigerated compartment; Indicates the temperature of the refrigerated compartment; represents the unit fuel supply level; K represents the set of delivery vehicles; represents the cooling cost; represents the heat generated by the refrigerator during the delivery vehicle k from node i to node j; E represents the heat generated by a unit of gasoline; If not, then in the time period Vehicle temperature Driving during this period Expressed as: renew , , re-judge Is it established to obtain the refrigeration cost.

[0030] Step 2: Based on the refrigeration cost calculation function, a multi-temperature distribution path planning model based on flexible compartments and ambient temperature is constructed with the goal of minimizing the total distribution cost, and the constraints of the multi-temperature distribution path planning model are constructed based on vehicle resources and customer information.

[0031] by , As decision variables, Indicates whether the delivery vehicle k travels from node i to the next node j; Indicates whether the p-type compartment of vehicle k meets the p-type commodity demand of customer j.

[0032] Based on the cooling cost calculation function, a multi-temperature distribution path planning model based on flexible compartments and ambient temperature is constructed with the goal of minimizing the total distribution cost, as follows: in, represents fixed cost; K represents the set of delivery vehicles; represents the fixed resource consumption of starting the delivery vehicle k; Indicates whether the vehicle departs from the distribution center; represents the transportation cost; N represents the node set consisting of the customer point set and the distribution center, i,j∈N, when i=0, it represents the distribution center; Indicates whether the delivery vehicle k travels from node i to the next node j; Indicates the capacity of the delivery vehicle; represents the initial quantity of cargo loaded when delivery vehicle k departs from the distribution center; Indicates the fuel consumption rate of the delivery vehicle when it is fully loaded; Indicates the fuel consumption rate of the delivery vehicle when it is unloaded; Represents the distance from node i to node j; Indicates the unit fuel supply level; represents the loss cost; p represents the p-type compartment of the delivery vehicle, p=1 represents the refrigerated compartment, and p=2 represents the frozen compartment; It represents the damage rate of p-type goods; L represents the unit price of goods per unit quantity; represents the time required to travel from node i to node j; represents the service time of node j; represents the demand for type p goods at node j; represents the compartment of vehicle k Whether the demand for product p of customer i is met; represents the time window penalty cost; represents the time window of customer point i, where represents the earliest arrival time required by node i for the delivery vehicle to arrive, It represents the latest arrival time that node i requires the delivery vehicle to arrive; represents the arrival time of delivery vehicle k to node i; , Respectively represent the penalty coefficients for early and late arrival of vehicles; Represents the cooling cost.

[0033] The constraints for constructing a multi-temperature co-distribution path planning model based on vehicle resources and customer information are as follows: It means that the vehicle starts from the distribution center and finally returns to the distribution center after serving the customer; It means that when a vehicle passes through arc (i, j), customer j accepts the delivery service; represents the balance of vehicle flow visiting customers; Indicates that the delivery demand of each customer's goods is not greater than the capacity of the corresponding delivery vehicle compartment; This means that each customer's goods can only be delivered by one vehicle; represents the time relationship between the predecessor and successor nodes at the time when the vehicle arrives at customer j; Indicates that customer demand is non-negative; It means that customer needs cannot be separated; Indicates elimination of sub-circuit; Indicates that the vehicle departure time must be within the distribution center time window; It means that all vehicles must return to the distribution center within the specified time; is a decision variable, which means that if vehicle k travels from customer i to customer j, it is 1, otherwise it is 0; is a decision variable, indicating that if the compartment of vehicle k If the demand for product p of customer i is met, it is 1, otherwise it is 0; Where N represents the node set consisting of the customer point set and the distribution center, i,j∈N, when i=0, it represents the distribution center; Indicates whether to start from the distribution center and rush to node j; Indicates whether to return to the distribution center from a certain i node; K represents the set of distribution vehicles; Indicates whether the delivery vehicle k travels from node i to the next node j; Indicates whether the p-type compartment of vehicle k meets the p-type commodity demand of customer j; p represents the p-type compartment of the delivery vehicle, p=1 represents refrigerated compartment, and p=2 represents frozen compartment; Indicates whether vehicle k goes from node j to node i; represents the demand for type p goods at node i; Indicates the capacity of the delivery vehicle; represents the time when delivery vehicle k departs from point j; represents the time when delivery vehicle k departs from point i; represents the time required to travel from node i to node j; represents the service time of node j; S represents the set of path vertices; represents the departure time of delivery vehicle k from the distribution center; represents the delivery time window of the distribution center, represents the earliest time when the distribution center requires delivery vehicle k; represents the latest time; m represents the number of customers; Indicates whether the delivery vehicle k travels from node i to the next node j; Indicates whether the p-type compartment of delivery vehicle k meets the p-type commodity demand of customer i; Indicates the departure time from the last customer point on the path; Indicates the latest time to return to the distribution center.

[0034] Step 3: Use Figure 2 The enhanced multi-strategy spider bee algorithm shown in the figure combines chaotic mapping, adaptive elite strategy and reciprocating population reduction strategy to solve the target of the multi-temperature distribution path planning model and obtain the optimal departure time and distribution path.

[0035] The enhanced multi-strategy spider bee algorithm combining chaotic mapping, adaptive elite strategy and reciprocating population reduction strategy is used to solve the target of the multi-temperature distribution path planning model, specifically: Initialization parameters, including: population size G, minimum population size , upper bound ub, lower bound nb, number of iterations , trade-off rate TR, crossover rate Cr, current optimal individual mutation times p_m, global optimal individual mutation times g_m; define and initialize the current number of iterations as T=0.

[0036] The tent chaotic mapping initialization strategy is used to generate the initial spider bee population, and the generated population is encoded and decoded, and the fitness value set of the initial population is calculated using the multi-temperature co-matching path planning model. , from the fitness value set of the initial population Select the minimum fitness value The corresponding spider bee individual is recorded as the optimal spider bee individual in the initial population and the global optimal spider bee individual , the corresponding fitness value is the global optimal fitness value .

[0037] The tent chaotic map initialization strategy is used to generate the initial spider bee population, and the generated population is encoded and decoded, specifically: An initial matrix of 2M rows and G columns is randomly generated, and the initial matrix is ​​updated using tent chaotic mapping to generate the initial spider bee population, as follows: in, represents the mth value of the gth spider bee individual; chaos parameter ; Represents the mth value of the gth parameter in the initial matrix; Indicates the population size; represents a chaotic variable; rand represents a random number; ub and nb represent upper and lower bounds respectively; the generated initial spider bee population is as follows: Each row represents a spider wasp individual , ; Each spider bee individual represents a set of solutions, simplifying the spider bee population to ; For any individual spider bee , Indicates the departure time. represents the customer node; the position-order coding method is introduced to decode the delivery path of the last M nodes. Sort in ascending order, record the number and then convert it into a natural number code, which represents the customer point number; assign the route according to the vehicle capacity constraint, if the sum of the customer point demand is met, then add the customer point to the route, otherwise generate a new route until all nodes are assigned; for the vehicle departure time , then directly allocate them from front to back according to the number of generated paths.

[0038] Enter the main loop and generate random numbers As a search proxy index, ,like , execute prey behavior to update the spider bee population, otherwise execute mating behavior to update the spider bee population; when executing prey behavior to update the spider bee population, let ,when When a random number p is generated, ,when When , the search behavior is used to update the individuals in the spider bee population. When , following and escaping behaviors are used to update individuals in the spider bee population; when When the nesting behavior is used to update the individuals in the spider bee population, a random number is generated. , , ,when When using the current optimal solution Update the individuals in the spider bee population. When it is time, randomly select spider wasps within the population and use an additional step size to update the individuals in the spider wasp population; when performing mating behavior to update the spider wasp population, randomly select spider wasp individuals 、 、 Generate parameters 、 ,and use the parameters 、 to generate new spider wasp individuals. For each newly generated spider wasp individual in the population, generate a random number rand. When rand < Cr, update the corresponding individual in the population to the new spider wasp individual; otherwise, do not update.

[0039] When performing prey behavior to update the spider wasp population, use search behavior to update the individuals in the spider wasp population. Specifically:[[]] Generate random numbers 、 , ;When ,generate parameters , ,where rn1 is a random number; randomly select two spider wasp individuals 、 from the population, and update the parameters in each spider wasp individual using the following formula:[[]] When ,generate parameters , ,where , , ,rand represents a random number; update the parameters in each spider wasp individual using the following formula:[[]] Adopt following and escaping behaviors to update the individuals in the spider wasp population. Specifically:[[]] When ,randomly select a spider wasp individual from the population, generate a parameter C, ,and update the parameters in each spider wasp individual using the following formula:[[]] When ,generate a parameter matrix , ,where ,and update the parameters in each spider wasp individual using the following formula:[[]] Use nesting behavior to update the individuals in the spider wasp population. Specifically:[[]] When it is the case, update the spider wasp individuals using the globally optimal spider wasp individual as follows: When it is the case, randomly select spider wasp individuals , , , and generate new spider wasp individuals using the following formula: where represents the parameter generated by Levy flight; When updating the spider wasp population by performing mating behavior, randomly select spider wasp individuals , , to generate parameters , , specifically: Calculate the matrix , randomly select a spider wasp individual , ; Calculate , randomly select spider wasp individuals , , ; Use the parameters , to generate new spider wasp individuals. For each newly generated spider wasp individual in the population, generate a random number rand. When rand < Cr, update the corresponding individual in the population to the new spider wasp individual using the following formula, otherwise do not update; Perform boundary check to check whether the new spider wasp position exceeds the upper and lower bounds ub and lb of the search space. If so, correct it to the boundary to obtain the updated spider wasp population at the T-th generation , .

[0040] Encode and decode the currently obtained new spider wasp individuals, and calculate the fitness value set of all individuals in the spider wasp population according to the multi-temperature co-allocation path planning model , and compare the fitness values in the current set with the fitness values of the corresponding individuals in the spider wasp population before update respectively. If the current one is better, update the corresponding individual in the population to the current spider wasp individual, otherwise retain the original individual, and compare them in turn to update the spider wasp population ; Encode and decode the new spider bee individuals currently obtained, calculate the fitness value set of all individuals in the updated spider bee population, and take the optimal fitness value And the global optimal fitness value For comparison, if the current fitness value is better, update the optimal spider bee individual and the optimal fitness value is the current spider bee individual and its corresponding fitness value.

[0041] Let the current number of iterations , and use the adaptive elite mutation strategy to perform elite mutation p_m times on each individual in the current spider bee population, and check whether the new spider bee position exceeds the upper and lower bounds ub and nb of the search space. If so, correct it to the boundary; use the adaptive elite mutation strategy to perform elite mutation on the new population The best spider bee individual in the population undergoes elite mutation g_m times. If the fitness value of the individual after each mutation is better, the corresponding spider bee individual in the population is updated. and fitness value The current spider bee individual and its fitness value are used to obtain the new spider bee population after mutation. .

[0042] The adaptive elite mutation strategy is used to perform elite mutation on each individual in the current spider bee population, as follows: in, , Indicates randomly selecting a spider wasp individual from the population.

[0043] Compare and The size of Better, then update the optimal spider bee individual and the optimal fitness value The current spider bee individual and the corresponding fitness value are used, and the population size G is updated using the reciprocating population reduction strategy until the number of reciprocating cycles is reached, and it is determined whether the current iteration number T is greater than , then the output corresponds to the optimal fitness value and the optimal spider bee individual , otherwise return to the main loop to repeat the steps and update the solution.

[0044] The population size G is updated using the reciprocating population reduction strategy as follows: Among them, % is the remainder operator, Indicates the number of reciprocating cycles.

[0045] Embodiment 2: Embodiment 2 of the present invention discloses a specific application of a multi-temperature co-distribution path planning method based on an enhanced multi-strategy spider bee algorithm, as follows: Since there is no internationally recognized example of MCVRPTW, in order to verify the effectiveness of the algorithm of the present invention, the VRPTW example of Solomon (1987) was modified and converted into an available MCVRP example. In the experiment, the refrigeration demand and freezing demand were obtained by splitting the customer demand in a ratio of 2:1. The capacity of the refrigerated compartment and the refrigerated compartment of the flexible compartment was not considered in the experiment and was divided in a ratio of 3:1 with the original compartment capacity. In order to finely simulate the change of ambient temperature corresponding to time, given that the time window of the selected distribution center is consistent, the time window of each distribution center is uniformly divided into 12 time periods, each time period represents 1 hour and corresponds to a temperature data. On this basis, all customer time window data in the original example are modified accordingly to conform to the new time division scheme, and the vehicle speed is also changed to the value corresponding to a time period. In this experiment, the first 4 of the C1 data sets of 25, 50, and 100 scales, a total of 12 examples, are selected for the transformation experiment, and the examples of each scale are called S1-S4, R1-R4, and L1-L4 respectively. The algorithm was programmed using MATLAB R2023b, and all experiments were performed on a server cluster with Intel(R) Xeon(R) Gold 6258R CPU @ 2.70GHz, 256GB memory, and CentOS Linux release7.6.1810 operating system. The relevant parameters of the embodiment are shown in Table 1.

[0046] Table 1 Parameters related to the embodiment In order to verify the effectiveness of the enhanced multi-strategy spider bee optimization algorithm proposed in this paper in solving path optimization problems, an experimental test was carried out. The results are as follows Figure 3 These calculation results further demonstrate the superiority of the proposed algorithm. Figure 3 The optimized path and service sequence are intuitively displayed. It can be clearly seen that all customer points have been successfully served, indicating that the optimization algorithm has effectively reduced the overall delivery cost while improving service quality. These results show that the enhanced multi-strategy spider bee optimization algorithm not only achieves significant cost optimization while ensuring service quality, but also can stably obtain high-quality solutions in multiple experimental rounds, fully verifying the robustness and effectiveness of the algorithm.

[0047] In order to prove the advantages of considering flexible compartments over traditional fixed compartments, the algorithm of the present invention is run 10 times in 12 examples to obtain the optimal result. Figure 4 As shown. Figure 4 It can be seen that compared with the fixed compartment model, the flexible compartment model reduces the number of vehicles used by an average of 2, and can reduce the number of vehicles used by 4 at most, which directly reduces the fixed cost of vehicle use. Secondly, in terms of full load rate, the full load rate of vehicles with and without flexible compartments is compared, such as Figure 5 As shown. Figure 5 It can be seen that the application of flexible compartments has also brought significant improvements. Whether it is a small-scale, medium-scale or large-scale case, the full load rate has been greatly improved, from an average of 57.5%, 71.67%, and 69.69% to 76.76%, 86%, and 89.85% respectively. This improvement means that each vehicle can carry more goods in a single delivery, thereby reducing the number of round trips and empty driving rates of the vehicle. At the same time, the increase in the full load rate also enhances the stability and reliability of the distribution system.

[0048] In order to prove the advantage of optimizing the departure time, the algorithm is run 10 times in each case to obtain the optimal solution, and the total cost of optimizing and not optimizing the departure time is compared, as shown in Figure 2. Figure 6 As shown. Figure 6 The results show that optimizing the departure time has a positive impact on the total cost. In the calculation examples of different scales, the total cost after optimization is reduced by an average of 4.34% (large scale), 4.08% (medium scale), and 0.67% (small scale). This reduction is mainly due to the fact that by accurately adjusting the departure time, the external temperature changes encountered by the vehicle during transportation are better matched, thereby reducing the demand for refrigeration, optimizing refrigeration efficiency, and avoiding energy waste from over-refrigeration.

[0049] In order to verify the superiority and performance of the strong multi-strategy spider bee algorithm proposed in this paper in model solving, it is compared with the new heuristic algorithms adaptive spiral flight sparrow search algorithm (ASFSSA), tuna optimization algorithm (TSO), and moth swarm optimization algorithm (MSA), which are also widely used and have excellent performance in recent years. At the same time, the particle swarm algorithm (PSO) is selected as a classic benchmark for comparison to show the improvement space and advantages of the algorithm of this invention compared with the classic algorithm. Each algorithm is run under the example L4, and the results are as follows Figure 7As shown. It can be seen that although PSO and MSA converge faster in the early stage, it is difficult to further optimize in the later stage due to the weak initial solution generation ability and the easy fall into the local optimum. In contrast, EMSWO, ASFSSA and TSO have more advantages in the early stage and can quickly approach the optimal solution area. Among them, EMSWO shows a faster decline trend and shows a significant convergence advantage in the early stage of iteration. Compared with ASFSSA and TSO, EMSWO has a lower final convergence value, indicating that it has a stronger optimization ability. In addition, EMSWO tends to be stable in the later stage of iteration, without obvious fluctuations or rebounds, showing excellent solution stability and convergence reliability.

[0050] The embodiment of the present invention discloses a multi-temperature co-distribution path planning method based on an enhanced multi-strategy spider bee algorithm. The present invention enriches the research content of flexible compartments in multi-temperature co-distribution path optimization, and proposes a dynamic adjustment method based on flexible compartments, so that distribution path planning is more flexible and efficient. Flexible compartments can adjust space allocation according to the temperature control requirements of different goods, improve loading efficiency and resource utilization in a multi-temperature zone co-distribution environment, and thus provide a more intelligent and adaptable solution for cold chain distribution. By optimizing the vehicle departure time strategy, the present invention effectively reduces energy consumption during transportation, especially in the face of long-distance, multi-temperature co-distribution cold chain transportation scenarios, optimizing the departure time can effectively avoid high temperature periods, reduce the vehicle's stay time in a high temperature environment, and thus reduce the refrigeration burden. The optimized departure time not only improves transportation efficiency, but also greatly reduces energy consumption, and enhances the environmental protection and economy of cold chain transportation. The present invention provides a comprehensive path optimization and energy efficiency management solution for the distribution of cold chain products such as pre-prepared dishes, combining flexible compartments with departure time optimization, which not only improves transportation efficiency, but also reduces refrigeration energy waste by accurately controlling the temperature control environment. By implementing this plan, companies can improve transportation efficiency and resource utilization while ensuring the quality of cold chain distribution, and promote the industry to develop in a more efficient and environmentally friendly direction.

[0051] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0052] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-temperature co-distribution path planning method based on an enhanced multi-strategy spider bee algorithm, characterized in that: include: Step 1: Obtain the vehicle resources, customer demand and location information of the distribution center, and establish a cooling cost calculation function based on the change of ambient temperature; Step 2: Based on the cooling cost calculation function, a multi-temperature distribution path planning model based on flexible compartments and ambient temperatures is constructed with the goal of minimizing the total distribution cost, and constraints of the multi-temperature distribution path planning model are constructed based on vehicle resources and customer information; Step 3: Use the enhanced multi-strategy spider bee algorithm that combines chaotic mapping, adaptive elite strategy and reciprocating population reduction strategy to solve the target of the multi-temperature co-distribution path planning model to obtain the optimal departure time and distribution path.

2. The multi-temperature co-distribution path planning method based on the enhanced multi-strategy spider bee algorithm according to claim 1 is characterized in that: In step 1, a cooling cost calculation function is established based on the change of ambient temperature, specifically: Divide the ambient temperature by hours, take each hour as a time zone, and calculate the ambient temperature of each time zone as follows: in, Indicates the ambient temperature of time zone m; m indicates the time of the time zone; Indicates the highest temperature of the day; Indicates the lowest temperature of the day; Represents hourly simulation Temperature value; is a fixed parameter; Assuming that the temperature is constant within each hour, the vehicle is considered to pass through one or more time zones. Suppose vehicle k departs from point i, and the departure time is expressed as , the next node visited is denoted as j, and the travel time from point i to point j is The service time at point j is expressed as , the heat transfer area of ​​the refrigerated and frozen compartments is as follows: in, represents the distance from node i to node j; v represents the speed of vehicle k; N represents the node set consisting of the customer point set and the distribution center, i,j∈N, when i=0, it represents the distribution center; p represents the p-type compartment of the distribution vehicle, p=1 represents the refrigerated type, and p=2 represents the frozen type; represents the demand for refrigerated goods at node j; represents the demand for type p cargo at node j; Z represents the total heat transfer area of ​​the carriage; represents the demand for refrigerated goods at node j; time The time zone m is represented by ,in, , Departure time The start time point and end time point corresponding to the time zone m to which it belongs, It also represents the start time of time zone m+1. Since the end time of time zone m coincides with the start time of time zone m+1, the end time of time zone m is represented by the start time of time zone m+1. The driving and service time is expressed as ,judge whether it is established; If established, then from From time to departure point j, the vehicles are at temperature The cooling cost during the period of driving is expressed as: in, represents the temperature in time zone m; It represents the heat transfer coefficient of the compartment; Indicates the temperature of the refrigerated compartment; Indicates the temperature of the refrigerated compartment; represents the unit fuel supply level; K represents the set of delivery vehicles; represents the cooling cost; represents the heat generated by the refrigerator during the delivery vehicle k from node i to node j; E represents the heat generated by a unit of gasoline; If not, then in the time period Vehicle temperature Driving during this period Expressed as: renew , , re-judge Is it established to obtain the refrigeration cost.

3. The multi-temperature co-distribution path planning method based on the enhanced multi-strategy spider bee algorithm according to claim 1 is characterized in that: In step 2, based on the cooling cost calculation function, a multi-temperature distribution path planning model based on flexible compartments and ambient temperature is constructed with the goal of minimizing the total distribution cost, as follows: in, represents fixed cost; K represents the set of delivery vehicles; represents the fixed resource consumption of starting the delivery vehicle k; Indicates whether the vehicle departs from the distribution center; represents the transportation cost; N represents the node set consisting of the customer point set and the distribution center, i,j∈N, when i=0, it represents the distribution center; Indicates whether the delivery vehicle k travels from node i to the next node j; Indicates the capacity of the delivery vehicle; represents the initial quantity of cargo loaded when delivery vehicle k departs from the distribution center; Indicates the fuel consumption rate of the delivery vehicle when it is fully loaded; Indicates the fuel consumption rate of the delivery vehicle when it is unloaded; Represents the distance from node i to node j; Indicates the unit fuel supply level; represents the loss cost; p represents the p-type compartment of the delivery vehicle, p=1 represents the refrigerated compartment, and p=2 represents the frozen compartment; It represents the damage rate of p-type goods; L represents the unit price of goods per unit quantity; represents the time required to travel from node i to node j; represents the service time of node j; represents the demand for type p goods at node j; represents the compartment of vehicle k Whether the demand for product p of customer i is met; represents the time window penalty cost; represents the time window of customer point i, where represents the earliest arrival time required by node i for the delivery vehicle to arrive, It represents the latest arrival time that node i requires the delivery vehicle to arrive; represents the arrival time of delivery vehicle k to node i; , Respectively represent the penalty coefficients for early and late arrival of vehicles; Represents the cooling cost.

4. The multi-temperature co-distribution path planning method based on the enhanced multi-strategy spider bee algorithm according to claim 1 is characterized in that: In step 2, the constraints of the multi-temperature co-distribution path planning model are constructed based on vehicle resources and customer information, as follows: Where N represents the node set consisting of the customer point set and the distribution center, i,j∈N, when i=0, it represents the distribution center; Indicates whether to start from the distribution center and rush to node j; Indicates whether to return to the distribution center from a certain i node; K represents the set of distribution vehicles; Indicates whether the delivery vehicle k travels from node i to the next node j; Indicates whether the p-type compartment of vehicle k meets the p-type commodity demand of customer j; p represents the p-type compartment of the delivery vehicle, p=1 represents refrigerated compartment, and p=2 represents frozen compartment; Indicates whether vehicle k goes from node j to node i; represents the demand for type p goods at node i; Indicates the capacity of the delivery vehicle; represents the time when delivery vehicle k departs from point j; represents the time when delivery vehicle k departs from point i; represents the time required to travel from node i to node j; represents the service time of node j; S represents the set of path vertices; represents the departure time of delivery vehicle k from the distribution center; represents the delivery time window of the distribution center, represents the earliest time when the distribution center requires delivery vehicle k; represents the latest time; m represents the number of customers; Indicates whether the delivery vehicle k travels from node i to the next node j; Indicates whether the p-type compartment of delivery vehicle k meets the p-type commodity demand of customer i; Indicates the departure time from the last customer point on the path; Indicates the latest time to return to the distribution center.

5. The multi-temperature co-distribution path planning method based on the enhanced multi-strategy spider bee algorithm according to claim 1 is characterized in that: In step 3, the enhanced multi-strategy spider bee algorithm combining chaotic mapping, adaptive elite strategy and reciprocating population reduction strategy is used to solve the target of the multi-temperature co-distribution path planning model, specifically: Initialization parameters, including: population size G, minimum population size , upper bound ub, lower bound nb, number of iterations , trade-off rate TR, crossover rate Cr, current optimal individual mutation times p_m, global optimal individual mutation times g_m; define and initialize the current iteration times as T=0; The tent chaotic mapping initialization strategy is used to generate the initial spider bee population, and the generated population is encoded and decoded, and the fitness value set of the initial population is calculated using the multi-temperature co-matching path planning model. , from the fitness value set of the initial population Select the minimum fitness value The corresponding spider bee individual is recorded as the optimal spider bee individual in the initial population and the global optimal spider bee individual , the corresponding fitness value is the global optimal fitness value ; Enter the main loop and generate a random number As the search agent index, , if , execute the prey behavior to update the spider wasp population, otherwise execute the mating behavior to update the spider wasp population; when executing the prey behavior to update the spider wasp population, let , when , generate a random number p, , when , use the search behavior to update the individuals in the spider wasp population, when , adopt the following and escaping behaviors to update the individuals in the spider wasp population; when , use the nesting behavior to update the individuals in the spider wasp population, generate a random number 、 , , when , use the current optimal solution to update the individuals in the spider wasp population, when , randomly select a spider wasp in the population and use an additional step size to update the individuals in the spider wasp population; when executing the mating behavior to update the spider wasp population, randomly select spider wasp individuals 、 、 to generate parameters 、 , use the parameters 、 to generate new spider wasp individuals. For each newly generated spider wasp individual in the population, generate a random number rand. When rand < Cr, update the corresponding individual in the population to the new spider wasp individual, otherwise do not update; The currently obtained new spider bee individuals are encoded and decoded, and the fitness value set of all individuals in the spider bee population is calculated according to the multi-temperature co-matching path planning model. , respectively compare the current collection The fitness value of the spider bee population before the update is compared with the fitness value of the corresponding individual in the spider bee population. If the current one is better, the corresponding individual in the updated population is the current spider bee individual. Otherwise, the original individual is retained and compared in turn to update the spider bee population. ; Encode and decode the new spider bee individuals currently obtained, calculate the fitness value set of all individuals in the updated spider bee population, and take the optimal fitness value And the global optimal fitness value For comparison, if the current fitness value is better, update the optimal spider bee individual and the optimal fitness value is the current spider bee individual and the corresponding fitness value; Let the current number of iterations , and use the adaptive elite mutation strategy to perform elite mutation p_m times on each individual in the current spider bee population, and check whether the new spider bee position exceeds the upper and lower bounds ub and nb of the search space. If so, correct it to the boundary; use the adaptive elite mutation strategy to perform elite mutation on the new population The best spider bee individual in the population undergoes elite mutation g_m times. If the fitness value of the individual after each mutation is better, the corresponding spider bee individual in the population is updated. and fitness value The current spider bee individual and its fitness value are used to obtain the new spider bee population after mutation. ; Compare and The size of Better, then update the optimal spider bee individual and the optimal fitness value The current spider bee individual and the corresponding fitness value are used, and the population size G is updated using the reciprocating population reduction strategy until the number of reciprocating cycles is reached, and it is determined whether the current iteration number T is greater than , then the output corresponds to the optimal fitness value and the optimal spider bee individual , otherwise return to the main loop to repeat the steps and update the solution.

6. The multi-temperature co-distribution path planning method based on the enhanced multi-strategy spider bee algorithm according to claim 5 is characterized in that: The tent chaotic map initialization strategy is used to generate the initial spider bee population, and the generated population is encoded and decoded, specifically: An initial matrix of 2M rows and G columns is randomly generated, and the initial matrix is ​​updated using tent chaotic mapping to generate the initial spider bee population, as follows: in, represents the mth value of the gth spider bee individual; chaos parameter ; Represents the mth value of the gth parameter in the initial matrix; Indicates the population size; represents a chaotic variable; rand represents a random number; ub and nb represent upper and lower bounds respectively; the generated initial spider bee population is as follows: Each row represents a spider wasp individual , ; Each spider bee individual represents a set of solutions, simplifying the spider bee population to ; For any individual spider bee , Indicates the departure time. represents the customer node; the position-order coding method is introduced to decode the delivery path of the last M nodes. Sort in ascending order, record the number and then convert it into a natural number code, which represents the customer point number; assign the route according to the vehicle capacity constraint, if the sum of the customer point demand is met, then add the customer point to the route, otherwise generate a new route until all nodes are assigned; for the vehicle departure time , then directly allocate them from front to back according to the number of generated paths.

7. The multi-temperature co-distribution path planning method based on the enhanced multi-strategy spider bee algorithm according to claim 5 is characterized in that: When executing the prey behavior to update the spider bee population, the search behavior is used to update the individuals in the spider bee population, specifically: Generate random numbers , , ;when When , the parameters are generated , , where rn1 is a random number; two spider wasp individuals are randomly selected from the population , , use the following formula to update the parameters of each spider bee individual: when When , the parameters are generated , ,in , , , rand represents a random number; the following formula is used to update the parameters of each spider bee individual: The following and escaping behaviors are used to update individuals in the spider bee population, specifically: when When , generating parameter C, , use the following formula to update the parameters of each spider bee individual: when When , the parameter matrix is ​​generated , ,in , use the following formula to update the parameters of each spider bee individual: Use nesting behavior to update individuals in the spider bee population, specifically: when When using the global optimal spider bee individual Updated the spider bee individuals as follows: when When , , , use the following formula to generate new spider bee individuals: in, Indicates the parameters for levy flight generation.

8. The multi-temperature co-distribution path planning method based on the enhanced multi-strategy spider bee algorithm according to claim 5 is characterized in that: When performing mating behavior to update the spider bee population, randomly select spider bee individuals , , Generate Parameters , , specifically: Calculating the Matrix , randomly select a spider bee individual , ; calculate , randomly select a spider bee individual , , ; Using parameters , to generate new spider wasp individuals. For each newly generated spider wasp individual in the population, generate a random number rand. When rand < Cr, update the corresponding individual in the population with the new spider wasp individual using the following formula, otherwise do not update; Boundary check: check whether the new spider bee position exceeds the upper and lower bounds ub and nb of the search space. If so, correct it to the boundary to obtain the updated spider bee population of the Tth generation. , .

9. The multi-temperature co-distribution path planning method based on the enhanced multi-strategy spider bee algorithm according to claim 5 is characterized in that: The adaptive elite mutation strategy is used to perform elite mutation on each individual in the current spider bee population, as follows: in, , Indicates randomly selecting a spider wasp individual from the population.

10. The multi-temperature co-distribution path planning method based on the enhanced multi-strategy spider bee algorithm according to claim 5, characterized in that: The population size G is updated using the reciprocating population reduction strategy as follows: Among them, % is the remainder operator, Indicates the number of reciprocating cycles.

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