CN108921326B - Intelligent cultural gene logistics distribution method based on similarity learning - Google Patents
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Abstract
The invention relates to the field of intelligent logistics distribution, in particular to an intelligent cultural gene logistics distribution method based on similarity learning. The method comprises the following steps: uploading data such as cargo data, client coordinates, road states and the like to a database; initializing a population; checking whether a stop condition is reached; roulette in the population to select two individuals to cross and generate offspring; calculating the similarity of the current individuals; if the similarity between the individual and the existing individual in the population is lower than a threshold value, carrying out heuristic local search on the individual; sorting the individuals in the population, selecting the optimal individual and the like; the invention adopts the concept of similarity, so that the algorithm controls the diversity of the population to a certain extent in the iterative process, avoids the situations of premature convergence and local optimum of the population, greatly improves the search range of the population, enables the population to put as many computing resources as possible on a solution with more potential, and improves the optimization capability of the population.
Description
Technical Field
The invention relates to the field of intelligent logistics distribution, in particular to an intelligent cultural gene logistics distribution method based on similarity learning.
Background
With the development of the internet, electronic commerce increasingly deepens into the lives of people, and the development of the logistics industry is greatly promoted by the appearance of the electronic commerce. The logistics industry comprises a plurality of links such as packaging, loading and unloading, transportation, distribution and the like. The network structure in which logistics are distributed directly determines the distribution cost and distribution efficiency. When the logistics industry is just emerging, distributors generally determine distribution routes according to daily accumulated experience, and the problems of low distribution efficiency, unreasonable personnel distribution, overlarge distribution consumption and the like are often caused due to the fact that reasonable optimization is not carried out.
The logistics distribution problem is actually an NP problem, and the following two main methods are currently used for solving the problem: precision algorithms and approximation algorithms. The precise algorithm comprises a branch and limit method, dynamic programming and the like, along with the expansion of the problem scale, the time for solving the algorithm is increased sharply, the approximate algorithm comprises a genetic algorithm, tabu search and the like, although the algorithms can solve a more reasonable solution within a certain time, the algorithms have a series of problems of poor robustness, easy falling into local optimum and the like.
The closest method of the invention is a method for dispatching agricultural chain operation delivery vehicles based on an improved genetic algorithm, which is CN107122929A Wuxi Zhongfu agricultural and Material Co-Ltd, wherein the method comprises the following steps: establishing a dispatching model of the agricultural material continuous distribution vehicle; improving a genetic algorithm; inputting distribution point information and vehicle information; inputting a distribution failure punishment coefficient; and outputting a distribution scheme and the like. According to the method, the penalty information of delivery failure is input, the genetic algorithm is used for calculation, time constraint is met to a certain extent, distance consumption in the delivery process is saved, but the algorithm has the defects of low calculation speed, easiness in falling into local optimization and the like, and actual factors such as fleet scale, road congestion and the like are not considered.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an intelligent cultural gene logistics distribution method based on similarity learning.
In order to solve the technical problems, the invention adopts the technical scheme that: an intelligent cultural gene logistics distribution method based on similarity learning comprises the following steps:
s1: uploading data such as cargo data, customer coordinates, road states and the like to a database, and determining a dispatching model of the delivery vehicle limited by the limited time;
s2: initializing a population;
s3: checking whether a stopping condition is reached, wherein the stopping condition is that the algorithm is stopped when the optimal solution is unchanged after a certain algebra, and if the stopping condition is reached, the chromosome is decoded to obtain an optimal distribution scheme; if the stop condition is not reached, step S4 is executed;
s4: randomly selecting two individuals from a population to cross to generate offspring;
s5: calculating the similarity of the current individuals;
s6: if the similarity between the individual and the existing individual in the population is lower than a threshold value, carrying out heuristic local search on the individual;
s7: sequencing the individuals in the population, and selecting the optimal individual;
s8: step S3 is executed until a stop condition is reached.
Preferably, the scheduling model includes a time constraint for delivery of the vehicle, a load constraint, customer coordinates, a vehicle scale including a vehicle rental fee, a scheduled driver fee, and a toll road fee, a total travel cost of the vehicle, and an objective function.
Preferably, the objective function is
min z ═ α nv(s) + β tc(s) + tp(s), wherein optimization objectives include vehicle scale, shortest total distance traveled by the vehicle, and shortest time cost; alpha is the lowest cost per vehicle, NV (S) is the total number of vehicles used, beta is the fuel consumption coefficient, TC (S) is the total cost for completing all distribution tasks, and TP (S) is the total time cost.
Preferably, the total cost of completing all delivery tasks is
Preferably, said total time cost
Preferably, in the step S5, the similarity of individuals in the population is calculated
Wherein, the SimilariRepresenting the similarity between the vehicle service sequence with the current individual number i in the population and the service sequences of other individuals;
Preferably, in step S2, a part of individuals in the population is initialized randomly, and a part of individuals in the population is initialized greedy, so as to obtain the initial distribution scheme.
Preferably, in step S4, two individuals to be crossed are selected by roulette selection, and the chromosomes are crossed to obtain offspring by using a sequence-based crossing scheme.
Preferably, in step S6, the heuristic local search specifically includes the following steps:
a: single insertion, double insertion and exchange operation are adopted;
b: randomly selecting n sub-routes for combination, according to a test, when n is 2, obtaining a better result, then segmenting according to 5 heuristic search rules of path search, constructing a route from an empty route, gradually inserting client points according to the following 5 rules on the premise of meeting the time looseness and the road congestion degree lower than a traffic threshold value to respectively obtain 5 solutions, and if the time window range is about to be exceeded, selecting the client point with the most urgent current time;
c: and repeating the step A to further obtain a better solution.
Preferably, the 5 heuristic search rules of the path search are as follows:
selecting unserviced customer points furthest from the warehouse;
selecting the nearest unserviced customer point to the warehouse;
selecting the unserviced customer points with the largest ratio of the demand to the service cost;
selecting the un-served customer points with the minimum ratio of the demand to the service cost;
if the loaded capacity of the vehicle is less than half of the capacity, using a rule I, otherwise, using a rule II;
and if no client point meeting the load capacity and the time window exists, returning to the warehouse, ending the sub-route and creating a new sub-route.
Compared with the prior art, the invention has the beneficial effects that:
1. the vehicle rental expense, the personnel consumption and the like are reduced by reducing the scale of the fleet, so that the labor and material cost of an enterprise is greatly reduced.
2. Considering the distribution time requirement of some goods, the distribution time requirement of fresh food such as fruits, meat and the like is high, and if the distribution time is too long, the goods can be rotten and deteriorated. The dispatching time is controlled by setting time window constraint, so that the satisfaction degree of customers can be improved, and the distribution scheme can be dynamically adjusted according to the emergency degree of different commodities.
3. The congestion coefficient is set for the road in consideration of road congestion caused by sudden factors such as traffic flow peaks and traffic accidents, and the possibility of running congested road sections is reduced.
4. Using a culture gene algorithm framework suitable for large-scale parallel solution;
5. introducing local search, and searching the solution with potential around the illegal solution through a merging-dividing operator;
6. by adopting a heuristic path search rule, the field search is carried out on solutions with potential but violating the vehicle-mounted capacity, so that the search capability of the algorithm is greatly improved;
7. in the algorithm, a similarity concept is adopted, the operator enables the algorithm to control the diversity of the population to a certain extent in the iteration process, the situations that the population converges prematurely and falls into local optimum are avoided, the search range of the population is greatly improved, the population puts as many computing resources as possible on a solution with more potential, and the optimization capability of the population is improved.
Drawings
FIG. 1 is a flow chart of an intelligent cultural genetic algorithm based on similarity learning;
FIG. 2 is a schematic diagram of the distribution before decoding of chromosomes;
FIG. 3 is a flow diagram of a heuristic search;
FIG. 4 is a schematic representation of the chromosome after decoding.
Detailed Description
The present invention will be further described with reference to the following embodiments. Wherein the showings are for the purpose of illustration only and are shown by way of illustration only and not in actual form, and are not to be construed as limiting the present patent; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", etc. based on the orientation or positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but it is not intended to indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the present patent, and the specific meaning of the terms may be understood by those skilled in the art according to specific circumstances.
Examples
Fig. 1 is a flowchart of an intelligent cultural gene logistics distribution method based on similarity learning, and the intelligent cultural gene logistics distribution method based on similarity learning comprises the following steps:
s1: uploading data such as cargo data, customer coordinates, road states and the like to a database, and determining a dispatching model of the delivery vehicle limited by the limited time;
s2: initializing a population;
s3: checking whether a stopping condition is reached, and if the stopping condition is reached, decoding the chromosome to obtain an optimal distribution scheme, as shown in fig. 4; if the stop condition is not reached, step S4 is executed;
s4: randomly selecting two individuals from a population to cross to generate offspring;
s5: calculating the similarity of the current individuals;
s6: if the similarity between the individual and the existing individual in the population is lower than a threshold value, carrying out heuristic local search on the individual;
s7: sequencing the individuals in the population, and selecting the optimal individual;
s8: step S3 is executed until a stop condition is reached.
Fig. 2 is a schematic diagram showing distribution before decoding chromosomes.
The dispatching model comprises time constraint, load constraint, customer coordinates, vehicle scale, total running cost of the vehicle and an objective function of vehicle distribution, wherein the vehicle scale comprises vehicle rental cost, driver arrangement cost and road toll payment.
In addition, the objective function is
min z ═ α nv(s) + β tc(s) + tp(s), wherein optimization objectives include vehicle scale, shortest total distance traveled by the vehicle, and shortest time cost; alpha is the lowest cost per vehicle, NV (S) is the total number of vehicles used, beta is the fuel consumption coefficient, TC (S) is the total cost for completing all distribution tasks, and TP (S) is the total time cost.
Wherein the total cost of completing all distribution tasks
In addition, the total time cost
Wherein, in the step S5, the similarity of the individuals in the population is calculated
Wherein, the SimilariRepresenting the similarity between the vehicle service sequence with the current individual number i in the population and the service sequences of other individuals;
In step S2, some individuals in the population are initialized randomly, and some individuals in the population are initialized greedy, so that an initial distribution plan is obtained.
In step S4, two individuals to be crossed are selected by roulette selection, and the chromosomes are crossed to obtain offspring by using a sequence-based crossing scheme.
In addition, fig. 3 shows a flowchart of heuristic search, and in step S6, the heuristic local search specifically includes the following steps:
a: single insertion, double insertion and exchange operation are adopted;
b: randomly selecting n sub-routes for combination, according to a test, when n is 2, obtaining a better result, then segmenting according to 5 heuristic search rules of path search, constructing a route from an empty route, gradually inserting client points according to the following 5 rules on the premise of meeting the time looseness and the road congestion degree lower than a traffic threshold value to respectively obtain 5 solutions, and if the time window range is about to be exceeded, selecting the client point with the most urgent current time;
c: and repeating the step A to further obtain a better solution.
The 5 heuristic search rules for path search are as follows:
selecting unserviced customer points furthest from the warehouse;
selecting the nearest unserviced customer point to the warehouse;
selecting the unserviced customer points with the largest ratio of the demand to the service cost;
selecting the un-served customer points with the minimum ratio of the demand to the service cost;
if the loaded capacity of the vehicle is less than half of the capacity, using a rule I, otherwise, using a rule II;
and if no client point meeting the load capacity and the time window exists, returning to the warehouse, ending the sub-route and creating a new sub-route.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (8)
1. An intelligent cultural gene logistics distribution method based on similarity learning is characterized by comprising the following steps:
s1: uploading data such as cargo data, customer coordinates, road states and the like to a database, and determining a dispatching model of the delivery vehicle limited by the limited time;
s2: initializing a population;
s3: checking whether a stopping condition is reached, and if the stopping condition is reached, decoding the chromosome to obtain an optimal distribution scheme; if the stop condition is not reached, step S4 is executed;
s4: randomly selecting two individuals from a population to cross to generate offspring;
s5: calculating the similarity of the current individuals;
s6: if the similarity between the individual and the existing individual in the population is lower than a threshold value, carrying out heuristic local search on the individual;
s7: sequencing the individuals in the population, and selecting the optimal individual;
s8: step S3 is executed until a stop condition is reached;
in step S6, the heuristic local search specifically includes the following steps:
a: single insertion, double insertion and exchange operation are adopted;
b: randomly selecting n sub-routes for combination, according to a test, when n is 2, obtaining a better result, then segmenting according to 5 heuristic search rules of path search, constructing a route from an empty route, gradually inserting into client points according to the 5 heuristic search rules of the path search on the premise of meeting the time slack and the road congestion degree lower than a traffic threshold value to respectively obtain 5 solutions, and if the time slack and the road congestion degree are about to exceed the time window range, selecting the client point with the most urgent current time;
c: repeating the step A to further obtain a better solution;
the 5 heuristic search rules for the path search are as follows:
selecting unserviced customer points furthest from the warehouse;
selecting the nearest unserviced customer point to the warehouse;
selecting the unserviced customer points with the largest ratio of the demand to the service cost;
selecting the un-served customer points with the minimum ratio of the demand to the service cost;
if the loaded capacity of the vehicle is less than half of the capacity, using a rule I, otherwise, using a rule II;
and if no client point meeting the load capacity and the time window exists, returning to the warehouse, ending the sub-route and creating a new sub-route.
2. The intelligent cultural gene logistics distribution method based on similarity learning as claimed in claim 1, wherein the method comprises the following steps: the scheduling model comprises time constraints, load constraints, customer coordinates, vehicle scale, total running cost of the vehicle and an objective function of the vehicle, wherein the vehicle scale comprises vehicle rental cost, driver arrangement cost and road toll payment.
3. The intelligent cultural gene logistics distribution method based on similarity learning as claimed in claim 2, wherein the method comprises the following steps: the objective function is min z ═ α nv(s) + β tc(s) + tp(s), wherein the optimization objective includes vehicle scale, total distance traveled by the vehicle as short as possible, and time cost as low as possible; alpha is the lowest cost per vehicle, NV (S) is the total number of vehicles used, beta is the fuel consumption coefficient, TC (S) is the total cost for completing all distribution tasks, and TP (S) is the total time cost.
4. The intelligent cultural gene logistics distribution method based on similarity learning as claimed in claim 3, wherein the method comprises the following steps: the total cost of completing all delivery tasks
Wherein SP (P)j,Pj+1) Representing a slave client PjTo client Pj+1The running cost of (n), len(s)i) Indicating the number of customers served by the vehicle with the number i, and M indicating the total number of vehicles of the fleet;
this equation represents that the cost of travel between two customer points can be calculated by multiplying the length of the route between them by the congestion level of that route, where ω isiIs the congestion level of the road i, riFor the length of road i, this equation can be solved using dijkstra's algorithm.
5. The intelligent cultural gene logistics distribution method based on similarity learning as claimed in claim 3, wherein the method comprises the following steps: the total time cost
Wherein λ1For a time penalty factor of early arrival, λ2For late arrival time penalty factor, len (S) represents the number of all client points in the current solution, aiEarliest permitted moment of service start for delivery destination i, biFor the latest starting service time of the delivery destination, tiTo serve the time when the vehicle reaches destination i.
6. The intelligent cultural gene logistics distribution method based on similarity learning as claimed in claim 1, wherein the method comprises the following steps: in the step S5, the similarity of the individuals in the population is calculated
Wherein, the SimilariRepresenting the similarity between the vehicle service sequence with the current individual number i in the population and the service sequences of other individuals;
7. The intelligent cultural gene logistics distribution method based on similarity learning as claimed in claim 1, wherein the method comprises the following steps:
in step S2, a part of individuals in the population is initialized randomly, and a part of individuals in the population is initialized greedy, so as to obtain an initial distribution scheme.
8. The intelligent cultural gene logistics distribution method based on similarity learning as claimed in claim 1, wherein the method comprises the following steps: in step S4, two individuals to be crossed are selected by roulette selection, and the chromosomes are crossed to obtain offspring by using a sequence-based crossing scheme.
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