CN114169804B - Optimized scheduling method for transport vehicles of multiple distribution centers - Google Patents

Optimized scheduling method for transport vehicles of multiple distribution centers Download PDF

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CN114169804B
CN114169804B CN202210015157.6A CN202210015157A CN114169804B CN 114169804 B CN114169804 B CN 114169804B CN 202210015157 A CN202210015157 A CN 202210015157A CN 114169804 B CN114169804 B CN 114169804B
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陈文博
王娟
钱斌
胡蓉
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Kunming University of Science and Technology
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Abstract

The invention relates to an optimized dispatching method for transport vehicles of multiple distribution centers, and belongs to the technical field of intelligent optimized dispatching of logistics transportation. Firstly, establishing a sequencing and scheduling model aiming at the distribution process of the transport vehicles with multiple distribution centers and establishing an optimization target, wherein the sequencing and scheduling model is established according to the number of distribution centers, the number of delivered vehicles, the distribution process of the vehicles and the mutual position distribution relation between a client and the distribution centers, and the optimization target is to minimize the total transport distance of the vehicles; then, an improved ant colony optimization scheduling algorithm with a learning mechanism is designed to optimize the target. The invention can make the distribution process clear and clear, and the scheduling scheme is feasible and effective and easy to popularize; the algorithm stagnation phenomenon caused by the accumulation of a large amount of invalid pheromones can be avoided, so that the algorithm convergence speed is increased; the method can rapidly acquire the approximate optimal scheduling scheme of the delivery process of the multi-delivery-center transport vehicle, effectively reduce the transport distance of the vehicle, improve the delivery efficiency, reduce the delivery cost and improve the customer satisfaction.

Description

Optimized scheduling method for transport vehicles of multiple distribution centers
Technical Field
The invention relates to an optimized dispatching method for transport vehicles of multiple distribution centers, and belongs to the technical field of intelligent optimized dispatching of logistics transportation.
Background
With the development of the internet and the rising of electronic commerce, the logistics transportation industry develops rapidly, and the scale of the distribution industry mainly taking the collection and distribution business of express delivery, takeout and the like is expanded unprecedentedly. Statistics data show that the number of express delivery practitioners in China exceeds ten millions, and huge energy waste and cost consumption are caused by huge industrial scale, so that how to arrange the customer service sequence in the delivery process to realize reasonable dispatching of transport vehicles is very important to reduce cost, improve efficiency and improve customer satisfaction.
Modern logistics enterprises have a plurality of distribution centers, and the core problem of the distribution business is the scheduling problem of the transportation vehicles of the distribution centers. The advantages and disadvantages of the scheduling scheme directly affect distribution efficiency, logistics cost, customer service level and even the overall operational strategy of the enterprise. However, the existing distribution scheme is mostly formulated manually according to experience, has poor effect and high randomness, and cannot be popularized and applied on a large scale. Therefore, a reasonable scheduling scheme is formulated through an effective optimization algorithm, universality is strong, sequential service sequences of different clients are comprehensively arranged, and the transportation distance of the delivery vehicles can be effectively reduced, so that the delivery time is saved, the delivery efficiency is improved, and the reduction of enterprise cost and the improvement of client satisfaction are realized.
The problem of dispatching the transport vehicles of the multiple distribution centers belongs to the problem of difficult non-deterministic polynomials, and has higher theoretical research value. The problem cannot be accurately solved in polynomial time, the solving difficulty increases in a factorial mode along with the increase of the problem scale, and the optimization quality of the solution cannot be guaranteed in effective time by the traditional heuristic construction method and the mathematical programming method. The improved ant colony optimization scheduling algorithm with the learning mechanism is an intelligent optimization algorithm capable of effectively overcoming the defects, and can obtain an approximate optimal solution of a problem in a short time.
Disclosure of Invention
The invention aims to provide an optimal scheduling method for a multi-distribution center transport vehicle, which is used for efficiently obtaining an approximately optimal distribution scheme of the multi-distribution center transport vehicle in a distribution process in a short time, namely, approximately optimal sequencing of the order of vehicle service clients so as to reduce the total transport distance of the vehicles, thereby helping enterprises to realize the requirements of reducing cost, improving efficiency and improving client satisfaction.
The technical scheme adopted by the invention is as follows: an optimized dispatching method for a multi-delivery center transport vehicle is characterized in that firstly, a sequencing dispatching model is established for the delivery process of the multi-delivery center transport vehicle and an optimized target is established, the sequencing dispatching model is established according to the number of delivery centers, the number of delivered vehicles, the delivery process of the vehicles and the mutual position distribution relation between a client and the delivery centers, and the optimized target is to minimize the total transport distance of the vehicles; then, an improved ant colony optimization scheduling algorithm with a learning mechanism is designed to optimize a target, wherein the improved ant colony algorithm with the learning mechanism introduces a knowledge base into a traditional ant colony algorithm, updates and accumulates the knowledge base by learning the dominant information of each generation of population and excellent individuals, and uses the knowledge base for guiding the subsequent iterative optimization process of the algorithm, so that an approximately optimal individual is generated by a variable neighborhood local search and an improved pheromone updating mechanism, and the minimum total transportation distance D (pi) of the vehicle is obtained:
π＝{π1,π2,…,πm} (9)
D(π)＝D1(π1)+D2(π2)+,…,Dm(πm) (10)
π*＝arg{D(π)}→min (11)
n＝n1+n2+,Ω,+nm (13)
Wherein m represents the number of distribution centers; pi h,1 represents the customer order serviced by the 1 st vehicle in the h-th distribution center; Representing the distance from the location of the 1 st customer serviced by the 1 st vehicle in the h-th distribution center to the location of the corresponding distribution center; /(I) Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its 1 st customer; n h,1 represents the number of customers serviced by the 1 st vehicle in the h distribution center; /(I)Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its j-1 st customer; /(I)Representing the distance from the location of the jth-1 customer to the location of the jth customer served by the 1 st vehicle in the jth distribution center; /(I)Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its j-th customer; /(I)Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its n h,1 th customer; representing the distance from the location of the nth h,1 customers served by the 1 st vehicle in the h distribution center to the location of the corresponding distribution center; /(I) Representing the total distance travelled by the 1 st vehicle in the h distribution center after returning to the corresponding distribution center; k h represents the number of vehicles dispatched by the h distribution center; pi h,i represents the customer order serviced by the ith vehicle in the h distribution center; /(I)Representing the distance from the location of the 1 st customer serviced by the i-th vehicle in the h-th distribution center to the location of the corresponding distribution center; /(I)Representing the distance travelled by the ith vehicle in the h distribution center after servicing its 1 st customer; n h,i represents the number of customers serviced by the ith vehicle in the h distribution center; /(I)Representing the distance travelled by the ith vehicle in the h distribution center after servicing its jth-1 customer; /(I)Representing a distance from a location of a jth-1 customer serviced by an ith vehicle in an ith distribution center to the location of the jth customer; /(I)Representing the distance travelled by the ith vehicle in the h distribution center after servicing its jth customer; Representing the distance from the location of the nth h,i th customer serviced by the ith vehicle in the nth distribution center to the location of the corresponding distribution center; /(I) Representing the distance travelled by the ith vehicle in the h distribution center after servicing its nth h,i customers; Representing the total distance travelled by the ith vehicle in the h distribution center after returning to the corresponding distribution center; pi h represents the customer order of all vehicle services dispatched by the h-th distribution center; d h(πh) represents the sum of the total distances traveled by all vehicles dispatched by the h-th distribution center; pi represents the customer order of all vehicle services dispatched by all distribution centers; d (pi) represents the sum of the total distances traveled by all vehicles dispatched by all distribution centers, i.e., the objective function; pi * represents the customer ordering in minimizing the objective function; n h represents the total number of customers for all vehicle services dispatched by the h-th distribution center; n represents the total number of customers for all vehicle services dispatched by all distribution centers;
the improved ant colony optimization scheduling algorithm with the learning mechanism specifically comprises the following steps:
step1, initializing a pheromone matrix and simultaneously initializing a knowledge base: initializing a pheromone matrix, generating a feasible solution through a greedy algorithm, calculating an objective function value Fitness of the feasible solution, and then setting all elements in the pheromone matrix to be 1/Fitness; initializing a knowledge base, firstly obtaining N p groups of algorithm parameters through an orthogonal test design method and setting contribution values corresponding to each group of parameters as Let/>Secondly, N l local operations for local search are designed, and the contribution value corresponding to each operation is set as/>Let/>
Step2, determining algorithm parameters: selecting a group of parameters for the iterative optimization process of the algorithm by using a roulette method according to the contribution value of the N p groups of algorithm parameters in the knowledge base;
Step3, generating a new population with a population size pop and evaluating the new population: the value of pop is 30-60, the generation process of individuals in the new population is carried out according to the formula (14), and after the new population is generated, if the average quality of the new population is improved, the contribution value of the k-th group algorithm parameter selected for the current iterative optimization process in the knowledge base is improved by 1, namely The knowledge base is updated and accumulated for one time according to the sequence;
Where p ij (ite) represents the probability that the current ant goes from point i to point j; τ ij (ite) represents the intensity of the pheromone on the path currently consisting of point i and point j; alpha represents the weight of the pheromone, and the value is 0.5-2.0; beta represents heuristic factor weight, and the value is 0.5-2.0; η ij (ite) represents the heuristic of the path currently consisting of points i and j; r is the set of points that are not in the current ant tabu list;
step4, performing variable neighborhood local search on the newly generated population: when the individuals in the population meet In the time-course of which the first and second contact surfaces,For the average mass of the population, D l is the mass of the individual, a variable neighborhood local search is performed on the individual, and in the variable neighborhood local search process, if the mass of the current individual is improved, the contribution value of the first local operation for improving the current individual in the knowledge base is improved by 1, namely/>The knowledge base is updated and accumulated for one time according to the sequence;
Step5, updating the pheromone matrix: the individual performing the variable neighborhood local search in Step4 is used to update the pheromone matrix, and the improved pheromone matrix updating process is carried out according to formulas (15) and (16);
τij(ite+1)＝(1-ρ)τij(ite)+Δτij(ite,ite+1) (16)
In the method, in the process of the invention, The meaning of D l is the same as in Step4, w l represents the individual weight; q represents the intensity of the pheromone, and the value is 500-2000; pi l represents the individual path; /(I)A pheromone representing the release of an individual on a pathway; Δτ ij (ite, ite+1) represents the sum of pheromones released on the pathway by all individuals; ρ represents a pheromone volatilization factor, and the value is 0.05-0.25; τ ij (ite) represents the pre-update pheromone matrix; τ ij (ite+1) represents the updated pheromone matrix;
Step6, judging termination conditions: if the algorithm meets the termination condition, namely the algorithm iteration number reaches the maximum iteration limit number 300, outputting an optimal solution; otherwise, jumping to Step2 for repeated iteration until the termination condition is met;
The variable neighborhood local search process for the newly generated population is specifically as follows: and executing an Insert operation, a Swap operation and a Change operation maxN =500 times in sequence on the individuals with quality superior to the average value of the population in the new population, and replacing the original individuals with the improved individuals if the quality of the executed individuals is improved in the execution process.
The beneficial effects of the invention are as follows:
1. Determining a dispatching model and an optimization target of a multi-dispatching center transport vehicle dispatching process, so that the dispatching process is clear, and the dispatching scheme is feasible, effective and easy to popularize;
2. The improved ant colony algorithm with a learning mechanism can improve the algorithm efficiency in an iterative optimization stage, namely, a knowledge base is introduced to learn and accumulate dominant population and individual information in each generation of the algorithm, so that the problem that the algorithm is sensitive to parameter selection and redundant local operation accumulation is solved, and the optimizing efficiency of the algorithm can be improved;
3. The improved pheromone updating mechanism can effectively screen better individuals in the population, so that algorithm stagnation caused by accumulation of a large amount of invalid pheromones is avoided, and the algorithm convergence speed is increased;
4. the variable neighborhood local search can promote the capability of the algorithm to jump out of the local optimum so as to tend to the global optimum, and can promote the quality of the algorithm to obtain the approximate optimum solution;
5. The invention can rapidly acquire the approximate optimal scheduling scheme of the delivery process of the transport vehicles of the multiple delivery centers, thereby effectively reducing the transport distance of the vehicles, saving the delivery time, improving the delivery efficiency, reducing the delivery cost and improving the customer satisfaction.
Drawings
FIG. 1 is a general flow chart of the present invention;
FIG. 2 is a flow chart of the optimal scheduling method of the present invention;
FIG. 3 is a schematic diagram of a scheduling scheme according to the present invention;
FIG. 4 is a diagram of a variant neighborhood search according to the present invention;
FIG. 5 is a schematic illustration of the Insert operation of the present invention;
FIG. 6 is a schematic illustration of the Swap operation of the present invention;
Fig. 7 is a schematic diagram of the Change operation of the present invention.
Detailed Description
Example 1: 1-7, an optimized dispatching method for multiple delivery center transport vehicles is disclosed, wherein a sequencing dispatching model is firstly established for the delivery process of the multiple delivery center transport vehicles and an optimized target is established, the sequencing dispatching model is established according to the number of delivery centers, the number of delivered vehicles, the delivery process of the vehicles and the mutual position distribution relation between customers and the delivery centers, and the optimized target is to minimize the total transport distance of the vehicles; then, an improved ant colony optimization scheduling algorithm with a learning mechanism is designed to optimize a target, wherein the improved ant colony algorithm with the learning mechanism introduces a knowledge base into a traditional ant colony algorithm, updates and accumulates the knowledge base by learning the dominant information of each generation of population and excellent individuals, and uses the knowledge base for guiding the subsequent iterative optimization process of the algorithm, so that an approximately optimal individual is generated by a variable neighborhood local search and an improved pheromone updating mechanism, and the minimum total transportation distance D (pi) of the vehicle is obtained:
π＝{π1,π2,…,πm} (9)
D(π)＝D1(π1)+D2(π2)+,…,Dm(πm) (10)
π*＝arg{D(π)}→min (11)
n＝n1+n2+,…,+nm (13)
Wherein m represents the number of distribution centers; pi h,1 represents the customer order serviced by the 1 st vehicle in the h-th distribution center; Representing the distance from the location of the 1 st customer serviced by the 1 st vehicle in the h-th distribution center to the location of the corresponding distribution center; /(I) Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its 1 st customer; n h,1 represents the number of customers serviced by the 1 st vehicle in the h distribution center; /(I)Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its j-1 st customer; /(I)Representing the distance from the location of the jth-1 customer to the location of the jth customer served by the 1 st vehicle in the jth distribution center; /(I)Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its j-th customer; /(I)Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its n h,1 th customer; representing the distance from the location of the nth h,1 customers served by the 1 st vehicle in the h distribution center to the location of the corresponding distribution center; /(I) Representing the total distance travelled by the 1 st vehicle in the h distribution center after returning to the corresponding distribution center; k h represents the number of vehicles dispatched by the h distribution center; pi h,i represents the customer order serviced by the ith vehicle in the h distribution center; /(I)Representing the distance from the location of the 1 st customer serviced by the i-th vehicle in the h-th distribution center to the location of the corresponding distribution center; /(I)Representing the distance travelled by the ith vehicle in the h distribution center after servicing its 1 st customer; n h,i represents the number of customers serviced by the ith vehicle in the h distribution center; /(I)Representing the distance travelled by the ith vehicle in the h distribution center after servicing its jth-1 customer; /(I)Representing a distance from a location of a jth-1 customer serviced by an ith vehicle in an ith distribution center to the location of the jth customer; /(I)Representing the distance travelled by the ith vehicle in the h distribution center after servicing its jth customer; Representing the distance from the location of the nth h,i th customer serviced by the ith vehicle in the nth distribution center to the location of the corresponding distribution center; /(I) Representing the distance travelled by the ith vehicle in the h distribution center after servicing its nth h,i customers; /(I)Representing the total distance travelled by the ith vehicle in the h distribution center after returning to the corresponding distribution center; pi h represents the customer order of all vehicle services dispatched by the h-th distribution center; d h(πh) represents the sum of the total distances traveled by all vehicles dispatched by the h-th distribution center; pi represents the customer order of all vehicle services dispatched by all distribution centers; d (pi) represents the sum of the total distances traveled by all vehicles dispatched by all distribution centers, i.e., the objective function; pi * represents the customer ordering in minimizing the objective function; n h represents the total number of customers for all vehicle services dispatched by the h-th distribution center; n represents the total number of customers for all vehicle services dispatched by all distribution centers;
the improved ant colony optimization scheduling algorithm with the learning mechanism specifically comprises the following steps:
step1, initializing a pheromone matrix and simultaneously initializing a knowledge base: initializing a pheromone matrix, generating a feasible solution through a greedy algorithm, calculating an objective function value Fitness of the feasible solution, and then setting all elements in the pheromone matrix to be 1/Fitness; initializing a knowledge base, firstly obtaining N p groups of algorithm parameters through an orthogonal test design method and setting contribution values corresponding to each group of parameters as Let/>Secondly, N l local operations for local search are designed, and the contribution value corresponding to each operation is set as/>Let/>
Step2, determining algorithm parameters: selecting a group of parameters for the iterative optimization process of the algorithm by using a roulette method according to the contribution value of the N p groups of algorithm parameters in the knowledge base;
Step3, generating a new population with a population size pop and evaluating the new population: the generation process of individuals in the new population is carried out according to the formula (14), after the new population is generated, if the average quality of the new population is improved, the contribution value of the k-th group algorithm parameters selected for the iterative optimization process in the knowledge base is improved by 1, namely The knowledge base is updated and accumulated for one time according to the sequence;
Where p ij (ite) represents the probability that the current ant goes from point i to point j; τ ij (ite) represents the intensity of the pheromone on the path currently consisting of point i and point j; alpha represents the pheromone weight; beta represents heuristic weights; η ij (ite) represents the heuristic of the path currently consisting of points i and j; r is the set of points that are not in the current ant tabu list;
step4, performing variable neighborhood local search on the newly generated population: when the individuals in the population meet In the time-course of which the first and second contact surfaces,For the average mass of the population, D l is the mass of the individual, a variable neighborhood local search is performed on the individual, and in the variable neighborhood local search process, if the mass of the current individual is improved, the contribution value of the first local operation for improving the current individual in the knowledge base is improved by 1, namely/>The knowledge base is updated and accumulated for one time according to the sequence;
Step5, updating the pheromone matrix: the individual performing the variable neighborhood local search in Step4 is used to update the pheromone matrix, and the improved pheromone matrix updating process is carried out according to formulas (15) and (16);
τij(ite+1)＝(1-ρ)τij(ite)+Δτij(ite,ite+1) (16)
In the method, in the process of the invention, The meaning of D l is the same as in Step4, w l represents the individual weight; q represents the intensity of the pheromone; pi l represents the individual path; /(I)A pheromone representing the release of an individual on a pathway; Δτ ij (ite, ite+1) represents the sum of pheromones released on the pathway by all individuals; ρ represents a pheromone volatilization factor; τ ij (ite) represents the pre-update pheromone matrix; τ ij (ite+1) represents the updated pheromone matrix;
Step6, judging termination conditions: if the algorithm meets the termination condition, namely the algorithm iteration number reaches the maximum iteration limit number 300, outputting an optimal solution; otherwise, jumping to Step2 for repeated iteration until the termination condition is met;
The variable neighborhood local search process for the newly generated population is specifically as follows: and executing an Insert operation, a Swap operation and a Change operation maxN =500 times in sequence on the individuals with quality superior to the average value of the population in the new population, and replacing the original individuals with the improved individuals if the quality of the executed individuals is improved in the execution process.
In this embodiment, N p =8 sets of algorithm parameters are obtained together, and specific values are as follows:
Example 2: 1-7, an optimized dispatching method for multiple delivery center transport vehicles is disclosed, wherein a sequencing dispatching model is firstly established for the delivery process of the multiple delivery center transport vehicles and an optimized target is established, the sequencing dispatching model is established according to the number of delivery centers, the number of delivered vehicles, the delivery process of the vehicles and the mutual position distribution relation between customers and the delivery centers, and the optimized target is to minimize the total transport distance of the vehicles; then, an improved ant colony optimization scheduling algorithm with a learning mechanism is designed to optimize a target, wherein the improved ant colony algorithm with the learning mechanism introduces a knowledge base into a traditional ant colony algorithm, updates and accumulates the knowledge base by learning the dominant information of each generation of population and excellent individuals, and uses the knowledge base for guiding the subsequent iterative optimization process of the algorithm, so that an approximately optimal individual is generated by a variable neighborhood local search and an improved pheromone updating mechanism, and the minimum total transportation distance D (pi) of the vehicle is obtained:
π＝{π1,π2,…,πm} (9)
D(π)＝D1(π1)+D2(π2)+,…,Dm(πm) (10)
π*＝arg{D(π)}→min (11)
n＝n1+n2+,Ω,+nm (13)
Wherein m represents the number of distribution centers; pi h,1 represents the customer order serviced by the 1 st vehicle in the h-th distribution center; Representing the distance from the location of the 1 st customer serviced by the 1 st vehicle in the h-th distribution center to the location of the corresponding distribution center; /(I) Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its 1 st customer; n h,1 represents the number of customers serviced by the 1 st vehicle in the h distribution center; /(I)Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its j-1 st customer; /(I)Representing the distance from the location of the jth-1 customer to the location of the jth customer served by the 1 st vehicle in the jth distribution center; /(I)Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its j-th customer; /(I)Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its n h,1 th customer; representing the distance from the location of the nth h,1 customers served by the 1 st vehicle in the h distribution center to the location of the corresponding distribution center; /(I) Representing the total distance travelled by the 1 st vehicle in the h distribution center after returning to the corresponding distribution center; k h represents the number of vehicles dispatched by the h distribution center; pi h,i represents the customer order serviced by the ith vehicle in the h distribution center; /(I)Representing the distance from the location of the 1 st customer serviced by the i-th vehicle in the h-th distribution center to the location of the corresponding distribution center; /(I)Representing the distance travelled by the ith vehicle in the h distribution center after servicing its 1 st customer; n h,i represents the number of customers serviced by the ith vehicle in the h distribution center; /(I)Representing the distance travelled by the ith vehicle in the h distribution center after servicing its jth-1 customer; /(I)Representing a distance from a location of a jth-1 customer serviced by an ith vehicle in an ith distribution center to the location of the jth customer; /(I)Representing the distance travelled by the ith vehicle in the h distribution center after servicing its jth customer; Representing the distance from the location of the nth h,i th customer serviced by the ith vehicle in the nth distribution center to the location of the corresponding distribution center; /(I) Representing the distance travelled by the ith vehicle in the h distribution center after servicing its nth h,i customers; Representing the total distance travelled by the ith vehicle in the h distribution center after returning to the corresponding distribution center; pi h represents the customer order of all vehicle services dispatched by the h-th distribution center; d h(πh) represents the sum of the total distances traveled by all vehicles dispatched by the h-th distribution center; pi represents the customer order of all vehicle services dispatched by all distribution centers; d (pi) represents the sum of the total distances traveled by all vehicles dispatched by all distribution centers, i.e., the objective function; pi * represents the customer ordering in minimizing the objective function; n h represents the total number of customers for all vehicle services dispatched by the h-th distribution center; n represents the total number of customers for all vehicle services dispatched by all distribution centers;
the improved ant colony optimization scheduling algorithm with the learning mechanism specifically comprises the following steps:
step1, initializing a pheromone matrix and simultaneously initializing a knowledge base: initializing a pheromone matrix, generating a feasible solution through a greedy algorithm, calculating an objective function value Fitness of the feasible solution, and then setting all elements in the pheromone matrix to be 1/Fitness; initializing a knowledge base, firstly obtaining N p groups of algorithm parameters through an orthogonal test design method and setting contribution values corresponding to each group of parameters as Let/>Secondly, N l local operations for local search are designed, and the contribution value corresponding to each operation is set as/>Let/>
Step2, determining algorithm parameters: selecting a group of parameters for the iterative optimization process of the algorithm by using a roulette method according to the contribution value of the N p groups of algorithm parameters in the knowledge base;
Step3, generating a new population with a population size pop and evaluating the new population: the generation process of individuals in the new population is carried out according to the formula (14), after the new population is generated, if the average quality of the new population is improved, the contribution value of the k-th group algorithm parameters selected for the iterative optimization process in the knowledge base is improved by 1, namely The knowledge base is updated and accumulated for one time according to the sequence;
Where p ij (ite) represents the probability that the current ant goes from point i to point j; τ ij (ite) represents the intensity of the pheromone on the path currently consisting of point i and point j; alpha represents the pheromone weight; beta represents heuristic weights; η ij (ite) represents the heuristic of the path currently consisting of points i and j; r is the set of points that are not in the current ant tabu list;
step4, performing variable neighborhood local search on the newly generated population: when the individuals in the population meet In the time-course of which the first and second contact surfaces,For the average mass of the population, D l is the mass of the individual, a variable neighborhood local search is performed on the individual, and in the variable neighborhood local search process, if the mass of the current individual is improved, the contribution value of the first local operation for improving the current individual in the knowledge base is improved by 1, namely/>The knowledge base is updated and accumulated for one time according to the sequence;
Step5, updating the pheromone matrix: the individual performing the variable neighborhood local search in Step4 is used to update the pheromone matrix, and the improved pheromone matrix updating process is carried out according to formulas (15) and (16);
τij(ite+1)＝(1-ρ)τij(ite)+Δτij(ite,ite+1) (16)
In the method, in the process of the invention, The meaning of D l is the same as in Step4, w l represents the individual weight; q represents the intensity of the pheromone; pi l represents the individual path; /(I)A pheromone representing the release of an individual on a pathway; Δτ ij (ite, ite+1) represents the sum of pheromones released on the pathway by all individuals; ρ represents a pheromone volatilization factor; τ ij (ite) represents the pre-update pheromone matrix; τ ij (ite+1) represents the updated pheromone matrix;
Step6, judging termination conditions: if the algorithm meets the termination condition, namely the algorithm iteration number reaches the maximum iteration limit number 300, outputting an optimal solution; otherwise, jumping to Step2 for repeated iteration until the termination condition is met;
The variable neighborhood local search process for the newly generated population is specifically as follows: and executing an Insert operation, a Swap operation and a Change operation maxN =500 times in sequence on the individuals with quality superior to the average value of the population in the new population, and replacing the original individuals with the improved individuals if the quality of the executed individuals is improved in the execution process.
In this embodiment, N p =8 sets of algorithm parameters are obtained together, and specific values are as follows:
While the present invention has been described in detail with reference to the drawings, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.

Claims (2)

1. An optimized dispatching method for a multi-distribution center transport vehicle is characterized in that: firstly, establishing a sequencing scheduling model and an optimization target aiming at the delivery process of the multi-delivery center transport vehicle; then, an improved ant colony optimization scheduling algorithm with a learning mechanism is designed to optimize the target;
The sorting and scheduling model of the multi-distribution center transportation vehicle distribution process is established according to the distribution center number, the distribution vehicle number, the vehicle distribution process and the mutual position distribution relation between the client and the distribution center, and the optimization target is to minimize the total transportation distance D (pi):
Dh,πh,1,1＝dh,πh,1,1,h＝1,2,…,m (1)
π＝{π1,π2,…,πm} (9)
D(π)＝D1(π1)+D2(π2)+,…,Dm(πm) (10)
π*＝arg{D(π)}→min (11)
n＝n1+n2+,…,+nm (13)
Wherein m represents the number of distribution centers; pi h,1 represents the customer order serviced by the 1 st vehicle in the h-th distribution center; Representing the distance from the location of the 1 st customer serviced by the 1 st vehicle in the h-th distribution center to the location of the corresponding distribution center; /(I) Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its 1 st customer; n h,1 represents the number of customers serviced by the 1 st vehicle in the h distribution center; /(I)Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its j-1 st customer; /(I)Representing the distance from the location of the jth-1 customer to the location of the jth customer served by the 1 st vehicle in the jth distribution center; /(I)Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its j-th customer; Representing the distance travelled by the 1 st vehicle in the h distribution center after servicing its n h,1 th customer; /(I) Representing the distance from the location of the nth h,1 customers served by the 1 st vehicle in the h distribution center to the location of the corresponding distribution center; /(I)Representing the total distance travelled by the 1 st vehicle in the h distribution center after returning to the corresponding distribution center; k h represents the number of vehicles dispatched by the h distribution center; pi h,i represents the customer order serviced by the ith vehicle in the h distribution center; /(I)Representing the distance from the location of the 1 st customer serviced by the i-th vehicle in the h-th distribution center to the location of the corresponding distribution center; /(I)Representing the distance travelled by the ith vehicle in the h distribution center after servicing its 1 st customer; n h,i represents the number of customers serviced by the ith vehicle in the h distribution center; /(I)Representing the distance travelled by the ith vehicle in the h distribution center after servicing its jth-1 customer; Representing a distance from a location of a jth-1 customer serviced by an ith vehicle in an ith distribution center to the location of the jth customer; /(I) Representing the distance travelled by the ith vehicle in the h distribution center after servicing its jth customer; /(I)Representing the distance from the location of the nth h,i th customer serviced by the ith vehicle in the nth distribution center to the location of the corresponding distribution center; /(I)Representing the distance travelled by the ith vehicle in the h distribution center after servicing its nth h,i customers; /(I)Representing the total distance travelled by the ith vehicle in the h distribution center after returning to the corresponding distribution center; pi h represents the customer order of all vehicle services dispatched by the h-th distribution center; d h(πh) represents the sum of the total distances traveled by all vehicles dispatched by the h-th distribution center; pi represents the customer order of all vehicle services dispatched by all distribution centers; d (pi) represents the sum of the total distances traveled by all vehicles dispatched by all distribution centers, i.e., the objective function; pi * represents the customer ordering in minimizing the objective function; n h represents the total number of customers for all vehicle services dispatched by the h-th distribution center; n represents the total number of customers for all vehicle services dispatched by all distribution centers;
the improved ant colony optimization scheduling algorithm with the learning mechanism specifically comprises the following steps:
step1, initializing a pheromone matrix and simultaneously initializing a knowledge base: initializing a pheromone matrix, generating a feasible solution through a greedy algorithm, calculating an objective function value Fitness of the feasible solution, and then setting all elements in the pheromone matrix to be 1/Fitness; initializing a knowledge base, firstly obtaining N p groups of algorithm parameters through an orthogonal test design method and setting contribution values corresponding to each group of parameters as Let/>Secondly, N l local operations for local search are designed, and the contribution value corresponding to each operation is set as/>Let/>
Step2, determining algorithm parameters: selecting a group of parameters for the iterative optimization process of the algorithm by using a roulette method according to the contribution value of the N p groups of algorithm parameters in the knowledge base;
Step3, generating a new population with a population size pop and evaluating the new population: the value of pop is 30-60, the generation process of individuals in the new population is carried out according to the formula (14), and after the new population is generated, if the average quality of the new population is improved, the contribution value of the k-th group algorithm parameter selected for the current iterative optimization process in the knowledge base is improved by 1, namely The knowledge base is updated and accumulated for one time according to the sequence;
Where p ij (ite) represents the probability that the current ant goes from point i to point j; τ ij (ite) represents the intensity of the pheromone on the path currently consisting of point i and point j; alpha represents the weight of the pheromone, and the value is 0.5-2.0; beta represents heuristic factor weight, and the value is 0.5-2.0; η ij (ite) represents the heuristic of the path currently consisting of points i and j; r is the set of points that are not in the current ant tabu list;
step4, performing variable neighborhood local search on the newly generated population: when the individuals in the population meet Time,/>For the average mass of the population, D l is the mass of the individual, a variable neighborhood local search is performed on the individual, and in the variable neighborhood local search process, if the mass of the current individual is improved, the contribution value of the first local operation for improving the current individual in the knowledge base is improved by 1, namely/>The knowledge base is updated and accumulated for one time according to the sequence;
Step5, updating the pheromone matrix: the individual performing the variable neighborhood local search in Step4 is used to update the pheromone matrix, and the improved pheromone matrix updating process is carried out according to formulas (15) and (16);
τij(ite+1)＝(1-ρ)τij(ite)+Δτij(ite,ite+1) (16)
In the method, in the process of the invention, The meaning of D l is the same as in Step4, w l represents the individual weight; q represents the intensity of the pheromone, and the value is 500-2000; pi l represents the individual path; /(I)A pheromone representing the release of an individual on a pathway; Δτ ij (ite, ite+1) represents the sum of pheromones released on the pathway by all individuals; ρ represents a pheromone volatilization factor, and the value is 0.05-0.25; τ ij (ite) represents the pre-update pheromone matrix; τ ij (ite+1) represents the updated pheromone matrix;
Step6, judging termination conditions: if the algorithm meets the termination condition, namely the algorithm iteration number reaches the maximum iteration limit number 300, outputting an optimal solution; otherwise, jumping to Step2 to iterate repeatedly until the termination condition is met.
2. The optimized dispatch method for a multi-dispatch center transport vehicle of claim 1, wherein: the variable neighborhood local search process for the newly generated population is specifically as follows: and executing an Insert operation, a Swap operation and a Change operation maxN =500 times in sequence on the individuals with quality superior to the average value of the population in the new population, and replacing the original individuals with the improved individuals if the quality of the executed individuals is improved in the execution process.
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