CN111178440A - Path optimization method for efficiently reducing total transport cost of AGV - Google Patents
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Abstract
A path optimization method for efficiently reducing the total transport cost of an AGV belongs to the technical field of path optimization; the prior art can lengthen the running path of the AGV, increase the number of the AGV and increase the cost; determining the number of required delivery stations according to the number of targets to be delivered, demand data and a delivery range; performing initial clustering by using a K-Means algorithm according to the distance between the target to be distributed and the distribution station, wherein the number of clusters is the number of the distribution stations; dividing targets which are more than SumD from the distribution station and have a direction included angle of less than 45 degrees with a connecting line of the distribution station into targets to be distributed, and keeping other targets in initial distribution, namely determining the targets; searching a distribution path by using a genetic algorithm, and calculating a shorter distribution route, namely an initial distribution route; on the basis of the initial distribution route, the distribution station to which the target to be distributed belongs is dynamically adjusted, the distribution route is optimized again, the shortest distribution distance is kept, the number of AGVs is saved, and the total distribution cost is reduced.
Description
Technical Field
The invention belongs to the technical field of path optimization, and particularly relates to a path optimization method for efficiently reducing total transport cost of an AGV.
Background
Path optimization means that N targets to be allocated are visited, each target can only pass through once, a short path is planned, and finally the original starting point needs to be returned, and the path optimization can be concluded as a TSP problem, namely a typical N-P problem. In the actual transportation process, due to the limitation of AGV capacity and other aspects, the model of the AGV cannot be solved by the model of the TSP problem.
There are many methods available to solve the path optimization problem. Early optimization algorithms mainly focus on precise algorithms, such as a simplex method, a secant plane method, a copy exclusion method and the like. However, under the constraint of the solution model, in practical application, the construction of a heuristic algorithm which is more widely applied is more important to solve the problems. Heuristic algorithms are also developed from the saving algorithms of the last century, the interpolation method and the like to artificial intelligent taboo search algorithms, simulated annealing algorithms, self-adaptive large neighborhood search algorithms and the like.
Because the positions of the targets to be distributed are randomly distributed and the distribution requirements are different, the path planning problem of multiple distribution targets is more complicated due to the diversity of the quantity and the requirements of the distribution targets. With this distributed configuration, if the random distribution method is adopted, not only the travel path of the AGVs is lengthened but also the number of AGVs is increased, thereby increasing the total cost.
Disclosure of Invention
The invention overcomes the defects of the prior art, provides a path optimization method based on high-efficiency AGV total transportation cost reduction, clusters the targets according to the actual situation, and the clustering principle is the distance between the targets and the distribution station, and essentially reduces the distribution distance, and achieves the purpose of reducing the number of AGVs and optimizing the total distribution cost by dynamically adjusting the belongings of the targets to be distributed.
The technical scheme of the invention is as follows:
a path optimization method based on efficient AGV total transportation cost reduction comprises the following steps:
step a, determining the number of required distribution stations according to the number of targets to be distributed, demand data and distribution range;
b, performing initial clustering by using a K-Means algorithm according to the distance between the target to be distributed and the distribution station, wherein the number of clusters is the number of the distribution stations;
c, on the basis of the initial clustering, dividing targets which are more than SumD away from the distribution station and have a direction included angle of less than 45 degrees with a connecting line of the distribution station into targets to be distributed, and keeping the initial distribution of other targets, namely determined targets;
d, searching a distribution path by using a genetic algorithm, and calculating a shorter distribution route, namely an initial distribution route;
and e, on the basis of the initial distribution route, optimizing the distribution route again by dynamically adjusting the distribution station to which the target to be distributed belongs, keeping the shortest distribution distance, saving the quantity of the AGVs and achieving the purpose of optimizing the total distribution cost.
Further, the method using the K-Means algorithm in step b comprises the following steps:
step b1, determining the 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 conditions.
Further, the specific method of step c comprises the following steps:
step c1, determining initial parameters of the K-Means algorithm, including determining the number of clusters, selecting the position of an initial centroid and iterating termination conditions;
step c2, determining the distance from each data target to each clustering centroid in the K-Means algorithm, and classifying the data target to the class of the clustering centroid closest to the data target;
step c3, calculating a new clustering centroid for the adjusted new class;
step c4, an iterative algorithm, wherein when all data targets are correctly classified, no adjustment is performed, and no change is performed on the clustering center, which indicates that convergence is performed;
and c5, dividing the targets which are more than SumD away from the distribution station and have a direction included angle of less than 45 degrees with the direction between the targets and the connection line of the distribution station into targets to be distributed on the basis of the step c4, namely the initial clustering, and keeping the initial distribution of other targets, namely the determined targets.
Further, the specific method of step d comprises the following steps:
d1, initializing experimental parameters of the genetic algorithm according to actual conditions;
d2, searching a distribution path by using a genetic algorithm, and calculating a shorter distribution route, namely an initial distribution route;
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, the shorter distribution distance is kept, the distribution vehicles are saved, and the purpose of optimizing the distribution total 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 AGV total transportation cost reduction, which determines the number of distribution stations according to the number and demand data of targets to be distributed and distribution range, divides the targets into parts to be determined and determined parts on the basis of initial K-Means clustering, searches distribution paths by using a genetic algorithm, calculates shorter distribution routes, namely initial distribution routes, optimizes the distribution routes again by dynamically adjusting the distribution stations to which the targets to be distributed belong, keeps shorter distribution distances, saves the number of AGVs at the same time, and achieves the purpose of optimizing the total distribution cost.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a target to be determined and a target determination map;
figure 3 is a comparison graph of the actual optimization of 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 AGV total transport cost reduction, as shown in fig. 1 and 2, comprising the steps of:
step a, determining the number of required distribution stations according to the number of targets to be distributed, demand data and distribution range;
b, performing initial clustering by using a K-Means algorithm according to the distance between the target to be distributed and the distribution station, wherein the number of clusters is the number of the distribution stations;
c, on the basis of the initial clustering, dividing targets which are more than 0.7 of SumD mean variance from the distribution station and have a direction included angle of less than 45 degrees with a connection line of the distribution station into targets to be distributed, and keeping the initial distribution of other targets, namely determined targets;
d, searching a distribution path by using a genetic algorithm, and calculating a shorter distribution route, namely an initial distribution route;
and e, on the basis of the initial distribution route, optimizing the distribution route again by dynamically adjusting the distribution station to which the target to be distributed belongs, keeping a short distribution distance, saving the quantity of AGV (automatic Guided vehicles) and achieving the aim of optimizing the total distribution cost.
Specifically, the method for applying the K-Means algorithm in the step b comprises the following steps:
step b1, determining the 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 conditions.
Specifically, the constraints in step b1 include:
(1) the capacity of the AGV carts to carry items is limited, i.e., not capable of being delivered at one time to meet all customer requirements.
(2) Each user can only deliver once.
(3) The delivery path is symmetrical, i.e., the AGV carts are the same distance from user i to j as j to i.
(4) And (5) distribution demand conservation constraint.
(5) The AGV must return to the dispensing station after completing the task and have no articles.
(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 travel distance of the AGV not only relate to the oil consumption cost in the distribution process, but also include the problem of working efficiency, and the shorter travel path not only can reduce the oil consumption cost, but also can improve the working efficiency and the like.
(2) The distribution task cost and the cost in the distribution process mainly comprise 1, the number of distribution vehicles. Both for 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 excessively 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 from i to jij1, otherwise c ij0. And xijRepresenting the path length from i to j.
Cost of delivery tasks
KiRepresenting the number of AGVs, CV1Representative coefficient, CV in the present embodiment1＝500
Cost of distribution station
ymRepresenting the number of AGVs, CV2Representative coefficient, CV in the present embodiment2＝500。
Specifically, the objective function of the distribution path optimization problem for constructing the multi-user random demand in step b4 is the sum of the three distribution costs, that is, the sum
Specifically, the specific method of step c comprises the following steps:
step c1, determining initial parameters of the K-Means algorithm, including determining the number of clusters, selecting the position of an initial centroid and iterating termination conditions;
step c2, determining the distance from each data target to each clustering centroid in the K-Means algorithm, and classifying the data target to the class of the clustering centroid closest to the data target;
step c3, calculating a new clustering centroid for the adjusted new class;
step c4, an iterative algorithm, wherein when all data targets are correctly classified, no adjustment is performed, and no change is performed on the clustering center, which indicates that convergence is performed;
and c5, on the basis of the step c4, namely the initial clustering, dividing the targets which are more than 0.7 of SumD mean variance from the distribution station and have a direction included angle of less than 45 degrees with the connection line of the distribution station into targets to be distributed, and keeping the other targets in the initial distribution, namely the determined targets.
Specifically, the determining of the initial parameters of the K-Means algorithm in the step c1 includes determining the number of clusters, selecting an initial centroid position, and an iteration termination condition; the number of clusters is determined according to the actual situation and the data amount during historical shipment, the number of distribution stations is the number of actual clusters, and the number of distribution stations in the embodiment is determined to be 3. The K-Means algorithm is sensitive to the initially selected centroid points, and the clustering results obtained by different random seed points are completely different, so that the results are greatly influenced. The most common method is random selection, and the selection of the initial centroid has an influence on the final clustering result, so the algorithm must be executed several times more, and which result is more reasonable, the first method is to select the point with the farthest distance from each other, specifically, to select the first point, then select the second point which is farthest from the first point, then select the third point, and the sum of the distances from the third point to the first and second points is the smallest, and so on.
Specifically, the specific method in step c2 includes first determining a K value, i.e., the number of clusters, which is 3 in this embodiment, i.e., we want to cluster the data set into K categories. K data points are then randomly selected from the data set as centroids. Then, for each point in the data set, the distance between the point and each centroid, namely the Euclidean distance, is calculated, the distance between each data point, namely the user and all centroids is calculated, and the data point is assigned to the centroid with the minimum distance. After all data are grouped together, K groups are shared, a new centroid is calculated by using the mean value of each data point in each class, 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 means the position of the recalculated centroid does not change much and tends to be stable or converged, it can be considered that the clustering has reached the expected result, and the algorithm is terminated. If the new centroid and the original centroid are widely separated, the iteration steps are repeated, and the formula is as follows.
Specifically, the specific method of step d comprises the following steps:
d1, initializing experimental parameters of the genetic algorithm according to actual conditions;
d2, searching a distribution path by using a genetic algorithm, and calculating a shorter distribution route, namely an initial distribution route;
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, the shorter distribution distance is kept, the distribution vehicles are saved, and the purpose of optimizing the distribution total cost is achieved.
Specifically, the genetic algorithm in step d2 comprises the following steps:
step d21, initialization: setting an evolution algebra counter T to be 0, setting a maximum evolution algebra T, a cross probability, a mutation probability and 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, selection operation: the selection operator is applied to the population. Based on individual fitness, selecting optimal individual to be directly inherited to next generation or generating new individual through pairing and crossing and then being inherited to next generation
Step d24, cross operation: crossing every two individuals in the group under the control of the crossing probability
Step d25, mutation operation: under the control of variation probability, two individuals in the group are varied, namely, the gene of one individual is randomly adjusted
And d26, obtaining the next generation group P1 after selection, intersection and mutation operation.
And (4) repeating the steps d 21-d 26 until the genetic algebra is T, outputting the individuals with the maximum fitness obtained in the evolution process as the optimal solution, and stopping the calculation.
The genetic algorithm basically does not utilize external information in evolution search, only takes a fitness function as a basis, and utilizes the fitness value of each individual in a population to search. The inverse of the total path length may be the fitness.
Generally, selection will give more chance of the presence of more suitable (good) individuals and less chance of the continued presence of less suitable (bad) individuals, and simple genetic algorithms employ a round-robin selection mechanism to order
Representing the sum of fitness values of the population, fiRepresenting the fitness value of the ith chromosome in the population, which gives rise to offspring in exactly the fraction of its fitness value
The number of iterations T is 1000. Genetic algorithm population size M is 100. The crossover probability is 0.9. The mutation 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; the reason is that the distribution problem of the user is easy to cause the situation that the utilization rate of the distribution vehicle is not high, namely, the quantity of the distributed articles carried by a plurality of AGV trolleys is less. However, by dividing users to be determined, the number of users in each distribution station can be adjusted, the distribution distance can also be adjusted, the utilization rate of a distribution vehicle can be improved, and the overall cost is reduced.
As shown in fig. 3, the following table is an experimental data table actually optimized in the present embodiment, to which comparative graphs before and after the present embodiment are applied.
TABLE 2 Result for the LRP instance from benchmark-T.
Claims (4)
1. A path optimization method based on efficient AGV total transportation cost reduction is characterized by comprising the following steps:
step a, determining the number of required distribution stations according to the number of targets to be distributed, demand data and distribution range;
b, performing initial clustering by using a K-Means algorithm according to the distance between the target to be distributed and the distribution station, wherein the number of clusters is the number of the distribution stations;
c, on the basis of the initial clustering, dividing targets which are more than SumD away from the distribution station and have a direction included angle of less than 45 degrees with a connecting line of the distribution station into targets to be distributed, and keeping the initial distribution of other targets, namely determined targets;
d, searching a distribution path by using a genetic algorithm, and calculating a shorter distribution route, namely an initial distribution route;
and e, on the basis of the initial distribution route, optimizing the distribution route again by dynamically adjusting the distribution station to which the target to be distributed belongs, keeping the shortest distribution distance, saving the quantity of the AGVs and achieving the purpose of optimizing the total distribution cost.
2. The method for optimizing a path based on efficient AGV total transport cost reduction of claim 1, wherein said method using K-Means algorithm in step b comprises the following steps:
step b1, determining the 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 conditions.
3. The method for optimizing the path based on the efficient reduction of the total transportation cost of the AGV according to claim 1, wherein the specific method of the step c comprises the following steps:
step c1, determining initial parameters of the K-Means algorithm, including determining the number of clusters, selecting the position of an initial centroid and iterating termination conditions;
step c2, determining the distance from each data target to each clustering centroid in the K-Means algorithm, and classifying the data target to the class of the clustering centroid closest to the data target;
step c3, calculating a new clustering centroid for the adjusted new class;
step c4, an iterative algorithm, wherein when all data targets are correctly classified, no adjustment is performed, and no change is performed on the clustering center, which indicates that convergence is performed;
and c5, dividing the targets which are more than SumD away from the distribution station and have a direction included angle of less than 45 degrees with the direction between the targets and the connection line of the distribution station into targets to be distributed on the basis of the step c4, namely the initial clustering, and keeping the initial distribution of other targets, namely the determined targets.
4. The method for optimizing the path based on the efficient reduction of the total transportation cost of the AGV according to claim 1, wherein the specific method of the step d comprises the following steps:
d1, initializing experimental parameters of the genetic algorithm according to actual conditions;
d2, searching a distribution path by using a genetic algorithm, and calculating a shorter distribution route, namely an initial distribution route;
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, the shorter distribution distance is kept, the distribution vehicles are saved, and the purpose of optimizing the distribution total cost is achieved.
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