Material offline service task scheduling method and system based on improved population algorithm

Abstract

The invention relates to the field of medical material scheduling, and discloses a material offline service task scheduling method and system based on an improved population algorithm, and the method comprises the following specific steps: constructing a task scheduling model; initializing a task scheduling model; applying the initial population; decoding individuals of the population to obtain a distribution scheme expressed by each individual, and calculating a fitness value of the distribution scheme; on the basis of the fitness values, grouping is carried out according to the fact that each breeding horse and a plurality of small horses serve as one group; selecting an individual with the maximum fitness value in the current population as a global optimal individual; sequentially performing wild horse grazing and small horse mating operation and team leader operation to update individuals; 2-Opt, overturning and exchanging subsequences are applied to locally search the wild horse, a leading function is applied to optimize and update the seeded horse, and a following function is applied to optimize and update the small horse; and outputting an optimal solution. The method solves the problems of low scheduling efficiency and easy falling into local optimum in the prior art, and has the characteristics of good convergence capability and high optimization efficiency.

Description

Technical Field

[0001] The present invention relates to the field of medical material scheduling, and more specifically, to a material offline service task scheduling method and system based on an improved population algorithm. Background Art

[0002] With the increasing severity of population aging, providing patients with more efficient and high-quality medical services in a timely manner is an urgent problem that needs to be solved in current medical services. Therefore, the development of advanced and practical medical material service task scheduling methods has important economic significance and social value.

[0003] The problem of scheduling medical supplies service tasks can be described as: providing medical supplies distribution services for remote offline patients, using a distribution center, which uses multiple delivery vehicles of the same type to deliver medical supplies to several patients; each vehicle departs from the distribution center, delivers medical supplies to all patients it is responsible for within the service time window required by the patient, and finally returns to the distribution center; each patient is required to be delivered medical supplies by exactly one vehicle. For all patients being served, it is determined that all patients can be served within their corresponding time windows, and the total driving distance of all vehicles in the distribution plan is required to be the shortest.

[0004] The prior art has a method for scheduling offline service tasks for remote health monitoring of multiple vehicles. Aiming at the problem of scheduling offline service tasks for remote health monitoring of multiple vehicles, an improved adaptive symbiotic biological search algorithm is proposed for solving the problem. The specific steps are: establishing a mathematical model for scheduling offline service tasks for remote health monitoring of multiple vehicles; defining a fitness function; setting control parameters of the algorithm; initialization; performing genetic fine-tuning and individual decoding on the initial population, and calculating the fitness value; determining the global optimal individual; sequentially performing adaptive mutualistic symbiotic operations and adaptive partial symbiotic operations to update the population; performing genetic fine-tuning and individual decoding on the updated population, and calculating the fitness value; updating the optimal solution; judging whether the maximum number of iterations has been reached; and outputting the optimal solution.

[0005] However, the scheduling efficiency of the existing technology for logistics distribution is low and it is easy to fall into the local optimum, so it is necessary to optimize and improve it. Inventing a scheduling method for offline service tasks of medical supplies is a technical problem that needs to be solved urgently in this technical field. Summary of the invention

[0006] In order to solve the problems of low scheduling efficiency and easy falling into local optimality in the prior art, the present invention provides a material offline service task scheduling method and system based on an improved population algorithm, which has the characteristics of good convergence ability and high optimization efficiency.

[0007] In order to achieve the above-mentioned purpose of the present invention, the technical scheme adopted is as follows: The offline material service task scheduling method based on the improved population algorithm includes the following specific steps: Build a task scheduling model; Set individual fitness function; initialize task scheduling model; Apply logistic mapping to generate the initial population of the model and assign individuals to stallions and ponies respectively; Decode the individuals in the population, determine the values ​​of the variables in the task scheduling model, make sure that the service time window is not violated, obtain the delivery plan expressed by each individual, and calculate its fitness value; Based on the fitness value, each stallion is grouped with a number of ponies; the individual with the largest fitness value in the current population is selected as the global optimal individual; Individuals are updated by sequentially performing wild horse grazing and pony mating operations, and team leader operations; Apply 2-Opt, flip and swap subsequences to perform local search on wild horses; Apply the leading function to optimize and update the stallion, and apply the following function to optimize and update the pony; Update the optimal solution; The hybrid improved goose optimization algorithm is used to optimize the global optimal solution; Iteratively update the optimal solution; Output the optimal solution as the optimal offline service task scheduling plan.

[0008] Preferably, the task scheduling model is specifically:

[0009] in, Dist is the total distance travelled by all vehicles; N is the number of patients, and the patients are numbered 1, 2, ..., N , the distribution center number is 0; M The number of vehicles owned by the distribution center; T ij For patients i , j The distance between i , j =0,1,... , N , i ≠ j ,; x ijm is a variable that is either 0 or 1. i , j =0,1,..., N , m =0,1,..., M ; x ijm =1 indicates vehiclem From the patient i Driving to the patient j ; g i For patients i The demand, i =1,2,..., N ; z im is a variable that is either 0 or 1. i =0,1,..., N ; m =1,2,..., M; z im =1 means the patient i By vehicle m delivery; cap Indicates the maximum cargo capacity of each vehicle; v Indicates the vehicle's speed; t ij Indicates that the vehicle is from the patient i arrive j time; a i Indicates patient i The left time window of b i Indicates patient i The right time window of s im Indicates patient i service time; each patient can only be served by one vehicle; each patient must be served within the given service time window.

[0010] Furthermore, the fitness function is specifically:

[0011] Initialize the task scheduling model, specifically: Setting the population size pop_N , maximum number of iterations M_Iter , current iteration number Iter、 Stallion ratio PS、 Mating probability PC ; Calculate the number of stallions Sta_N = pop_N * PS and the number of ponies foal_N = pop_N *(1- PS ).

[0012] Furthermore, we apply logistic mapping to generate the initial population of the model. The specific steps are as follows: The generated initial population is a real number encoding method, record i( i =1,2,..., pop_N ) individuals are W i : , j =1,2,..., b in, w ij For the i The individual j genes, .

[0013] Furthermore, the individuals in the population are decoded, specifically in the following steps: By making each individual W i ( i =1,2,..., pop_N ) are mapped to the integer domain according to the index value, constructing ; greater than N A few T i The components are grouped, each group is an ordered set, corresponding to a delivery plan for a vehicle service.

[0014] Furthermore, individuals are updated by sequentially executing the wild horse grazing and pony mating operations and the team leader operation. The specific steps are as follows: For each individual pony foal i ( i =1,2,..., pop_N *( 1 - PS )) For grazing:

[0015] in, P is a vector consisting of 0s and 1s. , are all random vectors uniformly distributed in the range [0, 1]. R 2 is a random number in [0, 1]; SEQ To meet the conditions P ==0 random vector R 1 The returned index value, Θ is the dot product; Y It is an adaptive mechanism; TR Represents a coefficient that decreases linearly from 1 to 0; For group member Xiao Ma j Current location, Lead the stallion for the group in his current position; Pony mating, specifically:

[0016] in, For Group i Medium q Rejoining a group after leaving the group i The individual position of For Group j Medium z Rejoining a group after leaving the group j individual location; For Group k Medium p By Group i In q and Group j In z The offspring produced by mating, and are their parent individuals respectively; Team leader operations include the following steps: Stallion Operations on Team Leaders Sta i ( i =1,2,..., pop_N * PS )calculate:

[0017] in, Yes Group i The leader's next position, Wbest is the global optimal value, Yes Group i The leader's current position, Y is the adaptive parameter for calculation, R is a random number in [-2, 2], π=3.14; Conduct communication and select leadership operations:

[0018] in, foal G,i The best member of the group, Dist Represents the path distance of the distribution plan; if the best member in the group is better than the group leader, the group leader and member will be exchanged.

[0019] Furthermore, 2-Opt, flipping and swapping subsequences are applied to perform local search on wild horses, which specifically includes the following steps: Perform 2-Opt operation on wild horse individuals, select the values ​​of any two dimensions in the individual and exchange them to obtain a new individual, compare the fitness value of the new individual with the original individual, and if the fitness value of the new individual is greater than that of the original individual, update the individual, specifically: Select In i , j The two dimensions are exchanged, and after the exchange, ; Perform a flip operation on the wild horse individuals, select the values ​​of any two dimensions in the individuals and flip them to obtain new individuals. Compare the fitness values ​​of the new individuals with those of the original individuals. If the fitness value of the new individual is greater than that of the original individual, the individual will be updated. Specifically: Select In i , j The two dimensions are flipped and the flipped .

[0020] The exchange subsequence performs a local search on the wild horse individuals, specifically: Select In i, j, k, l The four dimensions are exchanged, the first two positions are used as subsequence 1, and the remaining positions are used as subsequence 2, and the two subsequences are exchanged. .

[0021] Furthermore, the leading function is applied to optimize and update the stallion, and the following function is applied to optimize and update the pony. The specific steps are as follows: Improvements to the stallion application leading function:

[0022] For the improved individual, Wbest is the global optimal value, is the current value; Improve the follow function applied to the pony:

[0023] foal i+1 An improved individual of the pony. θ is the angle between the pony's position relative to the group leader and the global optimal individual, which is [0,45°], ; Continuously update the optimal solution.

[0024] Furthermore, the hybrid improved goose optimization algorithm is used to optimize the global optimal solution. The specific steps are as follows: ;

[0025] in, randn () is a random function that obeys the standard normal distribution; ; T Represents the time parameter; get the new W best Exchange with the worst stallion and compare with the current optimal value. If it is better than the optimal value, update the optimal value.

[0026] The material offline service task scheduling system based on the improved population algorithm includes a model building module, an initialization module, a fitness value calculation module, an iterative optimization module, a local search module, a leading function and a following function optimization module, a hybrid improved goose optimization algorithm module, and an optimal result output module; The model building module is used to build a task scheduling model; The initialization module is used to set the fitness function of the individual; initialize the task scheduling model; apply logistic mapping to generate the initial population of the model, and assign the individuals to the stallion and the pony respectively; The fitness value calculation module is used to decode the population individuals, determine the values ​​of the variables in the task scheduling model, determine that the service time window is not violated, obtain the delivery plan expressed by each individual, and calculate its fitness value; The iterative optimization module is used to group each stallion and a number of ponies into a group based on the fitness value; select the individual with the largest fitness value in the current population as the global optimal individual; The local search module is used to perform local search on wild horses by applying 2-Opt, flipping and swapping subsequences; The leading function and following function optimization module is used to apply the leading function to optimize and update the stallion, and apply the following function to optimize and update the pony; The hybrid improved goose optimization algorithm module is used to optimize the global optimal solution using the hybrid improved goose optimization algorithm; The optimal result output module is used to output the optimal solution as the optimal offline service task scheduling solution.

[0027] The beneficial effects of the present invention are as follows: The present invention discloses a material offline service task scheduling method based on an improved population algorithm. The optimal solution to the medical material offline service task scheduling problem is obtained by constructing a task scheduling model, constructing a population, decoding the population and solving its fitness value, and iteratively solving the optimal fitness value. The present invention uses 2-Opt, flipping and swapping subsequences to perform local search on wild horses, uses a leading function to optimize and update stallions, and uses a following function to optimize and update ponies. Compared with the prior art, the method can perform optimization faster and more accurately, and has the characteristics of fast running speed, not easy to fall into local optimum, and good convergence ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of the material offline service task scheduling method based on the improved population algorithm of the present invention; Figure 2 The figure is a schematic diagram of the optimal solution obtained by solving an embodiment of the material offline service task scheduling method based on the improved population algorithm of the present invention.

[0029] Figure 3 The figure is a schematic diagram showing a comparison between the convergence speed obtained by solving an embodiment of the material offline service task scheduling method based on the improved population algorithm of the present invention and the convergence speed of a genetic algorithm (GA) and an Ivy algorithm (IVYA).

[0030] Figure 4 It is a schematic diagram of the material offline service task scheduling system based on the improved population algorithm of the present invention. DETAILED DESCRIPTION

[0031] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] Example 1 like Figure 1 As shown in FIG. 1 , the offline material service task scheduling method based on the improved population algorithm includes the following specific steps: Build a task scheduling model; Set individual fitness function; initialize task scheduling model; Apply logistic mapping to generate the initial population of the model and assign individuals to stallions and ponies respectively; Decode the individuals in the population, determine the values ​​of the variables in the task scheduling model, make sure that the service time window is not violated, obtain the delivery plan expressed by each individual, and calculate its fitness value; Based on the fitness value, each stallion is grouped with a number of ponies; the individual with the largest fitness value in the current population is selected as the global optimal individual; Individuals are updated by sequentially performing wild horse grazing and pony mating operations, and team leader operations; Apply 2-Opt, flip and swap subsequences to perform local search on wild horses; Apply the leading function to optimize and update the stallion, and apply the following function to optimize and update the pony; Update the optimal solution; The hybrid improved goose optimization algorithm is used to optimize the global optimal solution; Iteratively update the optimal solution; Output the optimal solution as the optimal offline service task scheduling plan.

[0033] Example 2 In a specific embodiment, the task scheduling model is specifically:

[0034] in, Dist is the total distance travelled by all vehicles; N is the number of patients, and the patients are numbered 1, 2, ..., N , the distribution center number is 0; M The number of vehicles owned by the distribution center; T ij For patients i , j The distance between i , j =0,1,... , N , i ≠ j ,; x ijm is a variable that is either 0 or 1. i , j =0,1,..., N , m =0,1,..., M ; x ijm =1 indicates vehicle m From the patient i Driving to the patient j ; g i For patients i The demand, i =1,2,..., N ; z im is a variable that is either 0 or 1. i =0,1,..., N ; m =1,2,..., M; z im =1 means the patient iBy vehicle m delivery; cap Indicates the maximum cargo capacity of each vehicle; v Indicates the vehicle's speed; t ij Indicates that the vehicle is from the patient i arrive j time; a i Indicates patient i The left time window of b i Indicates patient i The right time window of s im Indicates patient i service time; each patient can only be served by one vehicle; each patient must be served within the given service time window.

[0035] In a specific embodiment, the fitness function is specifically:

[0036] In this embodiment, the task scheduling model is initialized, specifically: setting the population size popN =50, maximum number of iterations M_I =300, current number of iterations Iter、 Stallion ratio PS、 Mating probability PC Current iteration number Iter =1, stallion ratio PS =0.2, mating probability PC = 0.13, calculate the number of stallions S ta_N = popN * PS and the number of ponies foal_N = popN *(1- PS ).

[0037] Initialize the task scheduling model, specifically: Setting the population size pop_N , maximum number of iterations M_Iter , current iteration number Iter、 Stallion ratio PS、 Mating probability PC ; Calculate the number of stallions Sta_N = pop_N * PS and the number of ponies foal_N = pop_N *(1- PS ).

[0038] In a specific embodiment, the initial population of the model is generated by applying logistic mapping, and the specific steps are as follows: The generated initial population is a real number encoding method, record i ( i =1,2,..., pop_N ) individuals are W i : , j =1,2,..., b in, w ij For the i The individual j genes, .

[0039] In a specific embodiment, decoding the population individuals is performed by the following steps: By making each individual W i ( i =1,2,..., pop_N ) are mapped to the integer domain according to the index value, constructing ; greater than N A few T i The components are grouped, each group is an ordered set, corresponding to a delivery plan for a vehicle service.

[0040] In a specific embodiment, individuals are updated by sequentially executing wild horse grazing and pony mating operations and team leadership operations, and the specific steps are: For each individual pony foal i ( i =1,2,..., pop_N *( 1 - PS )) For grazing:

[0041] in, P is a vector consisting of 0s and 1s. , are all random vectors uniformly distributed in the range [0, 1]. R 2 is a random number in [0, 1]; SEQ To meet the conditions P ==0 random vector R 1 The returned index value, Θ is the dot product; Y It is an adaptive mechanism; TRRepresents a coefficient that decreases linearly from 1 to 0; For group member Xiao Ma j Current location, Lead the stallion for the group in his current position; Pony mating, specifically:

[0042] in, For Group i Medium q Rejoining a group after leaving the group i The individual position of For Group j Medium z Rejoining a group after leaving the group j individual location; For Group k Medium p By Group i In q and Group j In z The offspring produced by mating, and are their parent individuals respectively; Team leader operations include the following steps: Stallion Operations on Team Leaders Sta i ( i =1,2,..., pop_N * PS )calculate:

[0043] in, Yes Group i The leader's next position, Wbest is the global optimal value, Yes Group i The leader's current position, Y is the adaptive parameter for calculation, R is a random number in [-2, 2], π=3.14; Conduct communication and select leadership operations:

[0044] in, foal G,i The best member of the group, Dist Represents the path distance of the distribution plan; if the best member in the group is better than the group leader, the group leader and member will be exchanged.

[0045] In a specific embodiment, 2-Opt, flipping and swapping subsequences are applied to perform local search for wild horses, specifically including the following steps: Perform 2-Opt operation on wild horse individuals, select the values ​​of any two dimensions in the individual and exchange them to obtain a new individual, compare the fitness value of the new individual with the original individual, and if the fitness value of the new individual is greater than that of the original individual, update the individual, specifically: Select In i , j The two dimensions are exchanged, and after the exchange, ; Perform a flip operation on the wild horse individuals, select the values ​​of any two dimensions in the individuals and flip them to obtain new individuals. Compare the fitness values ​​of the new individuals with those of the original individuals. If the fitness value of the new individual is greater than that of the original individual, the individual will be updated. Specifically: Select In i , j The two dimensions are flipped and the flipped .

[0046] The exchange subsequence performs a local search on the wild horse individuals, specifically: Select In i, j, k, l The four dimensions are exchanged, the first two positions are used as subsequence 1, and the remaining positions are used as subsequence 2, and the two subsequences are exchanged. .

[0047] In a specific embodiment, the leading function is applied to optimize and update the stallion, and the following function is applied to optimize and update the pony. The specific steps are: Improvements to the stallion application leading function:

[0048] For the improved individual, Wbest is the global optimal value, is the current value; Improve the follow function applied to the pony:

[0049] foal i+1 An improved individual of the pony. θ is the angle between the pony's position relative to the group leader and the global optimal individual, which is [0,45°], ; Continuously update the optimal solution.

[0050] In a specific embodiment, a hybrid improved goose optimization algorithm is used to optimize the global optimal solution, and the specific steps are as follows: ;

[0051] in, randn () is a random function that obeys the standard normal distribution; ; T represents the time parameter, taking [0.03,0.5]; get the new W best Exchange with the worst stallion and compare with the current optimal value. If it is better than the optimal value, update the optimal value.

[0052] In this embodiment, the optimal solutions for the distribution center and patient information and output are shown in Tables 1 and Figure 2 As shown; the convergence speed of the method of the present invention is compared with that of the genetic algorithm (GA) and the Ivy algorithm (IVYA). Figure 3 As shown: Table 1 Information of the distribution center and patients in the embodiment

[0053] In summary, the present invention proposes a discrete wild horse optimization algorithm for solving a hybrid improved goose optimization algorithm. The present invention can effectively solve a medical material offline service task scheduling problem, and has the characteristics of fast running speed, not easy to fall into local optimum, good convergence ability, and high optimization efficiency.

[0054] Example 3 like Figure 4 As shown, the offline material service task scheduling system based on the improved population algorithm includes a model building module, an initialization module, a fitness value calculation module, an iterative optimization module, a local search module, a leading function and a following function optimization module, a hybrid improved goose optimization algorithm module, and an optimal result output module; The model building module is used to build a task scheduling model; The initialization module is used to set the fitness function of the individual; initialize the task scheduling model; apply logistic mapping to generate the initial population of the model, and assign the individuals to the stallion and the pony respectively; The fitness value calculation module is used to decode the population individuals, determine the values ​​of the variables in the task scheduling model, determine that the service time window is not violated, obtain the delivery plan expressed by each individual, and calculate its fitness value; The iterative optimization module is used to group each stallion and a number of ponies into a group based on the fitness value; select the individual with the largest fitness value in the current population as the global optimal individual; The local search module is used to perform local search on wild horses by applying 2-Opt, flipping and swapping subsequences; The leading function and following function optimization module is used to apply the leading function to optimize and update the stallion, and apply the following function to optimize and update the pony; The hybrid improved goose optimization algorithm module is used to optimize the global optimal solution using the hybrid improved goose optimization algorithm; The optimal result output module is used to output the optimal solution as the optimal offline service task scheduling solution.

[0055] Obviously, the above embodiments of the present invention are only examples for clearly explaining the present invention, and are not intended to limit the implementation methods of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A material offline service task scheduling method based on an improved population algorithm, characterized by: The specific steps include: Build a task scheduling model; Set individual fitness function; initialize task scheduling model; Apply logistic mapping to generate the initial population of the model and assign individuals to stallions and ponies respectively; Decode the individuals in the population, determine the values ​​of the variables in the task scheduling model, make sure that the service time window is not violated, obtain the delivery plan expressed by each individual, and calculate its fitness value; Based on the fitness value, each stallion is grouped with a number of ponies; the individual with the largest fitness value in the current population is selected as the global optimal individual; Individuals are updated by sequentially performing wild horse grazing and pony mating operations, and team leader operations; Apply 2-Opt, flip and swap subsequences to perform local search on wild horses; Apply the leading function to optimize and update the stallion, and apply the following function to optimize and update the pony; Update the optimal solution; The hybrid improved goose optimization algorithm is used to optimize the global optimal solution; Iteratively update the optimal solution; Output the optimal solution as the optimal offline service task scheduling plan.

2. The method for scheduling offline material service tasks based on an improved population algorithm according to claim 1 is characterized in that: The task scheduling model is specifically as follows: in, Dist is the total distance travelled by all vehicles; N is the number of patients, and the patients are numbered 1, 2, ..., N , the distribution center number is 0; M The number of vehicles owned by the distribution center; T ij For patients i , j The distance between i , j =0,1,... , N , i ≠j ,; x ijm is a variable that is either 0 or 1. i , j =0,1,..., N , m =0,1,..., M ; x ijm =1 indicates vehicle m From the patient i Driving to the patient j ; g i For patients i The demand, i =1,2,..., N ; z im is a variable that is either 0 or 1. i =0,1,..., N ; m =1,2,..., M;z im =1 means the patient i By vehicle m delivery; cap Indicates the maximum cargo capacity of each vehicle; v Indicates the vehicle's speed; t ij Indicates that the vehicle is from the patient i arrive j time; a i Indicates patient i The left time window of b i Indicates patient i The right time window of s im Indicates patient i service time; each patient can only be served by one vehicle; each patient must be served within the given service time window.

3. The method for scheduling offline material service tasks based on an improved population algorithm according to claim 2 is characterized in that: The fitness function is specifically: Initialize the task scheduling model, specifically: Setting the population size pop_N , maximum number of iterations M_Iter , current iteration number Iter, Stallion ratio PS, Mating probability PC ; Calculate the number of stallions Sta_N = pop_N * PS and the number of ponies foal_N = pop_N *(1- PS ).

4. The method for scheduling offline material service tasks based on an improved population algorithm according to claim 3 is characterized in that: Apply logistic mapping to generate the initial population of the model. The specific steps are: The generated initial population is a real number encoding method, record i ( i =1,2,..., pop_N ) individuals are W i : ， j =1,2,..., b in, w ij For the i The individual j genes, .

5. The method for scheduling offline material service tasks based on an improved population algorithm according to claim 4 is characterized in that: Decode the individuals in the population, specifically the steps: By making each individual W i ( i =1,2,..., pop_N ) are mapped to the integer domain according to the index value, constructing ; greater than N A few T i The components are grouped, each group is an ordered set, corresponding to a delivery plan for a vehicle service.

6. The method for scheduling offline material service tasks based on an improved population algorithm according to claim 5 is characterized in that: Individuals are updated by sequentially executing wild horse grazing, pony mating, and team leader operations. The specific steps are: For each individual pony foal i ( i =1,2,..., pop_N *( 1-PS )) For grazing: in, P is a vector consisting of 0s and 1s. , are all random vectors uniformly distributed in the range [0, 1]. R 2 is a random number in [0, 1]; SEQ To meet the conditions P ==0 random vector R 1 returns the index value, Θ is the dot product; Y It is an adaptive mechanism; TR Represents a coefficient that decreases linearly from 1 to 0; For group member Xiao Ma j Current location, Lead the stallion for the group in his current position; Pony mating, specifically: in, For Group i Medium q Rejoining a group after leaving the group i The individual position of For Group j Medium z Rejoining a group after leaving the group j individual location; For Group k Medium p By Group i In q and Group j In z The offspring produced by mating, and are their parent individuals respectively; Team leader operations include the following steps: Stallion Operations on Team Leaders Sta i ( i =1,2,..., pop_N * PS )calculate: in, Yes Group i The leader's next position, Wbest is the global optimal value, Yes Group i The leader's current position, Y is the adaptive parameter for calculation, R is a random number in [-2, 2], π=3.14; Conduct communication and select leadership operations: in, foal G,i The best member of the group, Dist Represents the path distance of the distribution plan; if the best member in the group is better than the group leader, the group leader and member will be exchanged.

7. The method for scheduling offline material service tasks based on an improved population algorithm according to claim 6 is characterized in that: Apply 2-Opt, flip and swap subsequences to perform local search for wild horses, which includes the following steps: Perform 2-Opt operation on wild horse individuals, select the values ​​of any two dimensions in the individual and exchange them to obtain a new individual, compare the fitness value of the new individual with the original individual, and if the fitness value of the new individual is greater than that of the original individual, update the individual, specifically: Select In i , j The two dimensions are exchanged, and after the exchange, ; Perform a flip operation on the wild horse individuals, select the values ​​of any two dimensions in the individuals and flip them to obtain new individuals. Compare the fitness values ​​of the new individuals with those of the original individuals. If the fitness value of the new individual is greater than that of the original individual, the individual will be updated. Specifically: Select In i , j The two dimensions are flipped and the flipped : The exchange subsequence performs a local search on the wild horse individuals, specifically: Select In i, j, k, l The four dimensions are exchanged, the first two positions are used as subsequence 1, and the remaining positions are used as subsequence 2, and the two subsequences are exchanged. .

8. The method for scheduling offline material service tasks based on an improved population algorithm according to claim 7 is characterized in that: Apply the leading function to optimize and update the stallion, and apply the following function to optimize and update the pony. The specific steps are: Improvements to the stallion application leading function: For the improved individual, Wbest is the global optimal value, is the current value; Improve the follow function applied to the pony: foal i+1 An improved individual of the pony. θ is the angle between the pony's position relative to the group leader and the global optimal individual, which is [0,45°], ; Continuously update the optimal solution.

9. The method for scheduling offline material service tasks based on an improved population algorithm according to claim 8 is characterized in that: The hybrid improved goose optimization algorithm is used to optimize the global optimal solution. The specific steps are as follows: ； in, randn () is a random function that obeys the standard normal distribution; ; T Represents the time parameter; get the new W best Exchange with the worst stallion and compare with the current optimal value. If it is better than the optimal value, update the optimal value.

10. The offline material service task scheduling system based on the improved population algorithm is characterized by: It includes model building module, initialization module, fitness value calculation module, iterative optimization module, local search module, leading function and following function optimization module, hybrid improved goose optimization algorithm module, and optimal result output module; The model building module is used to build a task scheduling model; The initialization module is used to set the fitness function of the individual; initialize the task scheduling model; apply logistic mapping to generate the initial population of the model, and assign the individuals to the stallion and the pony respectively; The fitness value calculation module is used to decode the population individuals, determine the values ​​of the variables in the task scheduling model, determine that the service time window is not violated, obtain the delivery plan expressed by each individual, and calculate its fitness value; The iterative optimization module is used to group each stallion and a number of ponies into a group based on the fitness value; select the individual with the largest fitness value in the current population as the global optimal individual; The local search module is used to perform local search on wild horses by applying 2-Opt, flipping and swapping subsequences; The leading function and following function optimization module is used to apply the leading function to optimize and update the stallion, and apply the following function to optimize and update the pony; The hybrid improved goose optimization algorithm module is used to optimize the global optimal solution using the hybrid improved goose optimization algorithm; The optimal result output module is used to output the optimal solution as the optimal offline service task scheduling solution.

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