CN109032150B - Genetic algorithm segmentation optimization-based dynamic scheduling method for rail-mounted automatic guided vehicle - Google Patents
Genetic algorithm segmentation optimization-based dynamic scheduling method for rail-mounted automatic guided vehicle Download PDFInfo
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Abstract
The invention discloses a dynamic scheduling method of a rail-mounted automatic guided vehicle based on genetic algorithm sectional optimization, which optimizes the stopping sequence of the rail-mounted automatic guided vehicle at each stopping position in sections according to the state parameters and preset processing parameters of each numerical control machine; and (3) encoding: encoding N future parking positions of the rail-mounted automatic guided vehicle into a gene string with the length of N; and (3) decoding: taking the ratio of the number of workpieces finished in the process of moving the rail-mounted automatic guided vehicle by N steps and the time spent as the fitness of the gene string; the genetic algorithm is operated by crossing, mutating and retaining elite gene strings, obtains the optimal gene strings through multi-round evolution and is used as a moving mode of the rail type automatic guided vehicle for N times in the future. The algorithm optimizes the moving track of the RGV for N times in the future when the CNC fails or recovers from the failure, so that the RGV scheduling scheme can be automatically adjusted according to the change of system parameters, and the CNC random failure and the failure recovery can be self-adapted.
Description
Technical Field
The disclosure relates to the technical field of industrial intelligent processing, in particular to a dynamic scheduling method of a rail type automatic guided vehicle based on genetic algorithm piecewise optimization.
Background
A typical intelligent processing system generally includes a guide vehicle, a plurality of numerically controlled machine tools and associated ancillary equipment, wherein one numerically controlled machine tool can only be used for mounting 1 tool to process 1 material at a time. If the processing course of material needs twice processes, then need to have different digit control machine tool installation different cutters to process respectively and accomplish. The guide vehicle can move and stop waiting on the linear track according to the instruction, and only one of the moving, stop waiting, loading and unloading and cleaning operations can be executed at the same time, and the operations all need a certain time.
For the intelligent processing system model, the moving steps of the RGV are optimized according to the parameter setting and the state of the CNC, including the track or the stop position N times in the future, and the working state of the CNC is not changed or controlled actively, so how to schedule the guide vehicle to ensure that the working efficiency of the whole system is the highest, namely, the maximum material processed in each shift (8 hours) is the main purpose of the intelligent processing system.
The inventor finds in research that the intelligent processing system can be regarded as a special job shop scheduling problem. For the scheduling problem of the job shop, the scheduling problem belongs to an NP-hard problem in the aspect of algorithm, a linear optimal solution cannot be found, and common solving methods of the scheduling problem comprise various group intelligent search algorithms represented by a heuristic algorithm, a greedy algorithm, a harmony search algorithm and a genetic algorithm.
However, these solving algorithms are not universal, and a specific algorithm can only obtain a better effective effect in a certain type of workshop scheduling problem. For a specific workshop operation scheduling problem, the above solving algorithm can only provide a rough solving framework, and a specific solving scheme must be designed according to the characteristics of the specific problem. Therefore, how to schedule the guide vehicle of the intelligent processing system is a main technical problem to be solved.
Disclosure of Invention
In order to solve the defects of the prior art, the method for dynamically scheduling the rail-mounted automatic guided vehicle based on genetic algorithm piecewise optimization can automatically adjust an RGV scheduling scheme according to system parameters, can self-adapt to CNC random faults and fault recovery, and improves the workpiece processing efficiency by optimizing the RGV moving track.
In order to achieve the purpose, the following technical scheme is adopted in the application:
a track type automatic guided vehicle dynamic scheduling method based on genetic algorithm piecewise optimization comprises the following steps:
optimizing the parking sequence of the rail type automatic guided vehicle at each parking position in a segmented mode according to the state parameters and preset processing parameters of each numerical control machine;
wherein, the coding: the method comprises the steps that the positions where the rail-mounted automatic guided vehicles can stop are represented by numbers, a genetic algorithm is used in a circulating mode, the stopping positions of the rail-mounted automatic guided vehicles for the next N times are optimized each time until the maximum working time is reached, and the stopping positions of the rail-mounted automatic guided vehicles for the next N times are coded into a gene string with the length of N;
and (3) decoding: the rail type automatic guided vehicle acquires state data of numerical control machine tools on two sides at a parking position, provides loading and unloading service, and takes the ratio of the number of workpieces finished in the process of moving the rail type automatic guided vehicle for N steps to the time spent as gene string fitness;
the genetic algorithm is operated by crossing, mutating and retaining elite gene strings, obtains the optimal gene strings through multi-round evolution and is used as a moving mode of the rail type automatic guided vehicle for N times in the future.
According to the further technical scheme, the state parameters and the preset processing parameters of each numerical control machine tool comprise: the maximum working time of the rail-mounted automatic guided vehicle, the continuous movement of the rail-mounted automatic guided vehicle and the time of setting a position, the rail-mounted automatic guided vehicle is the feeding and discharging time of the numerically-controlled machine tool with odd number marks, the rail-mounted automatic guided vehicle is the feeding and discharging time of the numerically-controlled machine tool with even number marks, and the rail-mounted automatic guided vehicle is used for washing the material.
According to the further technical scheme, before the genetic algorithm is executed each time, parameters including numerical control machine label numbers need to be clearly optimized, the current position of the rail type automatic guided vehicle is determined, the current position of the rail type automatic guided vehicle is 0 during first optimization, and the state of each numerical control machine is clearly determined.
According to a further technical scheme, when a gene string is coded, the gene string is constructed according to the length of the specified gene string and the coding scheme, and an initial population with the specified scale is produced;
the gene string is constructed by taking the rail type automatic guide vehicle position as a basic element, and the coding scheme is as follows:
step 2-1: adding the current position of the rail type automatic guided vehicle as a first number into a gene string;
step 2-2: randomly generating an integer representing a position, wherein the integer is more than or equal to 0 and less than or equal to the maximum value of the position of the rail type automatic guided vehicle, is different from the last position in the gene string, and adding the number into the gene string;
step 2-3: if the requirement of the length of the gene string is met, the coding of the gene string is finished, otherwise, the step 2-2 is carried out;
step 2-4: and calculating the fitness of the gene string.
And repeating the random construction of the gene string until the requirement of quantity scale is met.
According to a further technical scheme, the method for calculating the fitness of the gene string comprises the following steps:
step 3-1: taking the number of the first representative position from the gene string, recording the number as currPosi, recording the system time of the rail type automatic guided vehicle at the position as the starting time, recording the system time as start _ time, and acquiring the processing state information of each numerical control machine;
step 3-2: the rail type automatic guided vehicle operates at the first representative position of the gene string;
step 3-3: if the earliest operable time next _ operator _ time for predicting that the rail-mounted automatic guided vehicle directly moves to the next position next _ posi is the last digit in the gene string, no operation is executed, the position is used as the initial position of the rail-mounted automatic guided vehicle when the seed group is initialized next time, the step is switched to the step 3-4, and if the position is not the last digit, the step is switched to the step 3-2;
step 3-4: the fitness calculation method comprises the following steps: the fixed is the finished material quantity/(current system time-start _ time) during the movement of the automatic guided vehicle planned by the basic gene string.
According to a further technical scheme, the step 3-2: the operation of the rail type automatic guided vehicle on the first representative position of the gene string comprises the following steps:
(1) select a rail mounted automatic guided vehicle in two digit control machine tools that correspond from the current position can carry out last unloading operation at earliest, if do not have the trouble, operate immediately, rail mounted automatic guided vehicle is corresponding digit control machine tool execution operation respectively:
(1.1) if the current unprocessed materials of the corresponding numerical control machine tool correspond to the unprocessed materials of the numerical control machine tool, according to the working procedures that the numerical control machine tool can process, the time spent by the rail type automatic guided vehicle is taken as the materials on the rail type automatic guided vehicle, the time spent by the rail type automatic guided vehicle is updated, and if no materials can be fed, the rail type automatic guided vehicle is idle;
(1.2) if the corresponding numerical control machine tool is in a machining finish state at present, the rail type automatic guided vehicle executes feeding and discharging operation for the corresponding numerical control machine tool, and the time of the rail type automatic guided vehicle is updated:
if the numerical control machine tool can only process the first procedure, the type and the position of the material are required to be recorded after blanking, and new material is loaded;
if the numerical control machine tool processes the second procedure, the quantity of finished materials is required to be recorded after blanking, a material to be processed in the second procedure is selected for loading, if the material is not processed, loading is not required, and then the rail type automatic guide vehicle cleans the finished materials.
(2) Calculating the earliest operable time of the two numerical control machines as operator _ time and predicting the earliest operable time of the rail type automatic guided vehicle directly moving to the next position next _ posi as next _ operator _ time;
if operator _ time < ═ next _ operator _ time, the tracked automated guided vehicle waits at the current location until it can operate, otherwise moves to the next location.
According to the further technical scheme, after the initialized population is obtained, part of gene strings are randomly selected from the population to perform crossing and mutation operations, and the crossing operation method comprises the following steps:
step 4-1: randomly selecting two gene strings according to the fitness;
step 4-2: randomly determining a cross starting position posi and a cross length len, wherein posi + len cannot exceed the total length of the gene string;
step 4-3: swapping the content from posi to posi + len in the gene string 1 with the content from posi to posi + len in the gene string 2;
step 4-4: and (4) checking the validity, namely checking whether the gene string 1 and the gene string 2 conform to the gene string coding specification, if not, recovering the state before the two gene strings are crossed, and turning to the step 4-2.
The further technical scheme is that the mutation operation method comprises the following steps:
step 5-1: randomly selecting a gene string according to the fitness;
step 5-2: randomly generating n variable positions, recording the position number of the rail-mounted automatic guided vehicle as p for each variable position, and executing the step 5-3:
step 5-3: randomly generating mutation data posi belonging to {0,1,2,3} and p ≠ posi, replacing p with posi at the corresponding position in the gene string, if the validity check of the gene string is passed, going to step 5-2, otherwise, executing step 5-3.
According to the further technical scheme, the judgment basis of the evolution ending is that the evolution iteration times are more than or equal to the preset iteration times.
In a further technical scheme, the method for selecting the optimal gene string is to select a gene string with the maximum fitness value in the evolved population.
According to a further technical scheme, the optimal gene string decoding is a process that the rail type automatic guided vehicle moves according to the gene string planning position and provides service for a numerical control machine, and the method comprises the following steps:
step 6-1: taking the first digit representing the position from the gene string and recording as curPosi;
step 6-2: the rail type automatic guided vehicle operates at a curPosi position;
step 6-3: if the earliest operable time next _ operator _ time for predicting the rail-mounted automatic guided vehicle to move to the next position next _ posi is the last digit in the gene string, no operation is executed, the position is used as the initial position of the rail-mounted automatic guided vehicle when the species group is initialized next time, the step 6-4 is carried out, and if the position is not the last digit, the step 6-2 is carried out;
step 6-4: and finishing decoding.
The further technical scheme is as follows, step 6-2: operation of the rail-mounted automated guided vehicle in the curPosi position:
(1) selecting one rail type automatic guided vehicle from two numerical control machines corresponding to the current position, wherein the rail type automatic guided vehicle can execute the loading and unloading operation at the earliest time, the operable time is recorded as operator _ time, if the operator _ time is greater than the shift time T _ ALL, the decoding is finished, and the step is transferred to the step 6-4;
if no fault exists, the operation is carried out immediately, and the rail type automatic guide vehicle respectively executes the operation for the corresponding numerical control machine tool:
(1.1) if the current unprocessed materials of the corresponding numerical control machine tool correspond to the unprocessed materials of the numerical control machine tool, the time spent by the rail type automatic guided vehicle is taken as the materials on the rail type automatic guided vehicle according to the working procedures which can be processed by the numerical control machine tool, and the time of the rail type automatic guided vehicle is updated;
(1.2) if the corresponding numerical control machine tool is in a machining finished state at present, the rail type automatic guide vehicle executes feeding and discharging operation for the numerical control machine tool, and the RGV time is updated:
if the numerical control machine tool is a first processing procedure, the type and the position of the material are required to be recorded after blanking, and new material is loaded;
if the numerical control machine tool is used for the second working procedure, a material to be processed in the second working procedure is selected for feeding after blanking. Then, the rail type automatic guided vehicle cleans the finished material;
(2) updating the earliest operable time operator _ time of the two numerically controlled machine tools and predicting the earliest operable time next _ operator _ time when the rail-mounted automatic guided vehicle moves to the next position next _ posi;
if operator _ time < ═ next _ operator _ time, the tracked automated guided vehicle waits at the current location until it can operate, otherwise it takes time to move to the next location.
According to the further technical scheme, if the current time of the rail-mounted automatic guided vehicle is greater than the time T _ ALL of the shift, the operation is ended; if the rail-mounted automatic guided vehicle encounters random faults or fault recovery of the numerical control machine tool in the planning and estimating movement process, the rail-mounted automatic guided vehicle stops moving, and the future movement track of the rail-mounted automatic guided vehicle is optimized by taking the current position of the rail-mounted automatic guided vehicle, the system time and the state information of each numerical control machine tool as initial data.
A second object of the present application is to disclose a dynamic dispatching system of a rail-mounted automatic guided vehicle based on genetic algorithm segment optimization,
the system optimizes the parking sequence of the rail type automatic guided vehicle at each parking position in a segmentation way according to the state parameters of each numerical control machine and preset processing parameters;
the method comprises an encoding unit: the method comprises the steps that the positions where the rail-mounted automatic guided vehicles can stop are represented by numbers, a genetic algorithm is used in a circulating mode, the stopping positions of the rail-mounted automatic guided vehicles for the next N times are optimized each time until the maximum working time is reached, and the stopping positions of the rail-mounted automatic guided vehicles for the next N times are coded into a gene string with the length of N;
a decoding unit: the rail type automatic guided vehicle acquires state data of numerical control machine tools on two sides at a parking position, provides loading and unloading service, and takes the ratio of the number of workpieces finished in the process of moving the rail type automatic guided vehicle for N steps to the time spent as gene string fitness;
the genetic algorithm is operated by crossing, mutating and retaining elite gene strings, obtains the optimal gene strings through multi-round evolution and is used as a moving mode of the rail type automatic guided vehicle for N times in the future.
Compared with the prior art, the beneficial effect of this disclosure is:
the method comprises the steps of coding the RGV motion track in a segmented mode to generate a gene string, designing a decoding scheme according to the processing flow and the rule of an intelligent processing system, designing the fitness by integrating the processing time and the quantity of finished materials, and adopting a segmented optimization strategy for reducing the optimization time due to the fact that the system is long in continuous processing time, the RGV motion track is long, and the number of stopping positions is large. The algorithm optimizes the moving track of the RGV for N times in the future when the CNC fails or recovers from the failure, so that the RGV scheduling scheme can be automatically adjusted according to the change of system parameters, and the CNC random failure and the failure recovery can be self-adapted.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 RGV-CNC shop layout;
FIG. 2 is an abstract schematic of an intelligent processing system;
FIG. 3 is a flowchart of an algorithm according to some embodiments of the present application.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In a typical embodiment, the intelligent processing system is composed of 8 Computer Numerical Control (CNC) machines, 1 Rail Guide Vehicle (RGV) with a manipulator and a cleaning tank, 1RGV linear Rail, 1 feeding conveyor and 1 discharging conveyor, and the layout of the plant is shown in fig. 1.
With respect to (1) CNC: 4 CNC are respectively installed on the two sides of the feeding conveying belt and the discharging conveying belt and are arranged at equal intervals, and each CNC can only install 1 type of tool to process 1 material at the same time. If the processing course of material needs twice processes, then need to have different CNC to install different cutters and process respectively and accomplish, can not change the cutter in the processing course. The first and second processes need to be processed and completed in sequence on different CNC machines, the completion time is different, and each CNC machine can only complete one of the processes.
(2) RGV: the RGV has an intelligent control function and can receive and send command signals. The device can move and stop waiting on a linear track according to instructions, and can continuously move by 1 unit (distance between two adjacent CNC machines), 2 units (distance between three adjacent CNC machines) and 3 units (distance between four adjacent CNC machines). The RGV can perform only one of moving, stop waiting, loading and unloading, and cleaning operations at the same time, which require a certain time.
(3) Material loading conveyer belt: the feeding conveyor belt consists of 4 sections, and 1 section is arranged before each of the odd-numbered CNC1#, 3#, 5#, and 7 #. The system sensor is used for controlling the transmission, and the transmission can be carried out only in one direction, and not only can be linked, but also can be independently moved.
(4) Blanking and conveying belt: the blanking conveyor belt consists of 4 sections, and 1 section is arranged before each of even-numbered CNC2#, 4#, 6#, and 8 #. The sensor is used for controlling the transmission, and the transmission can only be carried out in the same direction, and not only can be linked, but also can be independently moved.
The operation flow of the intelligent processing system is as follows:
(1) after the intelligent processing system is powered on and started, the RGV is at the initial position between CNC1# and CNC2#, and all CNC are in an idle state.
(2) Under the normal working condition, if a CNC is in an idle state, a feeding demand signal is sent to the RGV; otherwise, the CNC is in a machining operation state and immediately sends a demand signal to the RGV when the machining operation is finished.
(3) After receiving the demand signal of a certain CNC, the RGV can automatically determine the loading and unloading operation sequence of the CNC and sequentially perform loading and unloading operation on the CNC. According to the demand instruction, the RGV is operated to a certain CNC position needing operation, and simultaneously the feeding conveyor belt sends raw materials to the right front of the CNC for the RGV feeding operation.
The time required for one feeding and discharging of the RGV for even-numbered CNC is larger than that for one feeding and discharging of the odd-numbered CNC.
(4) After the RGV is a CNC to complete one-time loading and unloading operation, the mechanical arm is rotated to move clinker on the mechanical arm to the upper part of the cleaning groove, and cleaning operation is carried out (only the processed clinker is cleaned).
(5) After finishing one job task, the RGV immediately judges and executes the next job instruction. At this point, if no other job instruction is received, the RGV waits in place until the next job instruction. And after finishing a processing task of a material, a CNC immediately sends a demand signal to the RGV. The CNC waits if the RGV fails to reach immediately for blanking on.
(6) The CNC possibly breaks down in the machining process, the time for fault removal (manual treatment and incomplete material scrapping) is 10-20 minutes each time, and an operation sequence is added immediately after the fault removal;
(7) the system repeats (3) to (5) repeatedly until the system stops working and the RGV returns to the initial position.
Abstract modeling is performed aiming at the problems:
fig. 2 is an abstract view of an intelligent processing system, which is composed of 8 CNC machines, 1RGV linear track, 1 feeding conveyor belt, 1 discharging conveyor belt and other accessories. The RGV can freely run on a fixed track, can automatically control the moving direction and distance according to instructions, is provided with a mechanical arm, two mechanical claws and a material cleaning tank, and can complete the tasks of loading, unloading, material cleaning and the like. The RGV has 4 stop positions from left, labeled 0,1,2,3, respectively.
The intelligent processing system processes materials containing two procedures, the first procedure and the second procedure of each material are processed by two different CNC at one time, the CNC possibly breaks down in the processing process, the time of fault removal (manual processing and incomplete material scrapping) each time is between 10-20 minutes, and an operation sequence is added immediately after the fault removal. The RGV movement requires different times for continuous movement of one position, two positions and 3 positions; when different materials are processed, the RGV is the CNC feeding and discharging time of different sides and the cleaning time is also different.
In a typical implementation example in the application, in order to optimize the problem of intelligent RGV dynamic scheduling and improve the material processing efficiency of an intelligent processing system, an intelligent RGV dynamic scheduling strategy algorithm based on a genetic algorithm is provided for a processing system, a frame of a basic genetic algorithm is adopted, an RGV motion track is encoded to generate a gene string, a decoding scheme is designed according to the processing flow and the rule of the intelligent processing system, the fitness is designed according to the comprehensive processing time and the quantity of finished materials, and a sectional optimization strategy is adopted for reducing the optimization time in view of longer continuous processing time of the system. The algorithm can automatically adjust the RGV scheduling scheme according to system parameters, and can be self-adaptive to CNC random faults and fault recovery.
The overall concept of the present embodiment is: according to each CNC state parameter and the preset processing parameter, the workpiece processing efficiency is improved by optimizing the stopping sequence (namely the moving track or the scheduling sequence) of the RGV at each stopping position.
The number 0-3 indicates the location where the RGV can be parked, and the genetic algorithm is used cyclically to optimize the parking location of the RGV N times in the future each time until the maximum working time (shift time) is reached; encoding the N future docking positions of the RGV into a gene string with the length of N; the basic decoding method comprises the steps that the RGV obtains CNC state data on two sides at a parking position, loading and unloading service is provided according to a processing flow, rules and preset parameters, and the ratio of the number of workpieces completed in the process that the RGV moves for N steps to the time spent by the RGV is taken as the fitness of a gene string; the genetic algorithm obtains the optimal gene string through multiple rounds of evolutions through operations such as crossing, mutation, retention of elite gene strings and the like, and the optimal gene string is used as a moving mode of the RGV for N times in the future. During the machining process, the CNC fails or recovers from the failure, which triggers the genetic algorithm to re-plan the parking positions N times in the future.
In a specific implementation example, a flow chart of the algorithm is shown in fig. 3. The scheduling strategy algorithm comprises the following main steps:
the first step is as follows: the time of shift and the values of other parameters are set.
The second step is that: and executing a genetic algorithm to optimize the RGV scheduling track in a segmented mode.
The third step: and initializing the population.
The fourth step: and performing crossover and mutation operations.
The fifth step: fitness of all gene strings was calculated.
And a sixth step: and if the evolution is finished, executing the seventh step, otherwise, turning to the fourth step.
The seventh step: and selecting an optimal gene string parallel decoding program RGV scheduling track, and scheduling the RGV.
Eighth step: if the scheduling track is executed and the shift time is not reached or the CNC parameter change (CNC random fault or fault recovery) is met, the second step is carried out, and other conditions are carried out to the ninth step.
The ninth step: and (6) ending.
Wherein, in a first step: the parameters to be set specifically include the shift time is denoted as T _ ALL, the times when the RGV continuously moves to one position, two positions and three positions are denoted as T _ RGV _ move1, T _ RGV _ move2 and T _ RGV _ move3, the CNC time for feeding and discharging with the RGV being odd-numbered is denoted as T _ O _ sl, the CNC feeding and discharging time for the even-numbered is denoted as T _ E _ sl, and the RGV washing time is denoted as T _ xl.
In a second step: and executing a genetic algorithm to optimize the RGV scheduling track in a segmented mode. During a shift of the system, the genetic algorithm may be executed multiple times to optimize the RGV dispatch trajectory in stages. Before executing the genetic algorithm, it is necessary to determine the optimized parameters including CNC labels, which are marked as a set CNClist, where a subscript i corresponds to the CNC labeled as i +1, for example, the CNC labeled as 3 in CNClist [2], it is necessary to determine the current position currposi of the RGV, the currposi is 0 during the first optimization, and it is determined whether each CNC state is faulty or not, and the current processing material information.
In the third step: and constructing the gene strings according to the specified length of the gene strings and the coding scheme, and producing the initial population of the specified scale.
In the algorithm, RGV positions (4 positions from left to right are respectively represented by numbers 0,1,2 and 3) are used as basic elements to construct a gene string, and the coding scheme is as follows:
step 1: the current position of RGV is the first number to be added into the gene string.
Step 2: randomly generating an integer representing a position, wherein the integer is greater than or equal to 0 and less than or equal to 3 and is different from the last position in the gene string. This number is added to the gene string.
Step 3: if the requirement of the gene string length is met, the gene string coding is completed, otherwise, the Step2 is carried out.
Step 4: and calculating the fitness of the gene string.
And repeating the random construction of the gene string until the requirement of quantity scale is met.
In the third step, the fitness calculation method of the gene string comprises the following steps:
step 1: and (4) taking the first number representing the position from the gene string, recording the number as currPosi, recording the system time when the RGV is at the position, taking the system time as the starting time, recording the system time as start _ time, and acquiring the processing state information of each CNC.
Step 2: operation of RGV at the curPosi position:
(1) selecting one RGV from two CNC corresponding to the current position can execute the loading and unloading operation at the earliest time if not
When there is a fault, it can operate immediately, and the RGVs perform the operations for the corresponding CNC respectively:
(1.1) if there is no material available, the RGV is idle according to the procedure that the CNC can process.
(1.2) if the corresponding CNC is in a machining finished state currently, the RGV executes loading and unloading operation for the CNC, and the RGV time is updated:
if the CNC can only process the first procedure, the type and the position of the material need to be recorded after blanking, and new material is loaded.
If the CNC processes the second procedure, the quantity of finished materials is required to be recorded after blanking, and a material to be processed in the second procedure is selected for loading, and if the CNC does not process the material instead, the material does not need to be loaded. The RGV then washes the finished material.
(2) Calculating the earliest operable time of the two CNC as operator _ time and predicting the earliest operable time next _ operator _ time when the RGV moves to next position next _ posi directly; if operator _ time < ═ next _ operator _ time, the RGV waits at the current location until it can operate, otherwise moves to the next location.
Step 3: if the next _ operator _ time is the last digit in the gene string, no action is performed and this position is taken as the initial position of the RGV at the next time the seed group is initialized, leading to Step 4. If not, go to Step2.
Step 4: the fitness calculation method comprises the following steps: the fixed ═ number of finished materials/(current system time-start _ time) during RGV movement planned by the present gene string;
in the fourth step, randomly selecting partial gene strings in the population to perform crossing and mutation operations.
The cross operation method comprises the following steps:
step 1: two gene strings were randomly selected according to fitness.
Step 2: crossover start position posi and crossover length len were randomly determined, where posi + len could not exceed the total length of the gene string.
Step 3: the contents of the part from posi to posi + len in gene string 1 and the contents from posi to posi + len in gene string 2 were swapped.
Step 4: and (5) checking the validity. Check if gene string 1 and gene string 2 meet the gene string coding specification. If the specification is not met, the state before the intersection of the two gene strings is restored, and the Step2 is reached.
The mutation operation method comprises the following steps:
step 1: a gene string is randomly selected according to fitness.
Step 2: randomly generating n variation positions, recording the RGV position number as p for each variation position, and executing Step 3:
step 3: randomly generating mutation data posi belonging to {0,1,2,3} and p ≠ posi, replacing p with posi at the corresponding position in the gene string, if the validity check of the gene string is passed, turning to Step2, otherwise executing Step 3;
in the fifth step, the gene cluster fitness calculation method is the same as that in the third step.
In the sixth step, the judgment basis of the evolution end is that the evolution iteration number is more than or equal to the preset iteration number.
In the seventh step, the method for selecting the optimal gene string is to select a gene string with the maximum fitness value in the evolved population.
In the seventh step, the optimal gene string decoding is the process of moving the RGV according to the gene string planning position and providing services for CNC. The method comprises the following steps:
step 1: the first number representing the position from the gene string is taken and is denoted as curPosi.
Step 2: operation of RGV at the curPosi position:
(1) selecting one RGV from two CNC corresponding to the current position to execute the loading and unloading operation earliest, and recording the operable time as operator _ time, if the operator _ time > T _ ALL (shift time), ending the decoding, and going to Step 4.
If there is no failure, it can operate immediately, and the RGVs perform operations for the corresponding CNC, respectively:
(1.1) if it corresponds to the CNC current unprocessed material, the RGV spends time as the material thereon according to the procedures that the CNC can process, and the RGV time is updated.
(1.2) if the corresponding CNC is in a machining finished state currently, the RGV executes loading and unloading operation for the CNC, and the RGV time is updated:
if the CNC is the first processing procedure, the type and the position of the material need to be recorded after blanking, and new material is loaded.
And if the CNC is the second processing procedure, feeding the material to be processed in the second processing procedure after blanking. The RGV then washes the finished material.
(2) Updating the earliest operable time, op _ time, of the two CNC's and the earliest operable time, next _ op _ time, of the predicted RGV to move to the next position, next _ posi;
if operator _ time < ═ next _ operator _ time, the RGV waits at the current location until it can operate, otherwise it takes time to move to the next location.
Step 3: if the next _ operator _ time is the last digit in the gene string, no action is performed and this position is taken as the initial position of the RGV at the next time the seed group is initialized, leading to Step 4. If not, go to Step2.
Step 4: and finishing decoding.
In the eighth step, if the current time of the RGV is greater than the time T _ ALL of the shift, the process is ended, otherwise, the process needs to be transferred to the second step; if a CNC random fault or fault recovery is met in the process of estimating movement of the RGV according to the plan, the RGV stops moving, and the second step is carried out, wherein the current RGV position, the system time and each piece of CNC state information are used as initial data, and the future movement track of the RGV is optimized.
The system optimizes the parking sequence of the rail-mounted automatic guided vehicle at each parking position in sections according to the state parameters of each numerical control machine and the preset processing parameters;
the method comprises an encoding unit: the method comprises the steps that the positions where the rail-mounted automatic guided vehicles can stop are represented by numbers, a genetic algorithm is used in a circulating mode, the stopping positions of the rail-mounted automatic guided vehicles for the next N times are optimized each time until the maximum working time is reached, and the stopping positions of the rail-mounted automatic guided vehicles for the next N times are coded into a gene string with the length of N;
a decoding unit: the rail type automatic guided vehicle acquires state data of numerical control machine tools on two sides at a parking position, provides loading and unloading service, and takes the ratio of the number of workpieces finished in the process of moving the rail type automatic guided vehicle for N steps to the time spent as gene string fitness;
the genetic algorithm is operated by crossing, mutating and retaining elite gene strings, obtains the optimal gene strings through multi-round evolution and is used as a moving mode of the rail type automatic guided vehicle for N times in the future.
In order to prove the effect of the technical scheme of the application, data verification is performed as follows:
the intelligent RGV dynamic scheduling policy algorithm proposed herein was validated with the following data:
after the optimization of the algorithm, the CNC can process 242 materials in total within one shift time, and the RGV moving track is
0121010121012101012012101021__0323012321012320123101230123__0123201230123210212301230123__0123031320132013201320132012__3210132012320102321012320132__1012321013201232101232101232__1021232101232101320132013201__3210123210132101321012321012__3201023201320232101232103210__1230102321023101232102321023__1023102310232102321023210123__1023102310123102321023202101
And (3) fault information:
the processing details are as follows:
through the specific example, the algorithm can automatically adjust the RGV scheduling scheme according to the system parameters, and can be adaptive to CNC random faults and fault recovery.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (8)
1. The track type automatic guided vehicle dynamic scheduling method based on genetic algorithm piecewise optimization is characterized by comprising the following steps:
optimizing the parking sequence of the rail type automatic guided vehicle at each parking position in a segmented mode according to the state parameters and preset processing parameters of each numerical control machine;
wherein, the coding: the method comprises the steps that the positions where the rail-mounted automatic guided vehicles can stop are represented by numbers, a genetic algorithm is used in a circulating mode, the stopping positions of the rail-mounted automatic guided vehicles for the next N times are optimized each time until the maximum working time is reached, and the stopping positions of the rail-mounted automatic guided vehicles for the next N times are coded into a gene string with the length of N;
when the gene string is coded, the gene string is constructed according to the length of the specified gene string and the coding scheme, and an initial population with the specified scale is produced;
the gene string is constructed by taking the rail type automatic guide vehicle position as a basic element, and the coding scheme is as follows:
step 2-1: adding the current position of the rail type automatic guided vehicle as a first number into a gene string;
step 2-2: randomly generating an integer representing a position, wherein the integer is more than or equal to 0 and less than or equal to the maximum value of the position of the rail type automatic guided vehicle, is different from the last position in the gene string, and adding the number into the gene string;
step 2-3: if the requirement of the length of the gene string is met, the coding of the gene string is finished, otherwise, the step 2-2 is carried out;
step 2-4: calculating the fitness of the gene string;
repeating the random construction of the gene strings until the requirement of quantity and scale is met;
and (3) decoding: the rail type automatic guided vehicle acquires state data of numerical control machine tools on two sides at a parking position, provides loading and unloading service, and takes the ratio of the number of workpieces finished in the process of moving the rail type automatic guided vehicle for N steps to the time spent as gene string fitness;
the genetic algorithm is operated by crossing, mutating and retaining elite gene strings, obtains the optimal gene strings through multi-round evolution and is used as a moving mode of the rail type automatic guided vehicle for N times in the future.
2. The method for dynamically scheduling the rail-mounted automatic guided vehicle based on the genetic algorithm piecewise optimization as claimed in claim 1, wherein the fitness calculation method of the gene string comprises the following steps:
step 3-1: taking the number of the first representative position from the gene string, recording the number as currPosi, recording the system time of the rail type automatic guided vehicle at the position as the starting time, recording the system time as start _ time, and acquiring the processing state information of each numerical control machine;
step 3-2: the rail type automatic guided vehicle operates at the first representative position of the gene string;
step 3-3: if the earliest operable time next _ operator _ time for predicting that the rail-mounted automatic guided vehicle directly moves to the next position next _ posi is the last digit in the gene string, no operation is executed, the position is used as the initial position of the rail-mounted automatic guided vehicle when the seed group is initialized next time, the step is switched to the step 3-4, and if the position is not the last digit, the step is switched to the step 3-2;
step 3-4: the fitness calculation method comprises the following steps: the fixed is the finished material quantity/(current system time-start _ time) during the movement of the automatic guided vehicle planned by the basic gene string.
3. The method for dynamically scheduling the rail-mounted automatic guided vehicle based on the genetic algorithm piecewise optimization as claimed in claim 2, wherein the step 3-2: the operation of the rail type automatic guided vehicle on the first representative position of the gene string comprises the following steps:
(1) select a rail mounted automatic guided vehicle in two digit control machine tools that correspond from the current position can carry out last unloading operation at earliest, if do not have the trouble, operate immediately, rail mounted automatic guided vehicle is corresponding digit control machine tool execution operation respectively:
(1.1) if the current unprocessed materials of the corresponding numerical control machine tool correspond to the unprocessed materials of the numerical control machine tool, according to the working procedures that the numerical control machine tool can process, the time spent by the rail type automatic guided vehicle is taken as the materials on the rail type automatic guided vehicle, the time spent by the rail type automatic guided vehicle is updated, and if no materials can be fed, the rail type automatic guided vehicle is idle;
(1.2) if the corresponding numerical control machine tool is in a machining finish state at present, the rail type automatic guided vehicle executes feeding and discharging operation for the corresponding numerical control machine tool, and the time of the rail type automatic guided vehicle is updated:
if the numerical control machine tool can only process the first procedure, the type and the position of the material are required to be recorded after blanking, and new material is loaded;
if the numerical control machine tool is used for processing a second working procedure, the number of finished materials is required to be recorded after blanking, a material to be processed in the second working procedure is selected for loading, if no material is required to be processed, loading is not required, and then the rail type automatic guide vehicle is used for cleaning the finished materials;
(2) calculating the earliest operable time of the two numerical control machines as operator _ time and predicting the earliest operable time of the rail type automatic guided vehicle directly moving to the next position next _ posi as next _ operator _ time;
if operator _ time < ═ next _ operator _ time, the tracked automated guided vehicle waits at the current location until it can operate, otherwise moves to the next location.
4. The method for dynamically scheduling the rail-mounted automatic guided vehicle based on the genetic algorithm segmental optimization according to claim 1, wherein after an initialized population is obtained, a part of gene strings in the population are randomly selected to perform crossover and mutation operations, and the crossover operation method comprises the following steps:
step 4-1: randomly selecting two gene strings according to the fitness;
step 4-2: randomly determining a cross starting position posi and a cross length len, wherein posi + len cannot exceed the total length of the gene string;
step 4-3: swapping the content from posi to posi + len in the gene string 1 with the content from posi to posi + len in the gene string 2;
step 4-4: and (4) checking the validity, namely checking whether the gene string 1 and the gene string 2 conform to the gene string coding specification, if not, recovering the state before the two gene strings are crossed, and turning to the step 4-2.
5. The method for dynamically scheduling track type automatic guided vehicles based on genetic algorithm piecewise optimization according to claim 1, wherein the method for selecting the optimal gene string is to select a gene string with the maximum fitness value in the evolved population.
6. The method for dynamically scheduling the rail-mounted automatic guided vehicle based on the genetic algorithm segmental optimization as claimed in claim 5, wherein the optimal gene string decoding is a process of moving the rail-mounted automatic guided vehicle according to the gene string planning position and providing service for a numerical control machine, and the method comprises the following steps:
step 6-1: taking the first digit representing the position from the gene string and recording as curPosi;
step 6-2: the rail type automatic guided vehicle operates at a curPosi position;
step 6-3: if the earliest operable time next _ operator _ time for predicting the rail-mounted automatic guided vehicle to move to the next position next _ posi is the last digit in the gene string, no operation is executed, the position is used as the initial position of the rail-mounted automatic guided vehicle when the species group is initialized next time, the step 6-4 is carried out, and if the position is not the last digit, the step 6-2 is carried out;
step 6-4: and finishing decoding.
7. The method for dynamically scheduling tracked automatic guided vehicles based on genetic algorithm segment optimization as claimed in claim 1, wherein if the current time of the tracked automatic guided vehicle is greater than the shift time T _ ALL, the method is ended; if the rail-mounted automatic guided vehicle encounters random faults or fault recovery of the numerical control machine tool in the planning and estimating movement process, the rail-mounted automatic guided vehicle stops moving, and the future movement track of the rail-mounted automatic guided vehicle is optimized by taking the current position of the rail-mounted automatic guided vehicle, the system time and the state information of each numerical control machine tool as initial data.
8. The rail type automatic guided vehicle dynamic scheduling system based on genetic algorithm sectional optimization is characterized in that the system optimizes the stopping sequence of the rail type automatic guided vehicle at each stopping position in sections according to the state parameters of each numerical control machine and preset processing parameters;
the method comprises an encoding unit: the method comprises the steps that the positions where the rail-mounted automatic guided vehicles can stop are represented by numbers, a genetic algorithm is used in a circulating mode, the stopping positions of the rail-mounted automatic guided vehicles for the next N times are optimized each time until the maximum working time is reached, and the stopping positions of the rail-mounted automatic guided vehicles for the next N times are coded into a gene string with the length of N;
when the gene string is coded, the gene string is constructed according to the length of the specified gene string and the coding scheme, and an initial population with the specified scale is produced;
the gene string is constructed by taking the rail type automatic guide vehicle position as a basic element, and the coding scheme is as follows:
step 2-1: adding the current position of the rail type automatic guided vehicle as a first number into a gene string;
step 2-2: randomly generating an integer representing a position, wherein the integer is more than or equal to 0 and less than or equal to the maximum value of the position of the rail type automatic guided vehicle, is different from the last position in the gene string, and adding the number into the gene string;
step 2-3: if the requirement of the length of the gene string is met, the coding of the gene string is finished, otherwise, the step 2-2 is carried out;
step 2-4: calculating the fitness of the gene string;
repeating the random construction of the gene strings until the requirement of quantity and scale is met;
a decoding unit: the rail type automatic guided vehicle acquires state data of numerical control machine tools on two sides at a parking position, provides loading and unloading service, and takes the ratio of the number of workpieces finished in the process of moving the rail type automatic guided vehicle for N steps to the time spent as gene string fitness;
the genetic algorithm is operated by crossing, mutating and retaining elite gene strings, obtains the optimal gene strings through multi-round evolution and is used as a moving mode of the rail type automatic guided vehicle for N times in the future.
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| CN110084462B (en) * | 2019-01-25 | 2023-05-05 | 安庆师范大学 | Intelligent two-process machining scheduling method based on fault RGV |
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| CN110134092A (en) * | 2019-05-28 | 2019-08-16 | 山东师范大学 | Rail mounted automatic guide vehicle dynamic dispatching method and system based on harmony search |
| CN112051806B (en) * | 2019-09-10 | 2022-01-28 | 南京邮电大学 | RGV intelligent scheduling method in reciprocating type track under random fault |
| CN110837950B (en) * | 2019-10-22 | 2022-07-19 | 山东女子学院 | Dynamic scheduling method of RGV (traffic volume group) trolley |
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