CN114781746B - Multi-layer AGV parking garage based multi-picking task optimization method - Google Patents
Multi-layer AGV parking garage based multi-picking task optimization method Download PDFInfo
- Publication number
- CN114781746B CN114781746B CN202210499104.6A CN202210499104A CN114781746B CN 114781746 B CN114781746 B CN 114781746B CN 202210499104 A CN202210499104 A CN 202210499104A CN 114781746 B CN114781746 B CN 114781746B
- Authority
- CN
- China
- Prior art keywords
- agv
- parking
- gene
- layer
- task
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000005457 optimization Methods 0.000 title abstract description 6
- 230000002068 genetic effect Effects 0.000 claims abstract description 12
- 108090000623 proteins and genes Proteins 0.000 claims description 47
- 239000011159 matrix material Substances 0.000 claims description 28
- 230000035772 mutation Effects 0.000 claims description 28
- 210000000349 chromosome Anatomy 0.000 claims description 17
- 230000037431 insertion Effects 0.000 claims description 15
- 238000003780 insertion Methods 0.000 claims description 15
- 238000013507 mapping Methods 0.000 claims description 8
- 230000001133 acceleration Effects 0.000 claims description 6
- 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
- 101150084750 1 gene Proteins 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
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000036961 partial effect Effects 0.000 description 1
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 an optimization method for multi-vehicle taking tasks based on a multi-layer 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, establishing a motion model of the AGV and the elevator; 2) Establishing a semi-open loop queuing network model of a parking and taking system of the multi-layer AGV parking garage; 3) Solving task completion time functions of the multi-layer AGV parking garage parking and taking system under different conditions; 4) An order of execution of the batch pick-up orders is determined based on the improved genetic algorithm. The invention can optimize the order delivery sequence of the system task order by improving the genetic algorithm, reduce the waiting time among the operation ring joints, reduce the times of the cross-roadway operation, improve the operation efficiency of the multi-layer AGV parking and taking system and reduce the time required for executing the task.
Description
Technical Field
The invention belongs to the field of intelligent parking, and relates to an optimization method for multi-vehicle taking tasks based on a multi-layer AGV parking garage.
Background
A multi-deck AGV parking garage system belongs to one of automatic access trolley systems (Autonomous vehicle storage AND RETRIEVAL SYSTEMS, AVS/RS). Unlike conventional automated access systems (Autonomous storage AND RETRIEVAL SYSTEMS, AS/RS) in which containers can be moved in a plane and cross-floor by shelves, the vehicles of the multi-floor AGV parking system move in the same floor by AGVs, and the cross-floor movement requires the use of elevators at the ends of the aisles to perform the cross-floor operation.
According to different working modes of the cross-layer elevator of the automatic car access system, AVS/RS is classified: layer-to-layer AVS/RS (tier to tier AVS/RS) and layer-based AVS/RS (TIER CAPTIVE AVS/RS). Layer-to-layer AVS/RS requires an elevator to carry an AGV to complete the movement between floors; based on the AVS/RS of layer, accomplish the handing-over between cross-floor elevator and AGV at the floor that AGV is located, the elevator only carries the goods promptly, and every floor all need be equipped with the AGV and accomplish the access task of this floor.
Unlike single-target scheduling, multi-target scheduling of AGVs is a complex combination process. In practical application, two time periods with highest order arrival rate of the parking garage are working and off working time periods respectively, and orders in the two time periods show the characteristics of dense parking and dense vehicle taking. According to the vehicle taking operation flow in fig. 1, for batch vehicle taking orders, the situations of cross-tunnel operation and cross-floor operation of the AGV during operation are unavoidable. Therefore, how to effectively avoid the occurrence of the situation is one difficulty of the multi-layer AGV parking garage multi-picking task.
Disclosure of Invention
In view of the above, the invention aims to provide an optimization method for multi-car taking tasks based on a multi-layer AGV parking garage. According to the motion model of AGVs and elevators established in the parking process of the multi-layer AGV parking system, the semi-open loop queuing network model of the multi-layer AGV parking garage parking system constructed on the basis is researched, 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, waiting time among all loops is reduced, the number of times of roadway crossing operation is reduced, the operation efficiency of the multi-layer AGV parking system is improved, and the time required for executing tasks is reduced.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A multi-layer AGV parking garage based multi-picking task optimization method comprises the following steps:
S1: according to the parking and taking process of the multi-layer AGV parking and taking system, a motion model of the AGV and an elevator is established:
the time required for the AGV to pass from column i to column j:
the time required for AGVs to pass from the ith lane to the jth lane:
The time required for the elevator from the i-th floor to the j-th floor:
Wherein C m is the number of columns that the AGV needs to pass when accelerating to maximum speed; c is the total number of parking lots; a v is AGV acceleration; v v is 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 roadway number of the parking garage; a m is the number of lanes that the AGV needs to pass when accelerating to maximum speed; h is the height of each floor; l is the total layer number of the parking garage, and L m is the layer number required by the elevator to accelerate to the maximum speed; a l is the elevator acceleration; v l is the maximum speed of the elevator;
S2: according to the motion model of the AGVs and the elevators in the S1, a semi-open loop queuing network model of a parking and taking system of the multi-layer AGV parking garage is established;
S3: solving a task completion time function of a parking and taking system of the multi-layer AGV parking garage at a vehicle taking position;
S4: an order of execution of the batch pick-up orders is determined based on the improved genetic algorithm.
Optionally, in S4, determining the execution sequence of the batch pick-up order according to the improved genetic algorithm, and the specific steps include:
s41: initializing parking space parameters of a multi-layer AGV parking garage system, parking and taking task queues, elevator motion model parameters, AGV motion model parameters, randomly initializing a chromosome task sequence, and setting iteration times;
s42: calculating an fitness function of the population scale according to the time function in the step S3;
s43: selecting an excellent parent chromosome by adopting a competitive game selection and proportion-based fitness distribution method;
s44: crossing by adopting IPMX strategies with a certain crossing probability Pc to generate excellent offspring chromosomes;
S45: carrying out random 2-point mutation operation according to a certain mutation probability Pm;
S46: if the iteration times are less than T, jumping to S42 for continuous execution, if the iteration times are more than or equal to T, stopping iteration, and returning to the chromosome gene coding combination, namely the optimal order task sequence matrix;
S47: and distributing the execution sequence of the optimal task order of each AGV according to the mapping relation between the optimal order task sequence matrix and the AGVs.
Optionally, in the step S44, crossing is performed with a IPMX strategy with a certain crossing probability Pc, so as to generate excellent offspring chromosomes, which specifically includes:
S441: setting the chromosome gene sequences of the two father generation as p1 and p2;
s442: performing gene exchange on the parent gene by using exchange mutation operation and 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 the gene exchange matrix Ge:
Go1(1,Ge(i,1))＝Ge(i,2)
Go2(1,Ge(i,2))＝Ge(i,1)
s445: from Go1 and Go2, the gene status matrices Gp1 and Gp2 are determined;
S446: combining the gene state matrices Gp1 and Gp2 to Gp, i.e., gp=gp1+gp2;
s447: solving a variable gene G v＝find(Gp (1,:1);
s448: s443 to S447 are repeated until the number of iterations is reached.
Optionally, in S442, the parent gene is subjected to gene exchange by using an exchange mutation operation and an insertion mutation operation, specifically:
s4421: the crossover mutation operation is as follows: the exchange positions a, b are generated using a random function, expressed as:
a＝rand()％n
b＝rand()％n
Swap＝(a,b),a≠b
Wherein: a is the position number of the mutation of the gene, b is the other position number exchanged with the gene, n is the number of total tasks, and Swap represents the transformation operation;
s4422: the insertion mutation procedure was as follows: the insertion position d is generated using a random function, expressed as:
d＝rand()％n
Insert＝(c,d),c≠d
Wherein: c is the number of mutated gene positions, d is the number of inserted target positions, n is the number of total tasks, and Insert represents the insertion mutation operation.
The invention has the beneficial effects that:
(1) And the improved genetic algorithm is used for determining the execution sequence of the batch vehicle taking orders, so that the waiting time among the links is reduced, the number of times of cross-roadway operation is reduced, the operation efficiency of the multi-layer AGV vehicle stopping and taking system is improved, and the time required for executing tasks is reduced.
(2) The method can effectively solve the problem of optimizing order sequence arrangement under the condition that the multi-layer AGV parking garage system takes the dense orders.
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 objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of the multi-level AGV parking garage system pick-up operation;
FIG. 3 is a diagram of a semi-open loop queuing network model of an AGV system in a batch job scenario;
FIG. 4 is a gene exchange variation map;
FIG. 5 is a diagram showing the variation of gene insertion.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present invention by way of illustration, and the following embodiments and features in the embodiments may be combined with each other without conflict.
Wherein the drawings are for illustrative purposes only and are shown in schematic, non-physical, and not intended to limit the invention; for the purpose of better illustrating embodiments of the invention, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the size of the actual product; it will be appreciated 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 numbers in the drawings of embodiments of the invention correspond to the same or similar components; in the description of the present invention, it should be understood that, if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc., that indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but not for indicating or suggesting that the referred device or element must have a specific azimuth, be constructed and operated in a specific azimuth, so that the terms describing the positional relationship in the drawings are merely for exemplary illustration and should not be construed as limiting the present invention, and that the specific meaning of the above terms may be understood by those of ordinary skill in the art according to the specific circumstances.
The aim of the invention is achieved by the technical proposal, as shown in figure 1, comprising the following specific steps:
S1: according to the parking and taking process of the multi-layer AGV parking 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 to pass from column i to column j:
the time required for AGVs to pass from the ith lane to the jth lane:
The time required for the elevator from the i-th floor to the j-th floor:
Wherein C m is the number of columns that the AGV needs to pass when accelerating to maximum speed; c is the total number of parking lots; a v is AGV acceleration; v v is 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 roadway number of the parking garage; a m is the number of lanes that the AGV needs to pass when accelerating to maximum speed; h is the height of each floor; l is the total layer number of the parking garage, and L m is the layer number required by the elevator to accelerate to the maximum speed; a l is the elevator acceleration; v l is the maximum speed of the elevator.
S2: the semi-open loop queuing network model of the multi-layer AGV parking garage parking system is established as shown in FIG. 3, and the average service time of the first service node is as follows:
The average service time of the second service node system is as follows:
Wherein T vti is the average service time (i=1, 2, …) of the first service node under the ith parking scenario job; t lti is the average service time (i=1, 2, …) of the second service node under the ith parking scenario job; t vqj is the average service time (j=1, 2, …) of the first service node under the j-th pick-up scenario job; t lqj is the average service time (j=1, 2, …) of the second service node under the j-th pick-up scenario job; p ti is the i-th parking job scene occurrence probability (i=1, 2 …); p qj is the j-th pick-up job scene occurrence probability (j=1, 2 …);
S3: and (3) solving a task completion time function of the multi-layer AGV parking garage parking and taking system at a vehicle taking position by using the models in S1 and S2, wherein C max is the maximum completion time of all the vehicle taking tasks, and the function model is as follows:
xi'ih+xi'ih≤1,i＝1,...，n，h＝1,...,m
Cmax＝max Ci＝max{Si+Qih+Pih}
s i is the starting time of the vehicle taking task i; c i is the completion time of the vehicle taking task i; p ih is the time consumed by the AGV to complete the task under the matching condition of the vehicle taking task i and the Ah; q ih is the time consumed by the elevator to complete the task under the condition that the vehicle taking task i is matched with Lh; z ih is a 0-1 decision variable; y ih is a 0-1 decision variable; x i'ih is a 0-1 decision variable, if the picking-up task i 'and i are executed by the same AGV, and the task i' is located at a position immediately before the task i; x ii'h is a 0-1 decision variable, if the picking tasks i 'and i are executed by the same AGV, and the task i' is positioned at a position after the task i is tightly acquired, and m is the number of the total AGVs;
S4: an order of execution of the batch pick-up orders is determined based on the improved genetic algorithm.
In step S4, the execution sequence 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, parking and taking task queues, elevator motion model parameters, AGV motion model parameters, randomly initializing a chromosome task sequence, and setting iteration times;
s42: calculating an fitness function of the population scale according to the task completion time function in the step S3, wherein the fitness function is expressed as follows:
s43: selecting an excellent parent chromosome by adopting a competitive game selection and proportion-based fitness distribution method;
s44: crossing by adopting IPMX strategies with a certain crossing probability Pc to generate excellent offspring chromosomes;
S45: carrying out random 2-point mutation operation according to a certain mutation probability Pm;
S46: if the iteration times are less than T, jumping to S42 for continuous execution, if the iteration times are more than or equal to T, stopping iteration, and returning to the chromosome gene coding combination, namely the optimal order task sequence matrix;
S47: and distributing the execution sequence of the optimal task order of each AGV according to the mapping relation between the optimal order task sequence matrix and the AGVs.
In S44, crossing is performed by using IPMX strategies with a certain crossing probability Pc, so as to generate excellent offspring chromosomes, and the specific steps include: the following examples are shown:
The two parent genes are assumed 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)
The crossing initial position and the ending position are set as the 4 th gene and the 9 th gene, and the IPMX crossing operator is designed as follows:
(1) The parent information is subjected to gene exchange:
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 matrix Ge
Go1(1,Ge(i,1))＝Ge(i,2)
Go2(1,Ge(i,2))＝Ge(i,1)
(3) A gene state matrix G pi and a dominant vector matrix G oi are generated.
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 state 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) Combining the gene state matrix:
Gp＝Gp1+Gp2＝[0 1 1 1 1 2 1 0 0 2 1 2]
(5) The variable genes are solved:
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) Repeatedly mapping the matrix relation between the dominant vector matrix G o1 and the matrix relation of the dominant vector matrix (6), and generating a progeny 1 gene with the positions of the remaining genes unchanged:
p’1＝(2,1,4,6,12,10,7,3,5,8,11,9)
(9) The mapping relationship between the 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 its progeny 2 is known from (9):
p’2＝(7,3,5,11,4,6,2,10,12,9,8,1)
In order to maintain diversity of population, improve search efficiency, expand exploration area, avoid sinking in "precocity", need introduce mutation operator, the invention uses traditional genetic algorithm to solve the common exchange mutation and insertion mutation in the integer coding problem.
(51) The crossover mutation operation is as follows: the exchange positions a, b are generated using a random function, expressed as:
a＝rand()％n
b＝rand()％n
Swap＝(a,b),a≠b
Wherein: a is the position number of the mutation of the gene, b is another position number exchanged with the mutation, n is the number of total tasks, and Swap represents the transformation operation, as shown in FIG. 4.
(52) The insertion mutation procedure was as follows: the insertion position d is generated using a random function, expressed as:
d＝rand()％n
Insert＝(c,d),c≠d
Wherein: c is the number of mutated gene positions, d is the number of inserted target positions, n is the number of total tasks, and Insert represents the insertion mutation operation, as shown in fig. 5.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (1)
1. The optimizing method for the multi-picking-up task based on the multi-layer AGV parking garage is characterized by comprising the following steps of: the method comprises the following steps:
S1: according to the parking and taking process of the multi-layer AGV parking and taking system, a motion model of the AGV and an elevator is established:
the time required for the AGV to pass from column i to column j:
the time required for AGVs to pass from the ith lane to the jth lane:
The time required for the elevator from the i-th floor to the j-th floor:
Wherein C m is the number of columns that the AGV needs to pass when accelerating to maximum speed; c is the total number of parking lots; a v is AGV acceleration; v v is 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 roadway number of the parking garage; a m is the number of lanes that the AGV needs to pass when accelerating to maximum speed; h is the height of each floor; l is the total layer number of the parking garage, and L m is the layer number required by the elevator to accelerate to the maximum speed; a l is the elevator acceleration; v l is the maximum speed of the elevator;
S2: according to the motion model of the AGVs and the elevators in the S1, a semi-open loop queuing network model of a parking and taking system of the multi-layer AGV parking garage is established;
S3: solving a task completion time function of a parking and taking system of the multi-layer AGV parking garage at a vehicle taking position;
s4: determining the execution sequence of the batch pick-up orders according to the improved genetic algorithm;
In the step S4, determining the execution sequence of the batch pick-up orders according to the improved genetic algorithm, wherein the specific steps include:
s41: initializing parking space parameters of a multi-layer AGV parking garage system, parking and taking task queues, elevator motion model parameters, AGV motion model parameters, randomly initializing a chromosome task sequence, and setting iteration times;
s42: calculating an fitness function of the population scale according to the time function in the step S3;
s43: selecting an excellent parent chromosome by adopting a competitive game selection and proportion-based fitness distribution method;
s44: crossing by adopting IPMX strategies with a certain crossing probability Pc to generate excellent offspring chromosomes;
S45: carrying out random 2-point mutation operation according to a certain mutation probability Pm;
S46: if the iteration times are less than T, jumping to S42 for continuous execution, if the iteration times are more than or equal to T, stopping iteration, and returning to the chromosome gene coding combination, namely the optimal order task sequence matrix;
S47: distributing the execution sequence of each AGV optimal task order according to the mapping relation between the optimal order task sequence matrix and the AGVs;
in S44, crossing is performed with a IPMX strategy at a certain crossing probability Pc, so as to generate excellent offspring chromosomes, which specifically includes the steps of:
S441: setting the chromosome gene sequences of the two father generation as p1 and p2;
s442: performing gene exchange on the parent gene by using exchange mutation operation and 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 the gene exchange matrix Ge:
Go1(1,Ge(i,1))＝Ge(i,2)
Go2(1,Ge(i,2))＝Ge(i,1)
s445: from Go1 and Go2, the gene status matrices Gp1 and Gp2 are determined;
S446: combining the gene state matrices Gp1 and Gp2 to Gp, i.e., gp=gp1+gp2;
s447: solving a variable gene G v＝find(Gp (1,:1);
s448: repeating S443-S447 until the iteration times are reached;
In S442, the crossover mutation operation and the insertion mutation operation are used to perform gene crossover on the parent gene, specifically:
s4421: the crossover mutation operation is as follows: the exchange positions a, b are generated using a random function, expressed as:
a＝rand()％n
b＝rand()％n
Swap＝(a,b),a≠b
Wherein: a is the position number of the mutation of the gene, b is the other position number exchanged with the gene, n is the number of total tasks, and Swap represents the transformation operation;
s4422: the insertion mutation procedure was as follows: the insertion position d is generated using a random function, expressed as:
d＝rand()％n
Insert＝(c,d),c≠d
Wherein: c is the number of mutated gene positions, d is the number of inserted target positions, n is the number of total tasks, and Insert represents 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 CN114781746A (en) | 2022-07-22 |
| CN114781746B true 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 (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BR0108953A (en) * | 2000-03-03 | 2002-12-17 | Kone Corp | Process and apparatus for allocating passengers in a group of elevators |
| CN110598920B (en) * | 2019-08-29 | 2023-03-17 | 华中科技大学 | Multi-objective optimization method and system for main production plan of casting parallel workshop |
-
2022
- 2022-05-09 CN CN202210499104.6A patent/CN114781746B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
Non-Patent Citations (3)
| Title |
|---|
| 基于改进遗传算法的立体车库布局对比及服务资源优化;李建国;西南大学学报(自然科学版);20190420;第41卷(第04期);139-148 * |
| 基于遗传算法的巷道堆垛式立体车库路径优化;李建国;梁英;刘日;;起重运输机械;20161220(第12期);59-63 * |
| 自动小车存取系统的调度优化;孙海龙;任楠;;制造业自动化;20180225;第40卷(第02期);26-32 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114781746A (en) | 2022-07-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109784566B (en) | Order sorting optimization method and device | |
| CN107808215B (en) | Goods allocation optimization method applied to Flying-V type non-traditional layout warehouse | |
| CN113359702B (en) | Intelligent warehouse AGV operation optimization scheduling method based on water wave optimization-tabu search | |
| CN106773686B (en) | Path model method for building up is dispatched with piler under the double vehicle operational modes of rail | |
| CN110991754B (en) | Multi-target goods location optimization method based on variable neighborhood NSGA-II algorithm | |
| CN108897316B (en) | Cluster warehousing robot system control method based on pheromone navigation | |
| CN109472362B (en) | AGV dynamic scheduling method and device based on variable task window | |
| CN115481897A (en) | AGV unmanned warehouse equipment optimization configuration method | |
| Li et al. | A simulation study on the robotic mobile fulfillment system in high-density storage warehouses | |
| CN116203959A (en) | Robot path planning method and system based on HAC algorithm | |
| CN116523165B (en) | Collaborative optimization method for AMR path planning and production scheduling of flexible job shop | |
| CN113627712A (en) | Method for optimizing operation sequence of shuttle vehicle of storage system | |
| CN114296440A (en) | AGV real-time scheduling method integrating online learning | |
| CN115421448A (en) | AGV (automatic guided vehicle) picking path planning method and system | |
| CN115145285A (en) | Multi-point goods taking and delivering optimal path planning method and system for storage AGV | |
| CN112990818A (en) | Automatic warehouse goods space optimization method and system based on auction mechanism | |
| CN114781746B (en) | Multi-layer AGV parking garage based multi-picking task optimization method | |
| CN115587679A (en) | AGV optimization scheduling method for intelligent warehouse | |
| CN111626516B (en) | Order ordering optimization method of double-deep four-way shuttle system considering cargo pouring strategy | |
| CN112508481A (en) | Intelligent storage multi-AGV scheduling method | |
| CN112989696A (en) | Automatic picking system goods location optimization method and system based on mobile robot | |
| CN116342039A (en) | Optimizing method for goods distribution and sorting of stereoscopic warehouse | |
| Pfrommer et al. | Autonomously organized block stacking warehouses: A review of decision problems and major challenges | |
| CN111047249A (en) | Shelf repositioning method and system | |
| CN116523221A (en) | Optimal scheduling method and system for intelligent warehouse picking task |
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 |