CN114781746A - Multi-vehicle-taking task optimization method based on multilayer AGV parking garage - Google Patents
Multi-vehicle-taking task optimization method based on multilayer AGV parking garage Download PDFInfo
- Publication number
- CN114781746A CN114781746A CN202210499104.6A CN202210499104A CN114781746A CN 114781746 A CN114781746 A CN 114781746A CN 202210499104 A CN202210499104 A CN 202210499104A CN 114781746 A CN114781746 A CN 114781746A
- Authority
- CN
- China
- Prior art keywords
- agv
- parking
- gene
- taking
- parking garage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000005457 optimization Methods 0.000 title claims abstract description 8
- 230000002068 genetic effect Effects 0.000 claims abstract description 12
- 108090000623 proteins and genes Proteins 0.000 claims description 45
- 230000035772 mutation Effects 0.000 claims description 27
- 239000011159 matrix material Substances 0.000 claims description 25
- 210000000349 chromosome Anatomy 0.000 claims description 17
- 230000037431 insertion Effects 0.000 claims description 17
- 238000003780 insertion Methods 0.000 claims description 17
- 238000013507 mapping Methods 0.000 claims description 8
- 230000001133 acceleration Effects 0.000 claims description 5
- 230000002860 competitive effect Effects 0.000 claims description 3
- 230000009191 jumping Effects 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 101150076211 TH gene Proteins 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 101150084750 1 gene Proteins 0.000 description 1
- YCKRFDGAMUMZLT-UHFFFAOYSA-N Fluorine atom Chemical compound [F] YCKRFDGAMUMZLT-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 229910052731 fluorine Inorganic materials 0.000 description 1
- 239000011737 fluorine Substances 0.000 description 1
- 230000036961 partial effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
Abstract
The invention relates to a multi-vehicle-taking task optimization method based on a multilayer AGV parking garage, and belongs to the field of intelligent parking. The method specifically comprises the following steps: 1) according to the parking and taking process of the multi-layer AGV parking and taking system, a motion model of the AGV and the elevator is established; 2) establishing a semi-open-loop queuing network model of a parking and taking system of a multilayer AGV parking garage; 3) obtaining a task completion time function under different conditions of a parking and taking system of the multi-layer AGV parking garage; 4) an order of execution for the batch pick orders is determined according to a modified genetic algorithm. The invention can optimize the ex-warehouse sequence of the system task order by improving the genetic algorithm, reduce the waiting time among all operation links, reduce the times of cross-roadway operation, improve the operation efficiency of the multilayer AGV parking and taking system and reduce the time required by executing tasks.
Description
Technical Field
The invention belongs to the field of intelligent parking, and relates to an optimization method for multiple car taking tasks based on a multilayer AGV parking garage.
Background
The multilayer AGV parking garage system belongs to one of automatic parking and taking car systems (AVS/RS). Unlike conventional automatic storage and retrieval systems (AS/RS), in which the containers can be moved in a plane and across floors by using shelves, the vehicles of the multi-floor AGV parking system are moved in the same floor by AGVs, and the floor-crossing movement requires an elevator at the end of a roadway to perform floor-crossing operation.
According to different working modes of cross-floor elevators of an automatic trolley storing and taking system, the AVS/RS is classified: layer-to-layer AVS/RS (Tier to Tier AVS/RS) and layer-based AVS/RS (Tier captured AVS/RS). The AVS/RS from floor to floor needs the elevator to carry the AGV to finish the movement between floors; based on the AVS/RS of the floor, the floor where the AGV is located is handed over between the cross-floor elevator and the AGV, namely the elevator only carries goods, and each floor needs to be equipped with the AGV to finish the access task of the floor.
Unlike single target scheduling, multiple target scheduling for AGVs is a complex combined process. In practical application, two time periods with the highest order arrival rate of the parking garage are respectively the working time period and the leaving time period, and orders in the two time periods have the characteristics of intensive parking and intensive vehicle taking. According to the car taking operation flow shown in fig. 1, for the batch car taking orders, the situation that the AGVs carry out cross-lane operation and cross-floor operation during operation inevitably occurs. Therefore, how to effectively avoid the situations is a difficult point of multiple car taking tasks of the multilayer AGV parking garage.
Disclosure of Invention
In view of the above, the present invention provides an optimization method for multiple car picking tasks based on a multi-layer AGV parking garage. According to the motion model of the AGV and the elevator established in the parking and taking process of the multilayer AGV parking and taking system, a half-open loop queuing network model of the parking and taking system of the multilayer AGV parking and taking system is established on the basis for research, partial cross matching in a genetic algorithm is improved, the execution sequence of batch vehicle taking orders is determined by using the improved genetic algorithm, the waiting time between all loop sections is reduced, the times of cross-roadway operation are reduced, the operation efficiency of the multilayer AGV parking and taking system is improved, and the time required by task execution is reduced.
In order to achieve the purpose, the invention provides the following technical scheme:
a multi-car-taking task optimization method based on a multi-layer AGV parking garage comprises the following steps:
s1: according to the stopping and taking process of the multilayer AGV stopping and taking system, a motion model of the AGV and the elevator is established:
the time required for the AGV from column i to column j:
the time required by the AGV from the ith lane to the jth lane is as follows:
time required for elevator from i floor to j floor:
wherein, CmThe number of columns that the AGV needs to pass through to accelerate to the maximum speed; c is the total number of the parking lot rows; a is avThe acceleration of the AGV is obtained; v. ofvIs the maximum speed of the AGV; l is the length of each parking space; w is the width of each roadway; w is the width of each parking space; a is the total lane number of the parking garage; a. themThe number of the roadways which need to be passed for accelerating the AGV to the maximum speed; h is the height of each floor of the floor; l is the total number of layers of the parking garage, LmThe number of floors required for the elevator to accelerate to maximum speed; a islIs the elevator acceleration; v. oflIs the maximum speed of the elevator;
s2: according to the motion models of the AGVs and the elevators in the S1, a semi-open-loop queuing network model of the parking and taking system of the multi-layer AGV parking garage is established;
s3: solving a task completion time function of the parking and taking system of the multi-layer AGV parking garage at the car taking position;
s4: an order of execution for the batch pick orders is determined according to a modified genetic algorithm.
Optionally, in S4, the determining the execution sequence of the batch pick-up orders according to the improved genetic algorithm includes:
s41: initializing parking space parameters of a multilayer AGV parking garage system, a parking and taking task queue, elevator motion model parameters and AGV motion model parameters, randomly initializing a chromosome task sequence, and setting iteration times;
s42: calculating a fitness function of the population scale according to the time function in the S3;
s43: selecting excellent parent chromosomes by adopting competitive competition selection and a fitness distribution method based on proportion;
s44: crossing with certain crossing probability Pc by adopting an IPMX strategy to generate excellent offspring chromosomes;
s45: carrying out random 2-point mutation operation according to a certain mutation probability Pm;
s46: updating iteration, if the iteration number is less than T, jumping to 2) to continue executing, if the iteration number is more than or equal to T, stopping iteration, and returning the chromosome gene coding combination, namely the optimal order task sequence matrix;
s47: and distributing the order execution sequence of the optimal tasks of the AGVs according to the mapping relation between the optimal order task sequence matrix and the AGVs.
Optionally, in S44, crossing with an IPMX strategy at a certain crossing probability Pc to generate excellent offspring chromosomes, the method includes the specific steps of:
s441: setting chromosome gene sequences of two parents as p1 and p 2;
s442: performing gene exchange on the parent gene by using an exchange mutation operation and an insertion compiling operation;
s443: establishing a gene exchange matrix Ge by using exchange information;
s444: mapping a gene state matrix Go1 and a dominant vector matrix Go2 according to a gene exchange matrix Ge:
Go1(1,Ge(i,1))＝Ge(i,2)
Go2(1,Ge(i,2))＝Ge(i,1)
s445: obtaining gene state matrixes Gp1 and Gp2 according to Go1 and Go 2;
s446: the combined gene state Gp1 and Gp2 are Gp, i.e., Gp1+ Gp 2;
s447: finding the variable Gene Gv＝find(Gp(1,:)>1)；
S448: and repeating S443-S447 until the iteration number is reached.
Optionally, in S442, performing gene exchange on the parent gene by using an exchange mutation operation and an insertion mutation operation, specifically:
s4421: crossover mutation operations were as follows: and generating the exchange positions a and b by using a random function, wherein the expression is as follows:
a＝rand()％n
b＝rand()％n
Swap＝(a,b),a≠b
wherein: a is the number of the position where the gene is mutated, b is the number of another position exchanged with it, n is the number of the total tasks, and Swap represents the transformation operation;
s4422: the insertion mutation operation was as follows: the insertion position d is generated using a random function, which is expressed as:
d＝rand()％n
Insert＝(c,d),c≠d
wherein: c is the number of the gene position where the mutation occurs, d is the number of the target position of the insertion, n is the number of the total tasks, Insert indicates the insertion mutation operation.
The invention has the beneficial effects that:
(1) the execution sequence of the batch vehicle taking orders is determined by using an improved genetic algorithm, the waiting time among all the ring sections is reduced, the number of cross-roadway operation is reduced, the operation efficiency of the multilayer AGV parking and taking system is improved, and the time required for executing tasks is reduced.
(2) The optimization problem of order sequence arrangement under the condition of dense car taking orders of a multilayer AGV parking garage system can be effectively solved.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
For a better understanding of the objects, aspects and advantages of the present invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of a car picking operation of the multi-level AGV parking garage system;
FIG. 3 is a diagram of a semi-open-loop queuing network model of an AGV system in a batch operation scene;
FIG. 4 is a diagram showing a gene exchange variation;
FIG. 5 shows gene insertion variation.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the present invention, and the specific meaning of the terms described above will be understood by those skilled in the art according to the specific circumstances.
The purpose of the invention is realized by the technical scheme, as shown in figure 1, the specific steps are as follows:
s1: according to the stopping and taking process of the multilayer AGV stopping and taking system, as shown in FIG. 2, a motion model of the AGV and the elevator is established:
the time required for the AGV from column i to column j:
the AGV needs time from ith roadway to jth roadway:
time required for elevator to pass from ith floor to jth floor:
wherein, CmThe number of columns that the AGV needs to pass when accelerating to the maximum speed; c is the total number of the parking lot columns; a is avFor AGV addSpeed; v. ofvIs the maximum speed of the AGV; l is the length of each parking space; w is the width of each roadway; w is the width of each parking space; a is the total lane number of the parking garage; a. themThe number of the roadways which need to be passed for accelerating the AGV to the maximum speed; h is the height of each floor of the floor; l is the total number of layers of the parking garage, LmThe number of floors required for the elevator to accelerate to maximum speed; a islIs the elevator acceleration; v. oflIs the maximum speed of the elevator.
S2: fig. 3 shows a model for establishing a semi-open-loop queuing network of a parking and picking system of a multi-layer AGV parking garage, wherein the average service time of a first service node is as follows:
the average service time of the second service node system is as follows:
wherein, TvtiThe average service time (i is 1,2, … 8) of the first service node under the ith parking scene operation; t isltiThe average service time of the second service node under the operation of the ith parking scene (i equals to 1,2, … 8); t isvqjThe average service time (j is 1,2, … 8) of the first service node in the j-th vehicle taking scene operation; t islqjThe average service time of the second service node under the j-th vehicle taking scene operation is (j is 1,2, … 8); p istiThe occurrence probability (i is 1,2 … 8) of the ith parking work scene; pqjThe occurrence probability of the j-th vehicle taking operation scene is (j is 1,2 … 8);
s3: utilizing the models in S1 and S2 to obtain a task completion time function C of the parking and fetching system of the multi-layer AGV parking garage at the car fetching positionmaxFor the maximum completion time of all the vehicle taking tasks, the function model is as follows:
xi′ih+xi′ih≤1，i＝1，...，n，h＝1，...，m
Cmax＝max Ci＝max{Si+Qih+Pih}
wherein S isiThe starting time of the vehicle taking task i is set; ciThe completion time of the vehicle taking task i is the completion time of the vehicle taking task i; p isihThe time consumed by the AGV to complete the task under the condition that the vehicle taking task i is matched with the Ah is obtained; qihThe time consumed by the elevator to complete the task under the condition that the car taking task i is matched with the Lh is saved; z is a radical of formulaihIs a 0-1 decision variable; y isihIs a 0-1 decision variable; x is the number ofi’ihIf the decision variable is 0-1, if the vehicle taking task i ' and the vehicle taking task i ' are executed by the same AGV, and the task i ' is located at the position immediately before the task i; x is a radical of a fluorine atomii’hA decision variable is 0-1, if the vehicle taking tasks i 'and i are executed by the same AGV, and the task i' is located at the position where the task i is tightly held, and m is the number of the total AGV;
s4: an order of execution for the batch pick orders is determined according to a modified genetic algorithm.
In step S4, the execution order of the batch pick-up orders is determined according to the improved genetic algorithm, and the specific steps are as follows:
s41: initializing parking space parameters of a multi-layer AGV parking garage system, a parking and taking task queue, elevator motion model parameters and AGV motion model parameters, randomly initializing a chromosome task sequence, and setting iteration times;
s42: calculating a fitness function of the population size according to the task completion time function in the step S3, wherein the expression is as follows:
s43: selecting excellent parent chromosomes by adopting competitive competition selection and a fitness distribution method based on proportion;
s44: crossing with certain crossing probability Pc by adopting an IPMX strategy to generate excellent offspring chromosomes;
s45: carrying out random 2-point mutation operation according to a certain mutation probability Pm;
s46: updating iteration, if the iteration number is less than T, jumping to 2) to continue executing, if the iteration number is more than or equal to T, stopping iteration, and returning the chromosome gene coding combination, namely the optimal order task sequence matrix;
s47: and distributing the order execution sequence of the optimal tasks of the AGVs according to the mapping relation between the optimal order task sequence matrix and the AGVs.
In S44, crossing with IPMX strategy at a certain crossing probability Pc to generate excellent offspring chromosomes, the specific steps include: the following examples are presented:
the two parent genes were assumed to be as follows:
p1＝(7,1,5,11,4,6,2,10,12,8,3,9)
p2＝(2,11,4,6,12,10,7,3,5,9,8,1)
setting the initial position and the end position of the crossing as the 4 th gene and the 9 th gene, and designing the IPMX crossing operator as follows:
(1) carrying out gene exchange on parent information:
p1＝(7,1,5,6,12,10,7,3,5,8,3,9)
p2＝(2,11,4,11,4,6,2,10,12,9,8,1)
(2) establishing a gene exchange matrix by exchanging information:
(2) mapping the Ge matrix
Go1(1,Ge(i,1))＝Ge(i,2)
Go2(1,Ge(i,2))＝Ge(i,1)
(3) Generation of Gene status matrix GpiAnd a dominant vector matrix Goi。
A dominant vector matrix:
Go1＝[0 0 10 0 12 11 2 0 0 6 0 4]
Go2＝[0 7 0 12 0 10 0 0 0 3 6 5]
gene status matrix:
Gp1＝[0 0 1 0 1 1 1 0 0 1 0 1]
Gp2＝[0 1 0 1 0 1 0 0 0 1 1 1]
(4) gene status matrices were merged:
Gp＝Gp1+Gp2＝[0 1 1 1 1 2 1 0 0 2 1 2]
(5) solving the variable gene:
Gv＝find(Gp(1,:)>1)＝[6 10 12]
(6) updating the gene exchange matrix according to the calculation result of (5):
(7) updating the dominant vector matrix according to the gene exchange matrix:
Go1＝[0 0 11 0 4 0 2 0 0 0 0 0]
(8) will lead vector matrix Go1And (6) carrying out repeated mapping on the matrix relationship, and keeping the positions of the rest genes unchanged to generate the offspring 1 gene:
p’1＝(2,1,4,6,12,10,7,3,5,8,11,9)
(9) the mapping between progeny genes 1 and 2 is now defined:
Fe(p’1(i))＝p1(i),i＝1,2,…,n
p’2(i)＝Fe(p2(i)),i＝1,2,…,n
(10) the gene sequence of the progeny 2 thereof is known from (9):
p’2＝(7,3,5,11,4,6,2,10,12,9,8,1)
in order to maintain the diversity of population, improve the search efficiency, expand the exploration area and avoid falling into 'precocity', a mutation operator is required to be introduced, and the invention uses the conventional genetic algorithm to solve the common exchange mutation and insertion mutation in the integer coding problem.
(51) Crossover mutation operations were as follows: and generating the exchange positions a and b by using a random function, wherein the expression is as follows:
a＝rand()％n
b＝rand()％n
Swap＝(a,b),a≠b
wherein: a is the number of the position where the gene is mutated, b is the number of the other position exchanged with it, n is the number of the total tasks, and Swap indicates the transformation operation, as shown in fig. 4.
(52) The insertion mutation operation was as follows: the insertion position d is generated using a random function, which is expressed as:
d＝rand()％n
Insert＝(c,d),c≠d
wherein: c is the number of the gene position where the mutation occurred, d is the number of the target position inserted, n is the number of the total task, Insert indicates the insertion mutation operation, as shown in FIG. 5.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (4)
1. A multi-car-taking task optimization method based on a multilayer AGV parking garage is characterized by comprising the following steps: the method comprises the following steps:
s1: according to the stopping and taking process of the multilayer AGV stopping and taking system, a motion model of the AGV and the elevator is established:
AGV required time from column i to column j:
the time required by the AGV from the ith lane to the jth lane is as follows:
time required for elevator from i floor to j floor:
wherein, CmThe number of columns that the AGV needs to pass when accelerating to the maximum speed; c is the total number of the parking lot rows; a isvThe acceleration of the AGV is obtained; v. ofvIs the maximum speed of the AGV; l is the length of each parking space; w is the width of each roadway; w is the width of each parking space; a is the total lane number of the parking garage; a. themThe number of the lanes required to be passed by the AGV to accelerate to the maximum speed; h is the height of each floor of the floor; l is the total number of layers of the parking garage, LmThe number of floors required for the elevator to accelerate to maximum speed; a is alIs the elevator acceleration; v. oflIs the maximum speed of the elevator;
s2: according to the motion models of the AGVs and the elevators in the S1, a semi-open-loop queuing network model of the parking and taking system of the multi-layer AGV parking garage is established;
s3: obtaining a task completion time function of a parking and picking system of the multi-layer AGV parking garage at a picking position;
s4: an order of execution for the batch pick orders is determined according to a modified genetic algorithm.
2. The optimizing method for multi-vehicle-taking task based on multi-layer AGV parking garage according to claim 1, wherein: in S4, determining an execution order of the batch pick-up orders according to the improved genetic algorithm includes:
s41: initializing parking space parameters of a multi-layer AGV parking garage system, a parking and taking task queue, elevator motion model parameters and AGV motion model parameters, randomly initializing a chromosome task sequence, and setting iteration times;
s42: calculating a fitness function of the population scale according to the time function in the S3;
s43: selecting excellent parent chromosomes by adopting competitive competition selection and a fitness distribution method based on proportion;
s44: crossing with a certain crossing probability Pc by adopting an IPMX strategy to generate excellent offspring chromosomes;
s45: carrying out random 2-point mutation operation according to a certain mutation probability Pm;
s46: updating iteration, if the iteration number is less than T, jumping to 2) to continue executing, if the iteration number is more than or equal to T, stopping iteration, and returning the chromosome gene coding combination, namely the optimal order task sequence matrix;
s47: and distributing the order execution sequence of the optimal tasks of the AGVs according to the mapping relation between the optimal order task sequence matrix and the AGVs.
3. The method for optimizing multi-vehicle taking task based on the multilayer AGV parking garage according to claim 2, wherein the method comprises the following steps: in S44, crossing with IPMX strategy at a certain crossing probability Pc to generate excellent offspring chromosomes, the specific steps include:
s441: setting chromosome gene sequences of two parents as p1 and p 2;
s442: performing gene exchange on the parent gene by using an exchange mutation operation and an insertion compilation operation;
s443: establishing a gene exchange matrix Ge by using exchange information;
s444: mapping a gene state matrix Go1 and a dominant vector matrix Go2 according to a gene exchange matrix Ge:
Go1(1，Ge(i，1))＝Ge(i，2)
Go2(1，Ge(i，2))＝Ge(i，1)
s445: obtaining gene state matrixes Gp1 and Gp2 according to Go1 and Go 2;
s446: the combined gene state Gp1 and Gp2 are Gp, i.e., Gp1+ Gp 2;
s447: finding the variable Gene Gv＝find(Gp(1，：)＞1)；
S448: and repeating S443-S447 until the iteration number is reached.
4. The method for optimizing multi-vehicle taking task based on the multilayer AGV parking garage according to claim 3, wherein the method comprises the following steps: in S442, the crossover mutation operation and the insertion mutation operation are used to perform gene crossover on the parent gene, specifically:
s4421: crossover mutation was performed as follows: and generating the exchange positions a and b by using a random function, wherein the expression is as follows:
a＝rand()％n
b＝rand()％n
Swap＝(a，b)，a≠b
wherein: a is the number of the position where the gene is mutated, b is the number of another position exchanged with it, n is the number of the total tasks, and Swap represents the transformation operation;
s4422: the insertion mutation operation was as follows: the insertion position d is generated using a random function, whose expression is:
d＝rand()％n
Insert＝(c，d)，c≠d
wherein: c is the number of the gene position where the mutation occurs, d is the number of the target position of the insertion, n is the number of the total tasks, Insert indicates the insertion mutation operation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210499104.6A CN114781746B (en) | 2022-05-09 | 2022-05-09 | Multi-layer AGV parking garage based multi-picking task optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210499104.6A CN114781746B (en) | 2022-05-09 | 2022-05-09 | Multi-layer AGV parking garage based multi-picking task optimization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114781746A true CN114781746A (en) | 2022-07-22 |
| CN114781746B CN114781746B (en) | 2024-04-19 |
Family
ID=82436226
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210499104.6A Active CN114781746B (en) | 2022-05-09 | 2022-05-09 | Multi-layer AGV parking garage based multi-picking task optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114781746B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040040791A1 (en) * | 2000-03-03 | 2004-03-04 | Tapio Tyni | Method and apparatus for allocating passengers by a genetic algorithm |
| JP2007091378A (en) * | 2005-09-27 | 2007-04-12 | Hitachi Ltd | Group-management system for elevator and its control method |
| CN103955818A (en) * | 2014-05-27 | 2014-07-30 | 山东大学 | Task scheduling method of multilayer shuttle vehicle automatic warehousing system |
| CN104835026A (en) * | 2015-05-15 | 2015-08-12 | 重庆大学 | Automatic stereoscopic warehouse selection operation scheduling modeling and optimizing method based on Petri network and improved genetic algorithm |
| CN111626516A (en) * | 2020-05-30 | 2020-09-04 | 湖南科技大学 | Double-deep-position four-way shuttle system order ordering optimization method considering goods reversing strategy |
| CN112070412A (en) * | 2020-09-15 | 2020-12-11 | 吉林大学 | Configuration scheme and task scheduling method for multiple elevators in three-dimensional warehouse |
| CN113627712A (en) * | 2021-06-25 | 2021-11-09 | 广东烟草惠州市有限责任公司 | Method for optimizing operation sequence of shuttle vehicle of storage system |
| US20210373888A1 (en) * | 2019-08-29 | 2021-12-02 | Huazhong University Of Science And Technology | Multi-objective optimization method and system for master production plan of casting parallel workshops |
-
2022
- 2022-05-09 CN CN202210499104.6A patent/CN114781746B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040040791A1 (en) * | 2000-03-03 | 2004-03-04 | Tapio Tyni | Method and apparatus for allocating passengers by a genetic algorithm |
| JP2007091378A (en) * | 2005-09-27 | 2007-04-12 | Hitachi Ltd | Group-management system for elevator and its control method |
| CN103955818A (en) * | 2014-05-27 | 2014-07-30 | 山东大学 | Task scheduling method of multilayer shuttle vehicle automatic warehousing system |
| CN104835026A (en) * | 2015-05-15 | 2015-08-12 | 重庆大学 | Automatic stereoscopic warehouse selection operation scheduling modeling and optimizing method based on Petri network and improved genetic algorithm |
| US20210373888A1 (en) * | 2019-08-29 | 2021-12-02 | Huazhong University Of Science And Technology | Multi-objective optimization method and system for master production plan of casting parallel workshops |
| CN111626516A (en) * | 2020-05-30 | 2020-09-04 | 湖南科技大学 | Double-deep-position four-way shuttle system order ordering optimization method considering goods reversing strategy |
| CN112070412A (en) * | 2020-09-15 | 2020-12-11 | 吉林大学 | Configuration scheme and task scheduling method for multiple elevators in three-dimensional warehouse |
| CN113627712A (en) * | 2021-06-25 | 2021-11-09 | 广东烟草惠州市有限责任公司 | Method for optimizing operation sequence of shuttle vehicle of storage system |
Non-Patent Citations (3)
| Title |
|---|
| 孙海龙;任楠;: "自动小车存取系统的调度优化", 制造业自动化, vol. 40, no. 02, 25 February 2018 (2018-02-25), pages 26 - 32 * |
| 李建国: "基于改进遗传算法的立体车库布局对比及服务资源优化", 西南大学学报(自然科学版), vol. 41, no. 04, 20 April 2019 (2019-04-20), pages 139 - 148 * |
| 李建国;梁英;刘日;: "基于遗传算法的巷道堆垛式立体车库路径优化", 起重运输机械, no. 12, 20 December 2016 (2016-12-20), pages 59 - 63 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114781746B (en) | 2024-04-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109784566B (en) | Order sorting optimization method and device | |
| CN110084512B (en) | Multi-robot task allocation method for intelligent warehousing system | |
| CN107808215B (en) | Goods allocation optimization method applied to Flying-V type non-traditional layout warehouse | |
| CN110991754B (en) | Multi-target goods location optimization method based on variable neighborhood NSGA-II algorithm | |
| CN113359702B (en) | Intelligent warehouse AGV operation optimization scheduling method based on water wave optimization-tabu search | |
| CN112783172B (en) | AGV and machine integrated scheduling method based on discrete whale optimization algorithm | |
| CN112232545B (en) | AGV task scheduling method based on simulated annealing algorithm | |
| CN111459108B (en) | Task allocation and conflict-free path planning method for pull-type multi-AGV system | |
| CN113050422B (en) | Multi-robot scheduling method based on maximin function multi-objective optimization algorithm | |
| CN115481897A (en) | AGV unmanned warehouse equipment optimization configuration method | |
| CN110543980B (en) | Storage robot management method and device based on segmented charging planning | |
| CN113627712A (en) | Method for optimizing operation sequence of shuttle vehicle of storage system | |
| CN116523165B (en) | Collaborative optimization method for AMR path planning and production scheduling of flexible job shop | |
| Sun et al. | An autonomous vehicle interference-free scheduling approach on bidirectional paths in a robotic mobile fulfillment system | |
| CN114911205A (en) | Multi-AGV multi-target path planning method based on improved genetic algorithm | |
| CN116203959A (en) | Robot path planning method and system based on HAC algorithm | |
| CN115145285A (en) | Multi-point goods taking and delivering optimal path planning method and system for storage AGV | |
| CN115421448A (en) | AGV (automatic guided vehicle) picking path planning method and system | |
| CN111626516B (en) | Order ordering optimization method of double-deep four-way shuttle system considering cargo pouring strategy | |
| CN114781746A (en) | Multi-vehicle-taking task optimization method based on multilayer AGV parking garage | |
| CN111027875A (en) | Intelligent warehousing multi-robot task allocation method based on self-adaptive task pool | |
| CN116342039A (en) | Optimizing method for goods distribution and sorting of stereoscopic warehouse | |
| CN116523221A (en) | Optimal scheduling method and system for intelligent warehouse picking task | |
| CN111047249A (en) | Shelf repositioning method and system | |
| CN116205550A (en) | Logistics garden vehicle goods taking scheduling method based on self-adaptive discrete particle swarm algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |