CN111091238B - Automatic container terminal AGV intelligent scheduling method - Google Patents
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Abstract
The application discloses an automatic container terminal AGV intelligent scheduling method, which takes a bin position and a bin region as nodes, takes the distance between the bin region with a scheduling task relationship and the bin position as an edge, constructs an AGV scheduling model, takes the minimum value of the constructed AGV scheduling model as an objective function, and adopts a genetic algorithm to solve an AGV optimal path; the application provides a path optimization model capable of improving the utilization rate of AGVs as much as possible under the condition of limited number of AGVs, and the AGV path planning can be carried out according to the operation state and the expected operation time of the AGVs based on the model, so that the AGV path optimization with the shortest invalid operation time is realized, and the idle running and waiting time of the AGVs can be effectively reduced; by adopting genetic algorithm calculation, the AGV path can be calculated and optimized in real time according to the container position and time of the loading and unloading ship, so that the AGV path has better global calculation performance, and the calculation process can be effectively prevented from converging on a local optimal solution.
Description
Technical Field
The application belongs to the technical field of automatic container terminals, and particularly relates to an automatic container terminal AGV intelligent scheduling method.
Background
The container terminal is mainly transported horizontally by the collector cards, the distance is a very important parameter in the path planning process of the collector card fleet, and in order to reduce the fuel consumption of the collector cards and improve the collector card workload under the same fuel cost, the path with the shortest length is prioritized during the path planning. In order to ensure that the travel path of the collector card is shortest, the condition that the collector card waits for a shore bridge or a track crane is necessarily caused.
In an automatic container terminal, horizontal transportation is mainly carried out by an AGV (automatic guided vehicle), the AGV uses electric drive, the AGV is charged in an opportunity without endurance limit, and high cost caused by fuel consumption is not needed to be considered in path planning of the AGV, so that the electric power cost is low.
In the research stage of the current automatic wharf AGV horizontal transport system, a greedy algorithm is mainly adopted to calculate a path optimal solution, but the algorithm has the problem of being trapped into a local optimal solution, a calculation result is accurate when the number of AGVs is small, but the algorithm has more obvious disadvantages on global scheduling along with the increase of the number of the AGVs, individual AGVs can have excellent paths, other AGVs obviously grow at the same time, along with the expansion of the wharf scale, the number of the AGVs needs tens or even hundreds, and a path planning method based on the greedy algorithm can not meet the requirements.
Disclosure of Invention
The application aims to provide an automatic container terminal AGV intelligent scheduling method, which provides a path optimization model capable of improving the AGV utilization rate as much as possible under the condition of limited AGV quantity, on one hand, the AGV path planning can be carried out according to the operation state and the expected operation time of the AGVs based on the model, the AGV path optimization with the shortest invalid operation time can be realized, the idle running and waiting time of the AGVs can be effectively reduced, and the same efficiency can be realized by using fewer AGVs under the condition of the same operation quantity; on the other hand, by adopting genetic algorithm calculation, the AGV path can be calculated and optimized in real time according to the container position of the loading and unloading ship and the time lapse, so that the AGV path has better global calculation performance, and the calculation process can be effectively prevented from converging on a local optimal solution.
The application is realized by adopting the following technical scheme:
the automatic container terminal AGV intelligent scheduling method comprises the following steps: taking the shellfish position and the box region as nodes, taking the distance between the box region with the scheduling task relationship and the shellfish position as an edge, and constructing an AGV scheduling model:taking the minimum value of F as an objective function, and solving an optimal path of the AGV by adopting a genetic algorithm; wherein p is the total number of all scheduling tasks in the planned job period; t is t ij The time interval from the completion of the scheduling task i to the start of the execution of the scheduling task j is provided for the AGV; x is x ij And when the AGV executes the scheduling task i and then the scheduling task j, the value is 1, otherwise, the value is zero.
Further, constraint conditions of the constructed AGV scheduling model are as follows:
when taking 1, the AGV a first performs task i,
when the AGV finishes the task in 1, the work is finished;
wherein t is i Start time, g, for AGV to execute scheduled task i i And (3) the running time of the scheduling task i is completed for the AGVs, and N is the total number of the AGVs required in each planned working period.
Further, in the genetic encoding step of the genetic algorithm, the serial number of the AGV corresponding to the execution scheduling task i is taken as the gene x i Is a value of (a).
Further, the fitness function of the genetic algorithm adopts the objective function as a criterion.
Further, the crossing step of the genetic algorithm adopts single-point crossing; and in the mutation step of the genetic algorithm, single or two gene loci are selected for transformation.
Further, the genetic operator probability of the crossover operation is 0.4-0.9, and the genetic operator probability of the mutation operation is 0.05-0.2.
Further, in the genetic algorithm, a penalty function is setTo reduce violations of constraintsIs the fitness of the individual; wherein M is a positive integer, and when the individual meets the constraint condition, the value F is taken, and the constraint condition is not metAnd taking the value F-M when the constraint condition is met.
Further, the expected value of the invalid operation time of the AGV is set as t, and the genetic algorithm is terminated when the error between the invalid operation time value and the expected value in the genetic algorithm is smaller than a set range.
Further, the scheduling task is set as follows: the AGVs are transported from the dock front to the yard or from the yard to the dock front.
Compared with the prior art, the application has the advantages and positive effects that: in the automatic container terminal AGV intelligent scheduling method provided by the application, a path optimization model for improving the AGV utilization rate as much as possible under the condition of limited AGV quantity is providedThe minimum value of the path optimization model is solved as an objective function to determine the path optimization model as an AGV optimal path, AGV path planning can be carried out according to the operation state and the expected operation time of the AGV based on the path optimization model, AGV path optimization with the shortest invalid operation time is achieved, the idle running and waiting time of the AGV can be effectively reduced, and the same efficiency can be achieved by using fewer AGVs under the condition of the same operation amount.
According to the application, the genetic algorithm is adopted to solve the objective function, the AGV path can be calculated and optimized in real time according to the container position of the loading and unloading ship and the time lapse, the method has good global calculation performance, and the calculation process can be effectively prevented from converging on a local optimal solution; according to the method, high-efficiency heuristic search is performed in a solution space instead of blind exhaustive or completely random search, so that the calculation speed is higher, and the resource consumption in the calculation process is less; the method also has the characteristic of parallel computing, and can improve the computing speed through massive parallel computing under the condition of sufficient computer resources.
Other features and advantages of the present application will become more apparent from the following detailed description of embodiments of the present application, which is to be read in connection with the accompanying drawings.
Drawings
FIG. 1 is a flow chart of an automated container terminal AGV intelligent scheduling method according to the present application;
fig. 2 is a directed graph established by the automated container terminal AGV path planning method of the present application.
Detailed Description
The following describes the embodiments of the present application in further detail with reference to the drawings.
The application provides an automatic container terminal AGV intelligent scheduling method, which aims to realize the shortest invalid operation time and solve the operation time by adopting a genetic algorithm.
The invalid operation time of the AGV mainly comprises two parts of waiting for a quay bridge and idle running, so that the following points are referred to when the AGV path optimization model is established:
(1) The priority of the shore bridge is higher than that of the AGV; that is, during the dispatch process, if a situation occurs in which the mechanical device needs to wait, the AGV may wait for the quay bridge, but the quay bridge should be avoided from waiting for the AGV.
(2) The application applies for the fact that the interaction area is set up at the end of the storage yard based on the mixed interaction mode of bracket interaction and direct interaction of the AGV and the storage yard track crane, the interaction area is constructed, no matter the AGV or the storage yard track crane, the container is firstly transported to the interaction area, the container can be directly placed on the interaction area without waiting for the latter, and the container can be directly taken from the interaction area after the latter arrives.
(3) The loading and unloading tasks of each quay bridge are determined before loading and unloading are started, the period of the container ship is generally formulated by a ship company one month before the ship arrives at a port, and the loading and unloading schedule, the ship loading diagram, the box information and the like are included, so that a dock dispatcher can confirm the loading and unloading plan with the ship company in advance.
In the embodiment of the application, a scheduling task is set as follows: and (3) transporting the AGVs from the dock front to a storage yard or a storage yard to the dock front. The loading and unloading plan of the quay before the start of the operation has been completed, the loading and unloading amount, the average loading and unloading efficiency of the quay and the container sequence of the operation have been given, and the latest start time of each task of the quay can be calculated as long as the quay can continuously operate, so that each container of the quay can judge the time and place of the start operation and the time and place of the completion operation, and the distance between each scheduling task is also known.
In the embodiment of the application, in order to change the mode of the AGV for fixedly serving each quay, the AGVs of all quay operations are comprehensively considered, and are ordered according to the operation starting time, and the quay factors are converted into time factors and constrained to the AGV operations.
Based on the above, the automatic container terminal AGV intelligent scheduling method provided by the application, as shown in FIG. 1, comprises the following steps:
step S1: and establishing a directed graph by taking the shellfish position and the box area as nodes and taking the distance between the box area with the scheduling task relationship and the shellfish position as an edge.
The embodiment of the application is developed aiming at the scheduling problem of AGVs when loading and unloading different beta containers; taking packing as an example, taking the shellfish position and the bin area on the code head as nodes to construct a directed graph (V, E), wherein V represents the nodes and represents entities such as the bin area, the shellfish position and the like; e represents the edges, representing the distances between the bin sections and between the shellfish positions.
If there are m outlet boxes, n bays to be boxed, q trolleys to be worked, when the boxing is required, the q trolleys are responsible for transporting the containers from the outlet boxes to the corresponding bays. If an outlet bin section is to be bin a bin, they are joined by a spaced edge to form a directed graph as shown in FIG. 2.
Step S2: an AGV scheduling model is constructed based on the directed graph.
The AGV scheduling model is constructed as follows:
wherein p is the total number of all scheduling tasks in the planned job period; t is t ij The time interval from the completion of the scheduling task i to the start of the execution of the scheduling task j for the AGV comprises the time of waiting for the quay bridge by the AGV and the time of no-load when entering and exiting in a storage yard for transferring, and is invalid operation time, i, j epsilon p; x is x ij Execute in advance of AGVThe value of the scheduling task j is 1 when the scheduling task i is executed, otherwise, the value is zero, namelyWhen 1 is taken, the AGV executes the task i first and then executes the task j.
Constraint conditions of the AGV scheduling model constructed are as follows:
AGV a first performs task i, (7) when taking 1
When the AGV finishes the task in 1, the work is finished; (8)
Wherein t is i Start time, g, for AGV to execute scheduled task i i And (3) the running time of the scheduling task i is completed for the AGVs, and N is the total number of the AGVs required in each planned working period.
The formula (2) represents the condition constraint that two scheduling tasks i, j can be satisfied by the sequential operation; the constraint task starting times are only one and only one time in the formula (3); the constraint task ending times of the formula (4) are only one time; the AGV operation starting times are restrained in the formula (5) and only once; the constraint operation is completed once and only once in the formula (6); the formula (7) and the formula (8) are decision variables of "0" or "1".
Step S3: and F, taking the minimum value as an objective function, and solving the optimal path of the AGV by adopting a genetic algorithm.
And solving the minimum value by taking the constructed AGV scheduling model as an objective function, and solving a scheduling scheme of the AGV, namely the sequence of executing each scheduling task by the AGV.
In the embodiment of the application, the genetic algorithm is adopted to solve the objective function based on the good convergence rate of the genetic algorithm in processing the parallel problem.
In the design of genetic algorithm, the actual problem is required to be converted into the design solution of the genetic algorithm, and the genetic algorithm is adopted because the algorithm has better convergence rate in the aspects of scheduling of containers, vehicles and the like and processing parallel problems. In the application, the AGV dynamic scheduling problem is converted into chromosome codes, the fitness function is allocated, and then some kinds of crossing and mutation operations are carried out.
In the step of gene coding, the embodiment of the application adopts a real number coding mode, which is different from the traditional binary coding, the real number coding of the embodiment of the application is a series of natural number coding, and the serial number of the AGV corresponding to the execution scheduling task i is taken as a gene x i Is specified by the following formula: one chromosome string x 1 、x 2 、……、x n Denoted as x i I represents a scheduled task, represents a certain container task of the import and export job, and the value of the container task represents the AGV number corresponding to the execution scheduled task i, because the number of the scheduled task is known in advance and is arranged according to the sequence of the scheduled task operation time, when an AGV is determined to execute a certain scheduled task, the sequence of the scheduled task in the AGV operation is determined. Assuming that n takes 15, i.e. there are 15 scheduling tasks, let us consider once whether it is an inlet or an outlet, x i The values of (2) are shown in Table one:
list one
| x i | x 1 | x 2 | x 3 | x 4 | x 5 | x 6 | x 7 | x 8 | x 9 | x 10 | x 11 | x 12 | x 13 | x 14 | x 15 |
| Value of | 1 | 4 | 2 | 3 | 1 | 4 | 2 | 1 | 3 | 2 | 1 | 4 | 3 | 1 | 2 |
As can be seen from the above table, the AGV numbers corresponding to the 15 scheduling tasks, if x i =4 then represents the number of the AGV assigned by the scheduling task i as 4; obviously, the AGV scheduling tasks numbered 1,2,3,4 are shown in Table two below:
watch II
| AGV numbering | Route of operation |
| 1 | 1→5→8→11→14 |
| 2 | 3→7→10→15 |
| 3 | 4→9→13 |
| 4 | 2→6→12 |
In the steps of initial population determination and parameter selection, the method adopts a mode of randomizing the initial population, because only the evolution of the population can possibly traverse all states, and find the globally optimal solution. Random selection is not equivalent to blind selection, but rather an initial population is selected based on actual AGV scheduling, knowing the possible solutions.
In the embodiment of the application, the objective function is adopted as the criterion in the selection of the fitness function, and the fitness of the individuals is determined by the non-dominant level, so that each individual is said to correspond to the priority, rather than the determined fitness value.
In the crossing operation, a single-point crossing method is adopted, the purpose of adopting the method is not to cause larger variation of parent population, the superiority of parent individuals can be kept to a certain extent, namely, the superiority of parent individuals can be kept, namely, the constraint condition can be met more likely, analysis is carried out by taking the example given in the table I, only the first 8 tasks are taken as the parent A (14231421), a parent B (21313141) is randomly selected, the single-point crossing is carried out at the 5 th position point, genes after the fifth position point of the parent A, B are interchanged, and offspring individuals C, D are obtained, wherein the specific cases are as follows: after the fifth single-point cross transformation operation is carried out on the father A (14231421) and the father B (21313141), the obtained offspring C, D are respectively: the scheduling sequence of AGVs is changed through the operation of a crossover operator since the offspring C (14231141) and the offspring D (21313421), one or two gene loci are selected for changing the mutation operation, and the mutation of the genes is very effective in the model limited by constraint conditions.
In the selection of genetic operator probability, the crossover probability is generally between (0.4 and 0.9), the mutation probability is relatively low and is generally between (0.05 and 0.2), and the crossover probability is 0.8 and the mutation probability is 0.1.
In the GA algorithm, due to the randomness of operations such as crossover and mutation and the limitation of constraint conditions, individuals with the best fitness in the current population may be destroyed. This would instead reduce the average fitness value of the population and would have a detrimental effect on the efficiency of operation and convergence of the genetic algorithm, so that the best fitness individuals should be kept as far as possible in the next generation population. According to the coding mode, each scheduling task can be completed by only one AGV, and the coding also indicates the sequence of the scheduling tasks in the same AGV, so that the obtained solution meets the constraint condition in the model, and the constraint in the algorithm is not needed again. Individuals produce infeasible solutions during the computation because of violating constraint (2). Here, consider scaling the fitness, setting a penalty function that reduces the fitness of the individual and reduces the likelihood that the individual will be inherited into the next generation population, the fitness scaling with penalty function being as follows:
for the objective function F, a penalty function F' is set, and the penalty value is M, wherein M is a larger positive number, therebyAnd when the individual meets the constraint condition, taking the value F, and when the individual does not meet the constraint condition, taking the value F-M.
According to the experience of the traditional task scheduling, setting the expected value of the invalid operation time of the trolley under the task completion as t, and if the invalid time value in the algorithm and the error value of the expected value are within a certain acceptable range and are set to be within 5%, ending the genetic algorithm.
It should be noted that the above description is not intended to limit the application, but rather the application is not limited to the above examples, and that variations, modifications, additions or substitutions within the spirit and scope of the application will be within the scope of the application.
Claims (3)
1. An automated container terminal AGV intelligent scheduling method is characterized by comprising the following steps:
taking the shellfish position and the box region as nodes, taking the distance between the box region with the scheduling task relationship and the shellfish position as an edge, and constructing an AGV scheduling model:
；
taking the minimum value of F as an objective function, and solving an optimal path of the AGV by adopting a genetic algorithm;
wherein p is the total number of all scheduling tasks in the planned job period;the time interval from the completion of the scheduling task i to the start of the execution of the scheduling task j is provided for the AGV; />When the AGV executes the scheduling task i and then the scheduling task j, the value is 1, otherwise, the value is zero;
constraint conditions of the constructed AGV scheduling model are as follows:
，
，
，
，
,
,
wherein (1)>Start time for performing scheduled task i for AGV, < >>The running time of the scheduling task i is completed for the AGVs, and N is the total number of the AGVs required in each planned working period;
in the genetic algorithm, a penalty function is setTo reduce violations of constraintsIs the fitness of the individual;
wherein M is a positive integer, and is a value F when the individual meets the constraint condition and is a value F-M when the individual does not meet the constraint condition;
in the genetic coding step of the genetic algorithm, the serial number of the AGV corresponding to the execution scheduling task i is taken as a geneIs a value of (2);
the fitness function of the genetic algorithm adopts the objective function as a discrimination standard;
the crossing step of the genetic algorithm adopts single-point crossing; in the variation step of the genetic algorithm, single or two gene loci are selected for transformation;
and setting the expected value of the invalid operation time of the AGV as t, and ending the genetic algorithm when the error between the invalid operation time value in the genetic algorithm and the expected value is smaller than a set range.
2. The intelligent dispatching method for the automatic container terminal AGVs according to claim 1 wherein the genetic operator probability of the cross operation is 0.4-0.9 and the genetic operator probability of the mutation operation is 0.05-0.2.
3. The automated container terminal AGV intelligent scheduling method of claim 1, wherein the scheduling task is set as:
the AGVs are transported from the dock front to the yard or from the yard to the dock front.
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