CN111178440B - Path optimization method based on efficient reduction of total transport cost of AGV - Google Patents
Path optimization method based on efficient reduction of total transport cost of AGV Download PDFInfo
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Abstract
A path optimization method based on efficient reduction of AGV total transportation cost belongs to the technical field of path optimization; the prior art can lengthen the travel path of the AGVs, increase the number of the AGVs and increase the cost; determining the number of required delivery stations according to the number of targets to be delivered, demand data and the delivery range; according to the distance between the object to be distributed and the distribution stations, carrying out initial clustering by using a K-Means algorithm, wherein the number of clusters is the number of the distribution stations; dividing targets with a distance from a delivery station greater than SumD and a direction included angle between the targets and a connection line of the delivery station less than 45 degrees into targets to be delivered, and keeping other targets to be initially distributed, namely determining targets; searching a delivery path by using a genetic algorithm, and calculating a shorter delivery path, namely an initial delivery path; on the basis of the initial delivery route, the delivery station to which the target to be delivered belongs is dynamically adjusted, the delivery route is optimized again, the shortest delivery distance is kept, the number of AGVs is saved, and the total delivery cost is reduced.
Description
Technical Field
The invention belongs to the technical field of path optimization, and particularly relates to a path optimization method based on efficient reduction of total transport cost of an AGV.
Background
The path optimization refers to the process of visiting N objects to be distributed, each object can only pass once, a shorter path is planned, and finally, the original place is returned to, so that the path optimization can be reduced to the TSP problem, namely the typical N-P problem. In the actual transportation process, the model is not solved by the model of the TSP problem due to the limitation of the AGV capacity and other aspects.
There are many methods available to solve the path optimization problem. Early optimization algorithms have focused mainly on accurate algorithms such as simplex, planar cutting, hidden enumeration, etc. However, the constraint of the solving model is more biased to construct and apply a wider heuristic algorithm to solve the problems in practical application. Heuristic algorithms have also evolved from saving algorithms, interpolation methods, etc. in the last century to artificial intelligence tabu search algorithms, simulated annealing algorithms, adaptive large neighborhood search algorithms, etc.
The problem of path planning for multiple delivery targets is further complicated by the variability in the number and demand of delivery targets, since the locations of the targets to be delivered are randomly distributed and the delivery demands are different. With such a distributed structure, if a random distribution method is adopted, not only the traveling path of the AGV becomes long, but also the number of AGVs increases, thus increasing the total cost.
Disclosure of Invention
The invention overcomes the defects of the prior art, provides the path optimization method based on high-efficiency reduction of the total transport cost of the AGVs, clusters targets according to actual conditions, and the clustering principle is that the distance between the targets and the distribution stations is basically that the distribution distance is reduced, and the purpose of reducing the number of the AGVs is achieved by dynamically adjusting the distribution distance of the targets to be distributed, so that the total distribution cost is optimized.
The technical scheme of the invention is as follows:
a path optimization method based on efficient reduction of AGV total transport cost comprises the following steps:
step a, determining the number of required delivery stations according to the number of targets to be delivered, the demand data and the delivery range;
step b, performing initial clustering by using a K-Means algorithm according to the distance between the object to be distributed and the distribution stations, wherein the number of clusters is the number of the distribution stations;
step c, dividing targets with a distance between the targets and the distribution station being greater than SumD and a direction included angle between the targets and the distribution station being smaller than 45 degrees into targets to be distributed on the basis of initial clustering, and keeping the other targets to be distributed initially, namely determining the targets;
step d, searching a distribution path by using a genetic algorithm, and calculating a shorter distribution path, namely an initial distribution path;
and e, on the basis of the initial delivery route, the delivery route is optimized again by dynamically adjusting the delivery station to which the target to be delivered belongs, so that the shortest delivery distance is kept, the number of AGVs is saved, and the purpose of optimizing the total delivery cost is achieved.
Further, the method for applying the K-Means algorithm in the step b comprises the following steps:
step b1, determining constraint conditions of the problems according to the path optimization problems;
step b2, determining the components of the total cost of the path optimization problem;
step b3, determining a simplified model of the cost of each part;
and b4, constructing an objective function of the distribution path optimization problem of the multi-user random demand according to the constraint condition.
Further, the specific method of the step c comprises the following steps:
step c1, determining initial parameters of a K-Means algorithm, including determining the number of clusters, selecting initial centroid positions and iteration termination conditions;
step c2, determining the distance between each data object and each cluster centroid in the K-Means algorithm, and classifying the data object into the class of the cluster centroid closest to the data object;
step c3, calculating a new cluster centroid by the adjusted new class;
step c4, iterating the algorithm, and when all the data targets are correctly classified, not adjusting the data targets, and not changing the clustering center, wherein the convergence is marked;
step c5, dividing the targets with the distance from the delivery station being larger than SumD and the direction included angle between the targets and the connection line of the delivery station being smaller than 45 degrees into targets to be delivered on the basis of step c4, namely initial clustering, and keeping the other targets to be initially distributed, namely the determined targets.
Further, the specific method of the step d comprises the following steps:
step d1, initializing experimental parameters of a genetic algorithm according to actual conditions;
step d2, searching a distribution path by using a genetic algorithm, and calculating a shorter distribution path, namely an initial distribution path;
step d3, dynamically adjusting the distribution station of the target to be distributed on the basis of the initial distribution route, wherein the aim is to reduce the distribution cost;
and d4, on the basis of the step d3, after the target to be determined is adjusted, the distribution route is optimized again, so that a shorter distribution distance is kept, meanwhile, the distribution vehicles are saved, and the purpose of optimizing the total distribution cost is achieved.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a path optimization method based on high-efficiency reduction of total transport cost of AGVs, which is characterized in that the number of delivery stations is determined according to the number of targets to be delivered, demand data and delivery range, the targets are divided into a part to be determined and a part to be determined on the basis of initial K-Means clustering, a genetic algorithm is used for searching and delivering paths, a shorter delivery route, namely an initial delivery route, is calculated, the delivery stations of the targets to be delivered are dynamically adjusted, the delivery route is optimized again, the shorter delivery distance is kept, meanwhile, the number of AGVs is saved, and the purpose of optimizing the total delivery cost is achieved.
Drawings
FIG. 1 is a general flow chart of the present invention;
FIG. 2 is a diagram of a target to be determined and a determination target;
FIG. 3 is a graph of actual optimization versus the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
A path optimization method based on efficient reduction of the total transport cost of an AGV, as shown in FIGS. 1 and 2, comprises the following steps:
step a, determining the number of required delivery stations according to the number of targets to be delivered, the demand data and the delivery range;
step b, performing initial clustering by using a K-Means algorithm according to the distance between the object to be distributed and the distribution stations, wherein the number of clusters is the number of the distribution stations;
step c, dividing targets with a distance from a distribution station being larger than 0.7 of SumD mean variance and a direction included angle between the targets and a distribution station connecting line being smaller than 45 degrees into targets to be distributed on the basis of initial clustering, and keeping other targets to be distributed initially, namely determining the targets;
step d, searching a distribution path by using a genetic algorithm, and calculating a shorter distribution path, namely an initial distribution path;
and e, on the basis of the initial delivery route, the delivery route is optimized again by dynamically adjusting the delivery station to which the target to be delivered belongs, so that the shorter delivery distance is kept, the number of AGVs (Automated Guided Vehicle) is saved, and the purpose of optimizing the total delivery cost is achieved.
Specifically, the method for applying the K-Means algorithm in the step b comprises the following steps:
step b1, determining constraint conditions of the problems according to the path optimization problems;
step b2, determining the components of the total cost of the path optimization problem;
step b3, determining a simplified model of the cost of each part;
and b4, constructing an objective function of the distribution path optimization problem of the multi-user random demand according to the constraint condition.
Specifically, the constraint conditions in step b1 include:
(1) The capacity of the articles that an AGV cart can carry is limited, i.e., it cannot be dispensed at a time to meet all customers' requirements.
(2) Each user can only dispense once.
(3) The delivery path is symmetrical, i.e., the AGV trolley is the same distance from user i to j as j to i.
(4) Distribution demand conservation constraints.
(5) The AGV must return to the dispensing station after completing the task and no items.
(6) Each user can be assigned to only one distribution station, etc.
Specifically, the components of the total cost of the path optimization problem in step b2 include the following:
(1) The path cost and the traveling distance of the AGV are not only related to the fuel consumption cost in the distribution process, but also comprise the problem of working efficiency, and the shorter traveling path can not only reduce the fuel consumption cost, but also improve the working efficiency and the like.
(2) The cost of the delivery task mainly comprises 1. The number of delivery vehicles. Both to the total cost.
(3) The cost of the distribution stations is mainly reflected in the number of the distribution stations, the distribution stations with small number cannot complete the specified tasks on time, the distribution stations consume too much manpower and material resources, and the reasonable number of the distribution stations is an important ring for saving the cost.
Specifically, the simplified module of the cost per part in step b3 is:
the path cost is
C if AGV goes from i to j ij =1, otherwise c ij =0. And x is ij Representing the path length from i to j.
Cost of delivery task
K i Representing the number of AGVs, CV 1 Representative coefficient, CV in this embodiment 1 ＝500
Cost of distribution station
y m Representing the number of AGVs, CV 2 Representative coefficient, CV in this embodiment 2 ＝500。
Specifically, the objective function of the distribution path optimization problem described in step b4, which constructs the multiuser random demand, is the sum of the three-part distribution costs, i.e
Specifically, the specific method of the step c comprises the following steps:
step c1, determining initial parameters of a K-Means algorithm, including determining the number of clusters, selecting initial centroid positions and iteration termination conditions;
step c2, determining the distance between each data object and each cluster centroid in the K-Means algorithm, and classifying the data object into the class of the cluster centroid closest to the data object;
step c3, calculating a new cluster centroid by the adjusted new class;
step c4, iterating the algorithm, and when all the data targets are correctly classified, not adjusting the data targets, and not changing the clustering center, wherein the convergence is marked;
step c5, dividing targets with a distance from the distribution station being greater than 0.7 of SumD mean variance and a direction included angle between the targets and the distribution station connecting line being smaller than 45 degrees into targets to be distributed on the basis of step c4, namely initial clustering, and keeping other targets to be distributed initially, namely the determined targets.
Specifically, the initial parameters determined in the K-Means algorithm in the step c1 include determining the number of clusters, the selection of initial centroid positions, and the iteration termination condition; the number of clusters is the number of distribution stations determined according to the actual situation and the data amount at the time of historic shipment, and the number of distribution stations, that is, the number of actual clusters, is determined to be 3. The K-Means algorithm is sensitive to the initially selected centroid point, and clustering results obtained by different random seed points are completely different, so that the results are greatly affected. The most common method is random selection, the selection of the initial centroid has an influence on the final clustering result, so the algorithm must be executed for more times, which result is more reasonable, the first is to select the point with the farthest distance from each other, specifically, the first point is selected firstly, then the second point with the farthest distance from the first point is selected, then the third point is selected, and the sum of the distances from the third point to the first point and the second point is the smallest, and so on.
Specifically, the specific method of step c2 includes determining a K value, that is, the number of clusters, and in this embodiment, the value is 3, that is, we want to cluster the data set to obtain K categories. K data points are then randomly selected from the dataset as centroids. Then, for each point in the dataset, calculate its distance from each centroid, i.e., euclidean distance, calculate each data point, i.e., the user's distance from all centroids, and assign the data point to the centroid with the smallest distance. After all the data are grouped together, K groups are combined, and in each classification, a new centroid is calculated by using the average value of each data point, and the value of the centroid is updated. If the distance between the newly calculated centroid and the original centroid is smaller than a certain set threshold, that is, the position of the newly calculated centroid is not changed greatly and tends to be stable, or converged, the clustering can be considered to reach the expected result, and the algorithm is terminated. If the distance between the new centroid and the original centroid varies greatly, the above iterative steps need to be repeated, and the formula is as follows.
Specifically, the specific method of the step d comprises the following steps:
step d1, initializing experimental parameters of a genetic algorithm according to actual conditions;
step d2, searching a distribution path by using a genetic algorithm, and calculating a shorter distribution path, namely an initial distribution path;
step d3, dynamically adjusting the distribution station of the target to be distributed on the basis of the initial distribution route, wherein the aim is to reduce the distribution cost;
and d4, on the basis of the step d3, after the target to be determined is adjusted, the distribution route is optimized again, so that a shorter distribution distance is kept, meanwhile, the distribution vehicles are saved, and the purpose of optimizing the total distribution cost is achieved.
Specifically, the genetic algorithm in step d2 includes the following steps:
step d21, initializing: setting an evolution algebra counter t=0, setting a maximum evolution algebra T, cross probability, mutation probability, randomly generating M individuals as an initial population P
Step d22, individual evaluation: calculating the fitness of each individual in the population P
Step d23, selecting operation: the selection operator is applied to the population. Based on individual fitness, selecting optimal individuals to inherit directly to the next generation or to generate new individuals by crossover pairing to inherit to the next generation
Step d24, cross operation: under the control of crossover probability, individuals in the population are crossed pairwise
Step d25, mutation operation: under the control of mutation probability, the individuals in the population are mutated pairwise, namely, the gene of one individual is randomly regulated
Step d26, selecting, crossing and mutating to obtain the next generation group P1.
Repeating the steps d 21-d 26 until the genetic algebra is T, outputting the individual with the maximum fitness obtained in the evolution process as the optimal solution, and terminating the calculation.
The genetic algorithm basically does not use external information in evolutionary search, and searches by using the fitness value of each individual in the population only based on the fitness function. The inverse of the total path length may be the fitness.
Generally, selection will give individuals with greater fitness (good) a greater chance of existence, while individuals with less fitness (bad) will continue to exist with less chance, with simple genetic algorithms employing a bet wheel selection mechanism, giving rise to
Representing the sum of fitness values of a population, f i Representing the fitness value of the ith chromosome in the population, whose ability to produce offspring is exactly the fraction of its fitness value +.>
The number of iterations is t=1000. Genetic algorithm population size m=100. The crossover probability is 0.9. The variation probability is 0.1.
Specifically, in step d3, the dynamic adjustment is performed on the distribution station to which the target to be distributed belongs; because the distribution problem of users is easy to occur that the utilization rate of the distribution vehicle is not high, namely, the quantity of the carried distribution objects of a plurality of AGV vehicles is small. However, by dividing the users to be determined, the number of the users at each distribution station can be adjusted, the distribution distance can be adjusted, and meanwhile, the utilization rate of the distribution vehicle can be improved, so that the overall cost is reduced.
As shown in fig. 3, the following table is a table of experimental data actually optimized in the present embodiment, using a comparison chart of the present embodiment before and after.
TABLE 2 Result for the LRP instance from benchmark-T.
Claims (3)
1. A path optimization method based on efficient reduction of the total transport cost of an AGV is characterized by comprising the following steps:
step a, determining the number of required delivery stations according to the number of targets to be delivered, the demand data and the delivery range;
step b, performing initial clustering by using a K-Means algorithm according to the distance between the object to be distributed and the distribution stations, wherein the number of clusters is the number of the distribution stations;
step c, dividing targets to be distributed into targets to be distributed, and keeping the initial distribution of other targets, namely the determined targets, on the basis of initial clustering, wherein the distance between the targets to be distributed and a distribution station is larger than the product of SumD mean variance and 0.7, and the direction included angle between the targets to be distributed and the distribution station is smaller than 45 degrees; the specific method of the step c comprises the following steps:
step c1, determining initial parameters of a K-Means algorithm, including determining the number of clusters, selecting initial centroid positions and iteration termination conditions;
step c2, determining the distance between each data object and each cluster centroid in the K-Means algorithm, and classifying the data object into the class of the cluster centroid closest to the data object;
step c3, calculating a new cluster centroid by the adjusted new class;
step c4, iterating the algorithm, and when all the data targets are correctly classified, not adjusting the data targets, and not changing the clustering center, wherein the convergence is marked;
step c5, dividing targets to be distributed into targets to be distributed, and keeping the initial distribution of other targets on the basis of the initial clustering, wherein the distance between the targets to be distributed and a distribution station is larger than the product of SumD mean variance and 0.7, and the direction included angle between the targets to be distributed and the distribution station is smaller than 45 degrees;
SumD is the sum of the distances between all objects to be distributed of each cluster and the distribution stations of the cluster;
step d, searching a distribution path by using a genetic algorithm, and calculating a shorter distribution path, namely an initial distribution path;
and e, on the basis of the initial delivery route, the delivery route is optimized again by dynamically adjusting the delivery station to which the target to be delivered belongs, so that the shortest delivery distance is kept, the number of AGVs is saved, and the purpose of optimizing the total delivery cost is achieved.
2. The path optimization method based on efficient reduction of total transport costs of an AGV according to claim 1, wherein the method of applying the K-Means algorithm in step b comprises the steps of:
step b1, determining constraint conditions of the problems according to the path optimization problems;
step b2, determining the components of the total cost of the path optimization problem;
step b3, determining a simplified model of the cost of each part;
and b4, constructing an objective function of the distribution path optimization problem of the multi-user random demand according to the constraint condition.
3. The path optimization method based on efficient reduction of the total transport cost of the AGV according to claim 1, wherein the specific method of step d comprises the steps of:
step d1, initializing experimental parameters of a genetic algorithm according to actual conditions;
step d2, searching a distribution path by using a genetic algorithm, and calculating a shorter distribution path, namely an initial distribution path;
step d3, dynamically adjusting the distribution station of the target to be distributed on the basis of the initial distribution route, wherein the aim is to reduce the distribution cost;
and d4, on the basis of the step d3, after the target to be determined is adjusted, the distribution route is optimized again, so that a shorter distribution distance is kept, meanwhile, the distribution vehicles are saved, and the purpose of optimizing the total distribution cost is achieved.
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