CN114493181A - Multi-load AGV task scheduling method under intelligent storage environment - Google Patents

Multi-load AGV task scheduling method under intelligent storage environment Download PDF

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CN114493181A
CN114493181A CN202210006556.6A CN202210006556A CN114493181A CN 114493181 A CN114493181 A CN 114493181A CN 202210006556 A CN202210006556 A CN 202210006556A CN 114493181 A CN114493181 A CN 114493181A
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individuals
agv
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CN114493181B (en
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蔺一帅
徐云龙
王亮
王徐华
安浩铜
胡刚
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Xidian University
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    • 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
    • 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
    • G06Q10/06316—Sequencing of tasks or work
    • 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
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

The invention discloses a multi-load AGV task scheduling method under an intelligent storage environment, which comprises the following steps: acquiring warehouse entry and exit information; carrying out population initialization to obtain an initial population with N individuals; performing cross and variation operation on the individuals in the initial population to generate offspring individuals and combining the offspring individuals with the parent individuals to form a second population with 2N individuals; dividing individuals in the second population into different levels of disposable layers according to a preset fitness function; carrying out crowding degree calculation on all individuals in the M-th level disposable layer of the second population to obtain a crowding distance; individuals with large crowding distances are reserved in the current disposable layer, and a third population with N individuals is formed by the individuals in the previous M-1 level disposable layer; and carrying out multiple iterations, and determining an optimal cargo scheduling scheme according to iteration results. The method is beneficial to solving the problem of task scheduling of multiple AGV in the field of intelligent storage, obtaining reasonable AGV quantity and AGV goods taking sequence, and improving the intelligent storage performance.

Description

Multi-load AGV task scheduling method under intelligent storage environment
Technical Field
The invention belongs to the technical field of intelligent storage, and particularly relates to a multi-load AGV task scheduling method in an intelligent storage environment.
Background
Automated Storage and Retrieval systems (AS/RS) based AGVs (Automated Guided vehicles) have become an effective and competitive solution for suppliers and distributors, and AGVs have been widely used to perform Storage or Retrieval tasks. Specifically, an AGV which is scheduled and designated according to the system starts from an original position, arrives at a designated station according to a planned route, provides the operation of dispatching or goods taking and the like, and finally returns to a storage position or a preset position. In the competitive intelligent manufacturing industry, the AGV can provide end-to-end warehousing services with better control and execution capacity, and has the advantages of efficient storage and retrieval performance, low error rate, low labor cost and the like.
With the development of AGV-based AS/RS, two kinds of AGVs, i.e., single-load AGVs and multiple-load AGVs, have been used in the AS/RS. A single load AGV can only carry one item or SKU at a time, while a multiple load AGV can pick up multiple different items from one or more stations. The comparison of the performances of the single-load AGV and the multiple-load AGV is deeply discussed, and the analysis of the experimental result obviously observes that the application of the multiple-load AGV can obviously reduce the necessary quantity and the related congestion required by the AGV and improve the effectiveness of the system. Therefore, the application of multiple AGVs has become a necessary trend in the face of the increase in AS/RS size and the increasing demand for system transport efficiency.
However, scheduling optimization for heavily loaded AGVs is far from fully understood, and many of the interrelated elements are simplified or even ignored, for example. Route conflicts, the number of AGVs, and load decisions of each AGV, which may have a greater impact on system performance and energy consumption, and economic cost.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a multi-load AGV task scheduling method in an intelligent storage environment. The technical problem to be solved by the invention is realized by the following technical scheme:
the invention provides a multi-load AGV task scheduling method under an intelligent storage environment, which comprises the following steps:
s1: acquiring warehouse entry and exit information, wherein the warehouse entry and exit information at least comprises the number of goods to be transported, the number of multi-load AGV and the position of a goods shelf;
s2: performing population initialization according to the warehousing and ex-warehousing information to construct an initial population with N individuals;
s3: performing cross and variation operation on individuals in the initial population as parent individuals to generate offspring individuals and combine the offspring individuals with the parent individuals to form a second population with 2N individuals;
s4: dividing the individuals in the second population into different levels of disposable layers according to a preset fitness function;
s5: carrying out crowding degree calculation on all individuals in an Mth-level disposable layer of a second population to obtain the crowding distance of each individual, wherein Nth individuals divided according to different levels in the second population are located in the Mth-level disposable layer;
s6: reserving individuals with congestion distances meeting requirements in the current disposable layer, and forming a third population with N individuals from the individuals in the previous M-1 level disposable layer;
s7: and repeating the steps S2-S6 by using the third group for a plurality of iterations, and determining the optimal cargo scheduling scheme according to the iteration result when the iteration times reach the preset iteration times.
In an embodiment of the present invention, the S2 includes:
setting the number of individuals in the initial population, wherein the gene of each individual comprises an AGV number A generated randomlykAnd goods number WjAnd the individuals in the initial population all satisfy the following four initial constraints:
Figure BDA0003455651710000031
wherein K represents the maximum available number of current multiple-load AGVs, M represents the total number of goods to be handled,
Figure BDA0003455651710000032
representing goods WjWhether or not to be AGV AkCarrying;
Figure BDA0003455651710000033
wherein the content of the first and second substances,
Figure BDA0003455651710000034
indicating AGV AkIn transporting goods WiWhether or not to carry goods Wj，
Figure BDA0003455651710000035
Where L represents the maximum load of each multiple loaded AGV,
Figure BDA0003455651710000036
in an embodiment of the present invention, the S3 includes:
s31: taking the individuals in the initial population as parent individuals, and performing cross operation on every two individuals to obtain offspring individuals with the same number as the parent individuals;
s32: carrying out mutation operation on the filial generation individuals after the cross operation to obtain mutated filial generation chromosomes;
s33: and combining the parent individuals and the variant offspring individuals to form a second population with the number of individuals being twice that of the initial population.
In an embodiment of the present invention, the S31 includes:
s311: randomly selecting two individuals from the initial population as a first parent individual and a second parent individual, wherein the genes are cargo numbers WjAnd AGV number AkRandomly selecting a goods number or an AGV number from the first parent individual as a first parent designated gene, copying all genes before the first parent designated gene to corresponding positions of a first child chromosome, carrying out inheritance on the goods numbers of the rest part in the first child chromosome according to the goods number sequence of the second parent individual, deleting the goods numbers of the first child chromosome, wherein the AGV numbers of the first child chromosome are randomly selected in the number interval of the AGV numbers in the first parent dyeing and the second parent chromosome, randomly inserting the AGV numbers into the goods numbers, and reserving individuals meeting the initial constraint condition, so that the first child chromosome is formed;
s312: randomly selecting a gene from the second parent individuals as a second parent designated gene, copying all genes before the second parent designated gene to corresponding positions of second child chromosomes, carrying out inheritance on the rest part of the cargo numbers in the second child chromosomes according to the gene sequence of the first parent individuals and deleting the cargo numbers of the second child chromosomes, wherein the AGV numbers of the second child chromosomes are randomly selected in the interval of the number of the AGV numbers in the first parent dyeing and the second parent chromosomes, randomly inserting the AGV numbers into the cargo numbers, and reserving individuals meeting the initial constraint condition so as to form the second child chromosomes;
s313: and repeating the steps S311-S312, and crossing the remaining parent individuals according to the preset crossing probability so as to obtain the offspring individuals with the same number as the parent individuals.
In an embodiment of the present invention, the S32 includes:
selecting the filial generation individuals after the cross operation, carrying out position exchange on different genes of the filial generation individuals to carry out mutation operation, then checking whether the exchanged individuals meet the initial constraint condition, if so, keeping the filial generation individuals as the mutated filial generation individuals, and if not, abandoning the mutation operation again.
In an embodiment of the present invention, the S4 includes:
s41: respectively setting a first fitness function, a second fitness function and a third fitness function according to the number of the AGVs, the maximum operation time of the AGVs and the conflict among the AGVs;
s42: and sequencing the individuals in the intermediate population by using a rapid non-dominant sequencing algorithm according to the first fitness function, the second fitness function and the third fitness function, and dividing the individuals in the intermediate population into dominant layers of different grades.
In one embodiment of the present invention, the first fitness function is targeted to optimize the number of AGVs, and is expressed as:
Figure BDA0003455651710000051
wherein A iskDenotes the k AGV, N (A)k) Indicating whether the kth AGV is selected to transport the goods, wherein K represents the maximum number of the AGV which can be selected currently;
the second fitness function objective is to minimize the maximum runtime of the AGV, expressed as:
F2＝min(max(G(Ak)))，k＝1,…,K
wherein A iskDenotes the k-th AGV, G (A)k) Indicates the total time, max (G (A), that the k AGV is transporting the loadk) Represents the maximum shipment time of a single AGV among all AGVs.
The third fitness function is to minimize conflicts between different AGVs, and is expressed as:
Figure BDA0003455651710000052
wherein A isiDenotes the ith AGV, AjA jth AGV is indicated,
Figure BDA0003455651710000053
indicating the number of collisions between the ith and jth AGVs.
In an embodiment of the present invention, the S5 includes:
sorting the first function values of all the individuals in the current disposable layer from large to small, setting the congestion distance initial values of the individuals with the minimum first function values and the individuals with the maximum first function values to be infinite, and setting the congestion distance initial values of the other individuals in the disposable layer to be zero;
calculating a first congestion distance of each individual according to the initial congestion distance value of each individual and the first function value;
sorting the second function values of each individual in the current disposable layer from large to small;
calculating a second congestion distance of each individual according to the first congestion distance of each individual and the second function value;
sorting the third function values of each individual in the current disposable layer from large to small;
and calculating a third crowding distance of each individual according to the second crowding distance of each individual and the third function value, and determining the third crowding distance as the crowding distance of the individual.
In an embodiment of the present invention, the calculation formula of the first congestion distance is:
n1＝n0+(f1(i+1)-f1(i-1))/(f1 max-f1 min)
wherein, for each level of disposable layers, n0An initial value of a crowding distance, f, representing the individual1(i +1) represents a first function value ranked at the (i +1) th individual, f1(i-1) represents a first function value ranked at the i-1 st individual, f1 maxRepresenting the maximum value of the first function, f1 minA minimum value representing the first function value;
the calculation formula of the second congestion distance is as follows:
Figure BDA0003455651710000061
wherein for eachA level of disposable layers, n1A first crowding distance, f, representing said individual2(i +1) represents a second function value ranked at the (i +1) th individual, f2(i-1) represents a second function value ranked at the i-1 st individual,
Figure BDA0003455651710000062
the maximum value of the second function value is represented,
Figure BDA0003455651710000063
a minimum value representing the second function value;
the calculation formula of the third congestion distance is as follows:
Figure BDA0003455651710000064
wherein, for each level of disposable layers, n2A second crowding distance, f, representing said individual3(i +1) represents a third function value ranked at the i +1 st individual, f3(i-1) represents a third function value ranked at the i-1 st individual,
Figure BDA0003455651710000071
the maximum value of the third function value is represented,
Figure BDA0003455651710000072
the minimum value of the third function value is indicated.
Another aspect of the present invention provides a storage medium, wherein the storage medium stores a computer program, and the computer program is configured to execute the steps of the method for scheduling multiple-load AGV tasks in a smart storage environment according to any one of the above embodiments.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the multi-load AGV task scheduling method under the intelligent storage environment, the number of multi-load AGVs which are not considered in the past is brought into an algorithm, after the ex-warehouse information is obtained, initialization is carried out according to the ex-warehouse information, the initial population is obtained, individuals in the initial population are divided into layers which can be controlled in different levels through the preset fitness function, the task scheduling of the multi-load AGVs in the intelligent storage field is facilitated, the reasonable AGV number and the AGV goods taking sequence are calculated, and the intelligent storage performance is improved.
2. According to the multi-load AGV task scheduling method in the intelligent storage environment, conflicts between the AGV with the number as small as possible and the AGV with the number as small as possible are used, so that the AGV goods taking time is shortened, and the economic benefit of intelligent storage is prompted.
3. According to the multi-load AGV task scheduling method, the distribution strategy of the AGVs can be designed under the condition that the number of the AGVs is insufficient, and the intelligent storage efficiency is improved as much as possible; the service efficiency of the AGV can be evaluated, the no-load condition of the AGV is analyzed, and theoretical support is provided for improving the storage efficiency of the AGV.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
FIG. 1 is a flowchart of a method for scheduling a multi-load AGV task in an intelligent storage environment according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a model of a smart warehouse according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a crossover operation according to an embodiment of the present invention.
Detailed Description
In order to further explain the technical means and effects of the present invention adopted to achieve the predetermined invention purpose, the following describes in detail an AGV task scheduling method in an intelligent storage environment according to the present invention with reference to the accompanying drawings and the detailed description.
The foregoing and other technical matters, features and effects of the present invention will be apparent from the following detailed description of the embodiments, which is to be read in connection with the accompanying drawings. The technical means and effects of the present invention adopted to achieve the predetermined purpose can be more deeply and specifically understood through the description of the specific embodiments, however, the attached drawings are provided for reference and description only and are not used for limiting the technical scheme of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a good or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of additional like elements in a good or device comprising the element.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for scheduling tasks of a multiple-load AGV in an intelligent storage environment according to an embodiment of the present invention. The multi-load AGV task scheduling method in the intelligent storage environment comprises the following steps:
s1: and acquiring warehouse entry and exit information, wherein the warehouse entry and exit information at least comprises the number of goods to be transported, the number of the multiple-load AGV and the position of a goods shelf where the goods are located.
As shown in fig. 2, the smart warehouse of the present embodiment is composed of components such as shelves, AGVs, loading/unloading platforms, and conveyors. The warehouse management system monitors the operational status of each component and coordinates the pick sequence of each AGV based on the in-out task list. The smart storage of this embodiment includes two columns of racks, each column including 10 racks, and a total of 50 multiple loaded AGVs. The AGV goes to the appointed goods shelf according to the system scheduling instruction to pick goods, goes to the next place to pick goods after the goods are picked, and returns to the sorting platform after all the goods are picked.
First, the present embodiment mathematically models task scheduling optimization to demonstrate performance indicators and optimization objectives. Specifically, Output and Input represent Input/Output stations of different work centers, which are destinations for AGVs to deliver all the goods (cargos), and both Output and Input can be used as Input stations or Output stations. SiDenotes the ith shelf, S ═ SiI 0, 1.., X represents the set of all shelves in the smart warehouse. A. thekDenotes the kth overloaded AGV, A ═ AkI K1, 2.., K represents the set of all overloaded AGVs. v represents the speed of the AGVs, all of which have the same speed in this embodiment. L is the constrained load (maximum load) of each multiple loaded AGV in the system. WjDenotes the jth load to be carried, W ═ WjI j ═ 1, 2.., M } represents the set of goods that need to be handled. M represents the total number of loads that need to be handled, and K represents the maximum available number of current multiple load AGVs. N (A)k) K denotes whether or not the AGV a is selected (0, 1) (K1, 2kWhen the goods are conveyed, the selection is represented by the value of 1, and the non-selection is represented by the value of 0.
Figure BDA0003455651710000091
Is a goods shelf SiWith goods shelves SjThe distance between them. T (A)k) Representing AGV AkThe time of delivery of the cargo. t is tijIndicating that each AGV is transferring goods from rack SiTo the goods shelf SjTime of (d).
Figure BDA0003455651710000092
Indicating AGV AiAnd AGV AjThe number of collisions that occur during the transport. G (A)k) Represents AGVAkThe time to complete all given tasks.
Figure BDA0003455651710000093
Indicating AGV AkThe current load of the vehicle.
Figure BDA0003455651710000094
Indicating AGV AkIn transporting goods WiWhether or not to carry goods Wj，
Figure BDA0003455651710000095
Denotes AgvAkIn transporting goods WiWhether or not to carry goods Wj，
Figure BDA0003455651710000096
Then represents AgvAkIn transporting goods WiNot to carry goods W at the rearj。
Figure BDA0003455651710000097
Representing goods WjWhether or not to be AGV AkThe material is carried and the material is conveyed,
Figure BDA0003455651710000101
representing goods WjBy AGV AkThe material is carried and the material is conveyed,
Figure BDA0003455651710000102
then it represents the goods WjNot received by AGV AkAnd (6) executing.
Figure BDA0003455651710000103
Indicating AGV from load WiShelf at the position of the goods WjThe time required for the shelf at that location. G (A)k) Indicating AGV AkThe time at which all the goods are delivered. RijIs a goods shelf SiTo the goods shelf Sj(ii) a path solution of (x)i,yi) Is a goods shelf SjThe coordinates of (a). Wherein the following relationship exists:
Figure BDA0003455651710000104
Figure BDA0003455651710000105
s2: and performing population initialization according to the warehousing and ex-warehousing information to construct an initial population with N individuals.
First, the number of individuals in an initial population is set, and the gene in each individual includes a randomly generated AGV number AkAnd goods number WjEach bracket indicates the pick sequence for an AGV:
[(Wa,Wb,…,Wc,A1),(Wd,…,We,A2),(…)]
wherein, in the one somatic chromosome, WjDenotes the jth cargo to be transported, AiIndicating the ith AGV.
Illustratively, the following is a randomly generated chromosome sequence of one individual:
[W3,W1,W10,A1,W9,W4,W2,W5,W6,A2,A3,W7,W8,A4]
wherein the given 10 tasks (i.e. acquisitions to be handled) are numbered 1-10, and the goods to be handled are W respectively1-W10Given a maximum number of AGVs of 4, with A1,A2,A3,A4These four AGVs are shown. This chromosome sequence represents the selection of three AGVs (1,2,4) for delivery, AGV "1" having pick sequences of 3, 1, 10, AGV "2" having pick numbers of 9, 4, 2, 5, 6, and AGV "2" having a route from the origin to the positions of items 7, 8 and then to the point of delivery.
Further, each individual chromosome in the initial population also satisfies the following constraints:
first, in a batch task, each load can only be transported once by AGVs, i.e. there are no AGVs transporting the same load, so it is necessary to satisfy (1):
Figure BDA0003455651710000111
where K represents the maximum available number of current multiple load AGVs and M represents the total number of loads that need to be handled.
Each multi-load AGV can take a plurality of goods once, after one goods is taken, only one goods to be taken next is needed, namely after one goods is taken, the next destination of the AGV is determined and independent, and therefore (2) needs to be met:
Figure BDA0003455651710000112
the number of picks per AGV run one trip cannot exceed its maximum capacity, and therefore (3):
Figure BDA0003455651710000113
in addition, in a batch task, the total number of the cargos transported by all AGVs is equal to the total number of the cargos to be transported, so that the following condition (4) needs to be satisfied:
Figure BDA0003455651710000114
therefore, according to the constraint conditions, the individuals which do not meet the conditions are removed, and the initial population can be obtained.
S3: and performing cross and mutation operations on the individuals in the initial population as parent individuals to generate offspring individuals, and combining the offspring individuals with the parent individuals to form a second population with 2N individuals.
In this embodiment, the S3 includes:
s31: and taking the individuals in the initial population as parent individuals to carry out cross operation randomly in pairs to obtain offspring individuals with the same number as the parent individuals.
Referring to fig. 3, fig. 3 is a schematic diagram of an interleaving operation according to an embodiment of the present invention, where S31 of the embodiment includes:
s311: randomly selecting two individuals from the initial population as a first parent individual and a second parent individual, wherein the genes are cargo numbers WjAnd AGV number AkRandomly selecting a goods number or an AGV number from the first parent individual as a first parent designated gene, copying all genes before the first parent designated gene to the corresponding position of a first child chromosome, carrying out inheritance on the goods numbers of the rest part in the first child chromosome according to the goods number sequence of the second parent individual, and deleting the goods numbers of the first child chromosome, wherein the A number of the first child chromosome is a member of the goods numbers of the first child chromosomeThe number of GV numbers is a number randomly selected from the number interval of AGV numbers in the first father dyeing and the second father dyeing, and randomly inserted into the goods numbers, and individuals meeting the initial constraint condition are reserved, so that a first offspring chromosome is formed.
S312: randomly selecting a gene from the second parent individuals as a second parent designated gene, copying all genes before the second parent designated gene to corresponding positions of second child chromosomes, carrying out inheritance on the rest part of the cargo numbers in the second child chromosomes according to the gene sequence of the first parent individuals and deleting the cargo numbers of the second child chromosomes, randomly selecting a quantity from the quantity intervals of the AGV numbers in the first parent dyeing and the second parent chromosomes, randomly inserting the AGV numbers into the cargo numbers, and reserving individuals meeting the initial constraint condition so as to form the second child chromosomes.
S313: and repeating the steps S311-S312, and crossing the remaining parent individuals to obtain the offspring individuals with the same number as the parent individuals.
Specifically, since one task can be completed only by one AGV, that is, one load can be carried only by one AGV, and the AGVs cannot repeatedly complete the same task, the parent chromosomes cannot be hybridized by the conventional method. This example presents a novel crossover method, i.e., randomly selecting a gene (including W) from a parent chromosomeiAnd Aj) All genes preceding the selected gene of the parent chromosome are inherited by the child chromosome, and another part of the genetic chromosome of the child chromosome is inherited by the second parent chromosome.
Illustratively, the specific interleaving procedure is as follows:
two parent chromosomes were randomly selected in the original population:
P1:W3,W1,A1,W9,W4,W2,W5,W6,A2,W7,W8,A3
P2:W9,W7,A1,W5,W3,W6,W8,A2,W1,W4,W2,A3
the two child chromosomes obtained from the two parent chromosomes are:
C1:W3,W1,A1,W9,W7,W2,W5,W6,A2,W8,W4,A3
C2:W9,W7,A1,W5,W3,W1,W4,A2,W2,W6,W8,A3
for the first parent chromosome P1, assume that the 4 th gene W was selected91-4 of the daughter chromosome C1 is identical to the 1-4 of the first parent chromosome P1, and the other part of the daughter chromosome C1 is inherited in the order of the second parent chromosome P2 and the already existing gene W is deleted3,W1,W9. Gene A in daughter chromosome C1jThe total number of (a) is equal to the number of any one gene of the two parent chromosomes. In this example, Gene A in daughter chromosome C1jIs three, followed by gene A2And A3Random insertion into W9In the latter genes, A was randomly selected2Conveying 5 articles, A32 items were shipped, resulting in daughter chromosome C1.
Similarly, for the second parent chromosome P2, assume that the 5 th gene W was selected3The genes at positions 1 to 5 of the daughter chromosome C2 and the genes at positions 1 to 5 of the second parent chromosome P2 are the same, and the other part of the daughter chromosome C2 is inherited in the order of the parent chromosome P1 and the existing genes are deleted. Gene A in daughter chromosome C2jThe total number of (a) is equal to the number of any one gene of the two parent chromosomes. In this example, Gene A in daughter chromosome C2jIs three, followed by gene A2And A3Random insertion into W9In the subsequent genes, daughter chromosome C2 was generated. According to the above-described procedure, i.e. according to the originalTwo parent chromosomes in the population acquire two child chromosomes.
In this example, W is mainly inheritedi(except for the genes already present in the first parent chromosome) in the second parent chromosome, and then randomly inserting the gene Aj(Gene A)iAnd AjThe number of tasks in between is less than the load limit, and the last gene must be Aj). In this embodiment, the probability of crossover is set to 0.8, i.e. 80% of the parent chromosomes in the original population are crossed to obtain the offspring chromosomes, and the genes of the remaining 20% of the parent chromosomes continue to be retained in the offspring chromosomes.
S32: and carrying out mutation operation on the filial generation individuals after the cross operation to obtain the mutated filial generation chromosomes.
Selecting the filial generation individuals after the cross operation, carrying out position exchange on different genes of the filial generation individuals to carry out mutation operation, then checking whether the exchanged individuals meet the initial constraint condition, if so, keeping the filial generation individuals as the mutated filial generation individuals, and if not, abandoning the mutation operation again.
In particular, since each task can only be performed once, i.e. each cargo can only be handled once, two nodes can be selected for exchange. Different genes WiCan be exchanged between them, different genes AiCan also be exchanged between, AiAnd WiThe exchange can be carried out, after the mutation is finished, whether the mutated individuals meet the initial constraint condition needs to be checked, if so, the mutated individuals are retained, and if not, the mutation operation is abandoned again. The variation strategy of this embodiment for the number of AGVs and the sequence of tasks is as follows:
P1＝Wa,Wb,Wc,A1,Wd,We,A2,Wf,Wg,Wh,…
C1＝Wa,Wb,Wc,A1,Wd,Wg,A2,Wf,We,Wh,…
in the example exchange WeAnd Wg. In this embodiment, the mutation probability is set to 0.1, that is, 10% of the parent chromosomes in the updated population are mutated to obtain mutated child chromosomes.
S33: and combining the parent individuals and the variant offspring individuals to form a second population with the number of individuals being twice that of the initial population.
In this embodiment, it is assumed that the original population includes N individuals, and the individuals in the original population form N offspring individuals after cross variation, thereby forming a second population having 2N individuals.
S4: and dividing the individuals in the second population into different levels of disposable layers according to a preset fitness function.
Specifically, the S4 includes:
s41: and respectively setting a first fitness function, a second fitness function and a third fitness function according to the number of the AGVs, the maximum AGV operation time and the conflict among the AGVs.
Firstly, a fitness function is set according to the number of the AGVs, the maximum AGV running time and the conflict among the AGVs, and the fitness function comprises a first fitness function, a second fitness function and a third fitness function. The first fitness function is targeted at optimizing the number of AGVs, and is expressed as:
Figure BDA0003455651710000151
wherein A iskDenotes the k AGV, N (A)k) Indicating whether the K-th AGV is selected to transport the package and K indicates the maximum number of AGVs currently selectable.
The second fitness function objective is to minimize the maximum runtime of the AGV, expressed as:
F2＝min(max(G(Ak)))，k＝1,…,K
wherein A iskDenotes the k-th AGV, G (A)k) Indicates the total time, max (G (A), that the k AGV is transporting the loadk) Denotes all AGsV maximum delivery time of a single AGV.
The third fitness function is to minimize conflicts between different AGVs, and is expressed as:
Figure BDA0003455651710000152
wherein A isiDenotes the ith AGV, AjA jth AGV is indicated,
Figure BDA0003455651710000153
indicating the number of collisions between the ith and jth AGVs.
S42: and sequencing the individuals in the intermediate population by using a rapid non-dominant sequencing algorithm according to the first fitness function, the second fitness function and the third fitness function, and dividing the individuals in the intermediate population into dominant layers of different grades.
Determining a first function value of each individual according to the ex-warehouse information and the first fitness function, namely the value of the first fitness function; determining a second function value of each individual according to the ex-warehouse information and the second fitness function; determining a third function value of each individual according to the ex-warehouse information and the third fitness function; and sequencing the individuals in the initial population by using the first function value, the second function value and the third function value of each individual and a rapid non-dominant sequencing algorithm, and dividing all the individuals in the second population into dominant layers of different levels. Specifically, the first function value, the second function value and the third function value of each individual in the second population are compared with the first function value, the second function value and the third function value of other individuals, and a first parameter and a second parameter of each individual are respectively determined, wherein the first parameter is a set of the dominatable individuals, and the second parameter is the number of individuals dominating each individual in the population.
The specific steps of layering the population by the rapid non-dominated sorting algorithm are as follows:
let i equal 1, for all j equal 1,2iAnd XjBetweenDominant versus non-dominant relationship of, if XiThe value at each objective function is better than XjThen XiDominating XjIf for any one XjIn the absence of XjIs superior to XiThen call XiAnd repeating the above operations for the non-dominant individuals to find all the non-dominant individuals to form a first-level non-dominant layer of the population. Subsequently, the individuals in the first level non-dominant layer are ignored, and the above steps are repeated in the remaining individuals, resulting in a second level non-dominant layer. The second non-dominant layer is again ignored and the above steps are repeated, thereby layering the entire population.
In this embodiment, two parameters n for each individual p in the second population are calculatedpAnd SpWherein n ispNumber of individuals, S, dominating individual p in the populationpIs the set of individuals within the population that are dominated by individual p. Finding out all n in the populationp0 and stored in the set F1In (1), as a first-level non-dominant layer, the rest population individuals form a set F2Calculating the parameter n of each individual p among the remaining individualspAnd SpAnd obtaining a second-level non-dominant layer, and so on.
For set FiIs S, the set of individuals governed by it isiGo through SiEach of l, nl＝nl1, if n islIf 0, the individual l is stored in the set Fi+1In (1). Note FiThe individual obtained in (1) is an individual of a non-dominant layer and is represented by Fi+1The above operation is repeated as a set of operations to be performed next time until the entire population is ranked.
In this embodiment, first two parameters n for each individual p in the second population are calculatedpAnd SpWherein n ispNumber of individuals, S, dominating individual p in the populationpIs the set of individuals within the population that are dominated by individual p. Finding out all n in the populationp0 and stored in the set F1And (4) as a first-level non-dominant layer. Subsequently, neglecting the individuals in the first level non-dominant layer, the parameters of each individual p are calculated in the remaining individualsNumber npAnd SpAnd obtaining a second-level non-dominant layer. The second non-dominant layer is again ignored and the above steps are repeated, thereby layering the entire population.
S5: and carrying out crowding degree calculation on all individuals in the preset disposable layer of the second population to obtain the crowding distance of each individual.
In step S4, the 2N individuals in the second population are divided into different disposable layers, each disposable layer may include different numbers of individuals, and the disposable layers where the nth individual is located are obtained in descending order of the disposable layer rank.
Next, determining the congestion distances of all the individuals in the layer where the nth individual is located, specifically including:
sorting the first function values of all the individuals in the current disposable layer from large to small, setting the congestion distance initial values of the individuals with the minimum first function values and the individuals with the maximum first function values to be infinite, and setting the congestion distance initial values of the other individuals in the disposable layer to be zero;
calculating a first congestion distance for each individual from the initial congestion distance value for each individual and the first function value:
n1＝n0+(f1(i+1)-f1(i-1))/(f1 max-f1 min)
wherein n is0An initial value of a crowding distance, f, representing the individual1(i +1) represents a first function value ranked at the (i +1) th individual, f1(i-1) represents a first function value ranked at the i-1 st individual, f1 maxRepresenting the maximum value of the first function, f1 minThe minimum value of the first function value is represented.
Sorting the second function values of each individual in the current disposable layer from large to small;
calculating a second congestion distance for each individual based on the first congestion distance for each individual and the second function value:
Figure BDA0003455651710000181
wherein n is1Representing a first crowding distance, f, of said individual2(i +1) represents a second function value ranked at the (i +1) th individual, f2(i-1) represents a second function value ranked at the i-1 st individual,
Figure BDA0003455651710000182
the maximum value of the second function value is represented,
Figure BDA0003455651710000183
the minimum value of the second function value is represented.
Sorting the third function values of each individual in the current disposable layer from large to small;
calculating a third crowding distance of each individual according to the second crowding distance of each individual and a third function value, and determining the third crowding distance as the crowding distance of the individual, wherein the third crowding distance is as follows:
Figure BDA0003455651710000184
wherein n is2Indicating a second crowding distance, f, of said individual3(i +1) represents a third function value ranked at the i +1 st individual, f3(i-1) represents a third function value ranked at the i-1 st individual,
Figure BDA0003455651710000185
the maximum value of the third function value is represented,
Figure BDA0003455651710000186
the minimum value of the third function value is represented.
S6: individuals with congestion distances meeting the requirements are retained in the current disposable layer, and a third population of N individuals is formed with the individuals in the top M-1 level disposable layer.
Specifically, all the individuals in the disposable layer where the nth individual is located are rearranged in the order of the congestion distance from large to small, all the individuals in the disposable layer at the level before the disposable layer where the nth individual is located and the individuals with the larger congestion distance in the disposable layer where the nth individual is located are retained, and a third population with N individuals is formed together.
Specifically, assuming that N is 100, that is, N individuals are included in the initial population, then 2N is 200 individuals are included in the second population after the cross mutation, and it is assumed that the 200 individuals are divided into six different levels according to a preset fitness function, and the levels are arranged from low to high, if the first level disposable layer includes 33 individuals, the second level disposable layer includes 38 individuals, the third level disposable layer includes 40 individuals, the fourth level disposable layer includes 29 individuals, the fifth level disposable layer includes 33 individuals, and the sixth level disposable layer includes 27 individuals.
As can be seen from the above-mentioned level information, the first, second, and third level dominable layers include 111 th individuals in total, and the 100 th is located in the third level dominable layer, so that the congestion distances are calculated for all the individuals in the third level dominable layer, and then rearranged in the order of the congestion distances from large to small, and the 29 individuals with larger congestion distances are selected to form a third population having 100 individuals together with the remaining 71 individuals in the first and second level dominable layers. It should be noted that the number of the individual layers, the number of the disposable layers, and the number of the individual layers in each disposable layer are all described by way of example and not by way of limitation.
S7: and repeating the steps S2-S6 by using the third group for a plurality of iterations, and determining the optimal cargo scheduling scheme according to the iteration result when the iteration times reach the preset iteration times.
Specifically, after the first iteration is completed, the gene codes of the individuals in the population, whether the individuals are the whole or the individuals, are changed, and then a new iteration is continued and the number of the algorithm iterations is increased by one. And when the iteration times reach the preset iteration times, after the algorithm cycle is skipped, the number of the AGV with multiple loads and the goods taking sequence of each AGV can be obtained according to the recorded optimal solution scheme.
According to the multi-load AGV task scheduling method under the intelligent storage environment, the number of multi-load AGVs which are not considered in the past is taken into an algorithm, after the ex-warehouse information is obtained, initialization is carried out according to the ex-warehouse information, an initial population is obtained, individuals in the initial population are divided into layers which can be controlled in different levels through a preset fitness function, the task scheduling of the multi-load AGVs in the intelligent storage field is facilitated, the reasonable AGV number and the AGV goods taking sequence are calculated, and the intelligent storage performance is improved; conflict between AGV and the AGV as far as possible through using as far as possible has reduced AGV and has got goods time, suggestion intelligent storage's economic benefits. In addition, according to the multi-load AGV task scheduling method, the distribution strategy of the AGVs can be designed under the condition that the number of the AGVs is insufficient, and the intelligent storage efficiency is improved as much as possible; the service efficiency of the AGV can be evaluated, the no-load condition of the AGV is analyzed, and theoretical support is provided for improving the storage efficiency of the AGV.
Yet another embodiment of the present invention provides a storage medium having a computer program stored therein, the computer program being used for executing the steps of the AGV task scheduling method in the smart storage environment described in the above embodiment. Still another aspect of the present invention provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the AGV task scheduling method in the smart storage environment according to the above embodiment when calling the computer program in the memory. Specifically, the integrated module implemented in the form of a software functional module may be stored in a computer readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable an electronic device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (10)

1. A multi-load AGV task scheduling method under an intelligent storage environment is characterized by comprising the following steps:
s1: acquiring warehouse entry and exit information, wherein the warehouse entry and exit information at least comprises the number of goods to be transported, the number of multi-load AGV and the position of a goods shelf;
s2: performing population initialization according to the warehousing and ex-warehousing information to construct an initial population with N individuals;
s3: performing cross and variation operation on individuals in the initial population as parent individuals to generate offspring individuals and combine the offspring individuals with the parent individuals to form a second population with 2N individuals;
s4: dividing the individuals in the second population into dominant layers of different grades by using rapid non-dominant sorting according to a preset fitness function;
s5: carrying out crowding degree calculation on all individuals in an Mth-level disposable layer of a second population to obtain the crowding distance of each individual, wherein Nth individuals divided according to different levels in the second population are located in the Mth-level disposable layer;
s6: reserving individuals with congestion distances meeting requirements in the current disposable layer, and forming a third population with N individuals from the individuals in the previous M-1 level disposable layer;
s7: and repeating the steps S2-S6 by using the third group for a plurality of iterations, and determining the optimal cargo scheduling scheme according to the iteration result when the iteration times reach the preset iteration times.
2. The method for dispatching multiple AGV tasks in a smart storage environment according to claim 1, wherein said S2 comprises:
setting the number of individuals in the initial population, wherein the gene of each individual comprises an AGV number A generated randomlykAnd goods number WjAnd the individuals in the initial population all satisfy the following four initial constraints:
Figure FDA0003455651700000021
wherein K represents the maximum available number of current multiple-load AGVs, M represents the total number of goods to be handled,
Figure FDA0003455651700000022
representing goods WjWhether or not to be AGV AkCarrying;
Figure FDA0003455651700000023
wherein the content of the first and second substances,
Figure FDA0003455651700000024
indicating AGV AkIn transporting goods WiWhether or not to carry goods Wj，
Figure FDA0003455651700000025
Where L represents the maximum load of each multiple loaded AGV,
Figure FDA0003455651700000026
3. the method for dispatching multiple AGV tasks in a smart storage environment according to claim 2, wherein said S3 comprises:
s31: taking the individuals in the initial population as parent individuals, and performing cross operation on every two individuals to obtain offspring individuals with the same number as the parent individuals;
s32: carrying out mutation operation on the filial generation individuals after the cross operation to obtain mutated filial generation chromosomes;
s33: and combining the parent individuals and the variant offspring individuals to form a second population with the number of individuals being twice that of the initial population.
4. The method for dispatching multiple AGV tasks in a smart storage environment according to claim 3, wherein said S31 comprises:
s311: randomly selecting two individuals from the initial population as a first parent individual and a second parent individual, wherein the genes are cargo numbers WjAnd AGV number AkRandomly selecting a goods number or an AGV number from the first parent individual as a first parent designated gene, copying all genes before the first parent designated gene to corresponding positions of a first child chromosome, carrying out inheritance on the goods numbers of the rest part in the first child chromosome according to the goods number sequence of the second parent individual, deleting the goods numbers of the first child chromosome, randomly selecting a number from the number interval of the AGV numbers in the first parent chromosome and the second parent chromosome, randomly inserting the AGV numbers into the goods numbers, and reserving the individuals meeting the initial constraint condition, thereby forming the first child chromosome.
S312: randomly selecting a gene from the second parent individuals as a second parent designated gene, copying all genes before the second parent designated gene to corresponding positions of second child chromosomes, carrying out inheritance on the rest part of the cargo numbers in the second child chromosomes according to the gene sequence of the first parent individuals and deleting the cargo numbers of the second child chromosomes, wherein the AGV numbers of the second child chromosomes are randomly selected in the interval of the number of the AGV numbers in the first parent dyeing and the second parent chromosomes, randomly inserting the AGV numbers into the cargo numbers, and reserving individuals meeting the initial constraint condition so as to form the second child chromosomes;
s313: and repeating the steps S311-S312, and crossing the remaining parent individuals according to the preset crossing probability so as to obtain the offspring individuals with the same number as the parent individuals.
5. The method for dispatching multiple AGV tasks in a smart warehousing environment according to claim 3, wherein said S32 comprises:
selecting the filial generation individuals after the cross operation, carrying out position exchange on different genes of the filial generation individuals to carry out mutation operation, then checking whether the exchanged individuals meet the initial constraint condition, if so, keeping the filial generation individuals as the mutated filial generation individuals, and if not, abandoning the mutation operation again.
6. The method for dispatching multiple AGV tasks in a smart storage environment according to claim 1, wherein said S4 comprises:
s41: respectively setting a first fitness function, a second fitness function and a third fitness function according to the number of the AGVs, the maximum operation time of the AGVs and the conflict among the AGVs;
s42: and sequencing the individuals in the intermediate population by using a rapid non-dominant sequencing algorithm according to the first fitness function, the second fitness function and the third fitness function, and dividing the individuals in the intermediate population into dominant layers of different grades.
7. The method of claim 6, wherein the first fitness function is targeted to optimize the number of AGVs, and is expressed as:
Figure FDA0003455651700000041
wherein A iskDenotes the k AGV, N (A)k) Indicates the k-th vehicleWhether the AGVs are selected to transport the goods or not, wherein K represents the maximum number of the AGV which can be selected currently;
the second fitness function objective is to minimize the maximum runtime of the AGV, expressed as:
F2＝min(max(G(Ak)))，k＝1,...,K
wherein A iskDenotes the k-th AGV, G (A)k) Indicates the total time, max (G (A), that the k AGV is transporting the loadk) Represents the maximum shipment time of a single AGV among all AGVs.
The third fitness function is to minimize conflicts between different AGVs, and is expressed as:
Figure FDA0003455651700000042
wherein A isiDenotes the ith AGV, AjA jth AGV is indicated,
Figure FDA0003455651700000043
indicating the number of collisions between the ith and jth AGVs.
8. The method for dispatching multiple AGV tasks in a smart storage environment according to claim 1, wherein said S5 comprises:
sorting the first function values of all the individuals in the current disposable layer from large to small, setting the congestion distance initial values of the individuals with the minimum first function values and the individuals with the maximum first function values to be infinite, and setting the congestion distance initial values of the other individuals in the disposable layer to be zero;
calculating a first congestion distance of each individual according to the initial congestion distance value of each individual and the first function value;
sorting the second function values of each individual in the current disposable layer from large to small;
calculating a second congestion distance of each individual according to the first congestion distance of each individual and the second function value;
sorting the third function values of each individual in the current disposable layer from large to small;
and calculating a third crowding distance of each individual according to the second crowding distance of each individual and the third function value, and determining the third crowding distance as the crowding distance of the individual.
9. The method of claim 8, wherein the first congestion distance is calculated by the following formula:
n1＝n0+(f1(i+1)-f1(i-1))/(f1 max-f1 min)
wherein, for each level of disposable layers, n0An initial value of a crowding distance, f, representing the individual1(i +1) represents a first function value ranked at the (i +1) th individual, f1(i-1) represents a first function value ranked at the i-1 st individual, f1 maxRepresenting the maximum value of the first function, f1 minA minimum value representing the first function value;
the calculation formula of the second congestion distance is as follows:
Figure FDA0003455651700000051
wherein, for each level of disposable layers, n1Representing a first crowding distance, f, of said individual2(i +1) represents a second function value ranked at the (i +1) th individual, f2(i-1) represents a second function value ranked at the i-1 st individual,
Figure FDA0003455651700000052
the maximum value of the second function value is represented,
Figure FDA0003455651700000053
a minimum value representing the second function value;
the calculation formula of the third congestion distance is as follows:
Figure FDA0003455651700000061
wherein, for each level of disposable layers, n2Indicating a second crowding distance, f, of said individual3(i +1) represents a third function value ranked at the i +1 st individual, f3(i-1) represents a third function value ranked at the i-1 st individual,
Figure FDA0003455651700000062
the maximum value of the third function value is represented,
Figure FDA0003455651700000063
the minimum value of the third function value is represented.
10. A storage medium, characterized in that the storage medium has stored therein a computer program for executing the steps of the method for scheduling multiple load AGV tasks in a smart storage environment according to any of claims 1 to 9.
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