CN108876090A - A kind of AGV cooperates with Optimization Scheduling with process unit - Google Patents

A kind of AGV cooperates with Optimization Scheduling with process unit Download PDF

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CN108876090A
CN108876090A CN201810358871.9A CN201810358871A CN108876090A CN 108876090 A CN108876090 A CN 108876090A CN 201810358871 A CN201810358871 A CN 201810358871A CN 108876090 A CN108876090 A CN 108876090A
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钱晓明
楼佩煌
孙颖
张硕
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Nanjing University of Aeronautics and Astronautics
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Abstract

The present invention discloses the Optimization Scheduling that cooperates with of a kind of AGV and process unit, including：Establish the Flexible workshop scheduling model using AGVS and hybrid genetic algorithm on multiple populations, algorithm is divided into coding and decoding, generates multiple initial populations, fitness value is calculated in conjunction with heuristic A GV scheduling rule, selection intersects, variation, it is realized by immigrant's operator and is interconnected between population, elite population is retained using elitism strategy, neighborhood search is carried out to elite population.The present invention uses process+process unit coding mode, and random search obtains initial schedule sequence；It needs to combine heuristic A GV scheduling rule when fitness value calculation.By Multiple-population Genetic Algorithm in conjunction with neighborhood search, the diversity of population can be improved and improve the local search ability of population.The present invention dispatches while realization by the algorithm to AGV and process unit, can be efficiently applied in the flexible job shop scheduling using AGVS.

Description

A kind of AGV cooperates with Optimization Scheduling with process unit
Technology neighborhood：
Optimization Scheduling is cooperateed with the present invention relates to a kind of AGV and process unit, belongs to solving job shop scheduling problem technology Using neighborhood.
Background technique：
High flexibility and fast-response time have become the essential characteristic of modern manufacturing system, the system of customer requirement various products Make cycle time.It is competing that flexible manufacturing system (Flexible Manufacturing System, FMS) is proved to be the reply whole world The correct selection of challenge is striven, performance depends greatly on the suitable scheduling strategy of selection.Flexible job shop scheduling Problem (Flexible Job-Shop Scheduling Problem, FJSP) is regarded as np hard problem from proposition, obtains The extensive research of domestic and international expert, each workpiece have multi-process routes, and multi-processing equipment selection, which increase scheduling Flexibility, but also it is more conform with the actual conditions of production.But with the development of industry, only using process unit, workpiece as scheduling The considerations of object, be it is insufficient, " transportation system " just become FMS have increasing need for concern the problem of.
Automatic Guided Vehicles System (Automatic guided vehicle System, AGVS) is used as a kind of flexible height The logistics transportation system of effect, is more and more used in manufacturing shop.It is many for the Solid Warehouse in Flexible Manufacturing Workshop using AGVS Scholar studies a question using product scheduling and transportation system as independent, but is interactional between the two undeniably. Individual FJSP goal in research is usually such that Maximal Makespan is most short, and this time is in the flexible manufacturing using AGVS Inherently influenced by the haulage time of AGV in workshop, and it is domestic for such issues that research it is less.Document " Xiao Haining, Lou Peihuang, Yan Weiguo wait machine and automatic guided vehicle on-line scheduling method [J] agricultural mechanical journal in flexible job shop, 2013,44(4):The problem is divided into two sub-problems and solved by 280-286. ", i.e. process unit selection subproblem and AGV scheduling Subproblem provides resolution policy respectively, has ignored connection between the two." Cheng Zian, Tong Ying, Shen Lijuan wait bis- kinds of to document Group's hybrid genetic algorithm solves Flexible Job-shop Scheduling Problems [J] computer engineering and design, 2016,37 (6):1636- Double Population Genetic Algorithms are introduced into FJSP and achieve preferable research achievement by 1642. ", but there is no researchs with AGVS's FJSP.Document " grind by Flexible workshop scheduling optimizing research [J] computer application that Xu Yunqin, Ye Chunming, Cao Lei contain AGV Study carefully, 2018,35 (11):It is preferential to publish " using improvement PSO Algorithm problem, but its coding mode is complex.Cause This, inquires into the Solid Warehouse in Flexible Manufacturing Workshop using AGVS, and AGV cooperates with Optimized Operation effective ways to become urgently with process unit Critical issue to be solved.
Summary of the invention：
It in view of the deficiencies of the prior art and needs, what the present invention provided a kind of AGV and process unit cooperates with Optimized Operation side Method realizes the Solid Warehouse in Flexible Manufacturing Workshop job scheduling for using AGVS.
The present invention adopts the following technical scheme that：A kind of AGV cooperates with Optimization Scheduling with process unit, it is characterised in that Include the following steps：
Step 1 is established based on the Solid Warehouse in Flexible Manufacturing Workshop operation mathematical model for using AGVS；
Step 2 solves the mathematical model that step 1 is established with improved hybrid genetic algorithm on multiple populations；
Further, Mathematical Modeling Methods are：It suppose there is n workpiece to be processed Ji(i=1,2 ..., n), are processed in m platform M is processed on equipmentj(j=1,2 ..., m), workpiece JiBy NiProcedure forms Oi,l(l=1,2 ..., n_i) has elder generation between process The constraint relationship afterwards, each workpiece has a plurality of machining process route it can be selected that every procedure Oi,lIt may be selected in m platform process unit Multiple devices processing, with process unit si,j∈{M1,M2,...,MmIndicate process Oi,lThe process unit of selection, pi,jIndicate work Sequence Oi,lIn process unit si,jOn process time.AGV is needed to transport between process unit, z AGV Vk(k=1,2 ..., Z),Indicate JiA workpiece uses VkAGV is from process unit si,jTo process unit si,j+1Haulage time.It need to adopt With it is assumed hereinafter that：
(1) every process unit can only process a workpiece every time, and once processing, cannot interrupt.Each workpiece It can only be processed by a process unit in the same time；
(2) AGV quantity it is known that and every AGV can only transport a workpiece every time, run with fixed rate；
(3) enter each work station output buffer after one procedure of the every completion of workpiece, wait scheduling system under its distribution One procedure process unit and transport AGV；
(4) AGV for having executed task rests on the process unit for just having executed task nearby, but it is logical not influence other AGV Row；
(5) process unit handle the time of each workpiece be determine and it is known, time be included in processing the time in；
(6) haulage time between any two points of workshop is known；
(7) process unit inputoutput buffer is considered unlimited；
(8) ignore process unit and AGV failure；
(9) defaulting every AGV trolley will not collide in operation.
Goal in research is to find feasible scheduling scheme under above-mentioned constraint condition, so that Maximal Makespan is most short.
min{maxCi, i=1,2 ..., n (1)
C in formulaiIndicate workpiece JiCompletion date, i.e. its last procedure Oi,n_iThe time of completion.The completion of workpiece Time includes process time and haulage time.
In formula, haulage timeJ ∈ (1,2 ..., n_i-1), including two parts load time and idle time.
In formulaIndicate JiA workpiece uses VkAGV is from process unit si,jTo process unit si,j+1Load Time,
Indicate JiA workpiece uses VkAGV is from process unit si,jTo process unit si,j+1Zero load when Between.
Further, steps are as follows for improved hybrid genetic algorithm on multiple populations：
(1) it is encoded by the way of process+process unit, multiple initial populations is randomly generated；
(2) each population is based on basic genetic algorithmic and heuristic A GV scheduling rule is evolved, setting immigrant's operator, realization kind Group's interconnection；
(3) elite population is formed using elitism strategy；
(4) neighborhood search is carried out to elite population；
(5) judge whether algorithm terminates, according to termination condition, if reaching condition terminates to optimize；Otherwise (2) continuation is gone to step It executes.
Further, heuristic A GV scheduling rule is：
(1) any regular：More than one of currently available vehicle, and all at the same place；
(2) shortest path rule：More than one of currently available vehicle, and not at the same place, since AGV speed is solid It is fixed,
Path length can be substituted by haulage time, be calculated reference formula (3).
Two kinds of regular randoms use, and specifically used steps are as follows：
Step 1 is decoded according to initial population, generates workpiece process；
Step 2 is according to process, from left to right, determines whether the process needs AGV to transport, and if being, continues, otherwise turns to walk Rapid 3；
2.1 determine the quantity of current idle AGV and position, and selection scheduling rule specifies the AGV carried；
2.2 mobile selected AGV point pickups from current location to workpiece, then arrive processing stand；
After 2.3 reach processing stand, workpiece is put down, is parked at processing stand, other AGV is not interfered to run；
Step 3 determination is last procedure, if being, completes AGV distribution, otherwise return step 2.
Further, the operator setting method of basic genetic algorithmic is：Selection operator uses roulette method, and crossover operator is adopted It is more in order to explore since chromosome includes process part and process unit selected section with more former generation's crossover mechanisms Space, crossover operation carry out twice, are operated to process part, are carried out to process unit selected section Operation.Mutation operator uses cross and variation, migrates operator for the optimum individual in the worst individual source population in target population Instead of.
Further, neighborhood search mode is insertion and exchange.The concrete operations of insertion：Randomly choose a process dyeing Body inserts it into any other positions in process chromosome, and the corresponding process unit of this process, is changed to process time The smallest process unit, other are constant.The concrete operations of exchange：Randomly choose any two position in process chromosome, work Sequence swaps, and the corresponding process unit of two-step is changed to process time the smallest process unit, other are constant.Neighborhood is searched Suo Hou calculates fitness value, if before great-than search, then population is changed, otherwise constant.
Detailed description of the invention：
Fig. 1 is the flow chart of the method for the present invention；
Fig. 2 is the machining information figure of application of the present invention；
Fig. 3 is test case field layout figure of the present invention；
Fig. 4 is test case field layout respective transfer time diagram of the present invention；
Fig. 5 is process crossover operation figure of the present invention；
Fig. 6 is mutation operation figure of the present invention；
Fig. 7 is the experimental result of test case；
Fig. 8 is the scheduling Gantt chart of test case 1；
Fig. 9 is test case contrast and experiment.
Specific implementation method：
Optimization Scheduling is cooperateed with the present invention provides a kind of AGV and process unit, below with reference to flow chart to this hair Bright embodiment is described in detail, so that advantages and features of the invention can be easier to be understood.
The method of the present invention is subjected to simulating, verifying on PC, programming software used is Matlab 2014b.
The important technological parameters for testing environment are as follows：
Operating system：Microsoft Windows 7, processor：Core (TM) i5-4570, dominant frequency：3.2GHz, memory： 8G。
Control parameter is as follows：
Population number is 10, and each population scale is 40, crossing-over rate PcIt is randomly generated between [0.7-0.9], aberration rate Pm? It is randomly generated between [0.1-0.5], it is 10 that optimum individual, which at least keeps algebra,；AGV quantity is 2.
Test case data derives from document " Kumar M.V.Satish, Janardhana Ranga, Rao C.S.P.Simultaneous scheduling of machines and vehicles in an FMS environment with alternative routing[J].The International Journal of Advanced Manufacturing Technology,2011,53(1):339-351. ", as shown in Fig. 2, Fig. 3 and Fig. 4.
Specific implementation step is as follows：
Firstly, carrying out mathematical modeling to the flexible manufacturing shop problem for using AGVS, the present invention is according to test case data It suppose there is 5 workpiece to be processed Ji, M is processed on 4 process unitsj, workpiece JiO is formed by 2 or 3 proceduresi,l, process Between have successive the constraint relationship, each workpiece has a plurality of machining process route it can be selected that every procedure Oi,l4 processing may be selected More equipment processing in equipment, with process unit si,j∈{M1,M2,...,M4Indicate process Oi,lThe process unit of selection, pi,j Indicate process Oi,lIn process unit si,jOn process time.AGV is needed to transport between process unit, 2 AGV Vk(k=1, 2),Indicate JiA workpiece uses VkAGV is from process unit si,jTo process unit si,j+1Haulage time.To institute The system of research makes the following assumptions：
(1) every process unit can only process a workpiece every time, and once processing, cannot interrupt.Each workpiece It can only be processed by a process unit in the same time；
(2) AGV quantity it is known that and every AGV can only transport a workpiece every time, run with fixed rate；
(3) enter each work station output buffer after one procedure of the every completion of workpiece, wait scheduling system under its distribution One procedure process unit and transport AGV；
(4) AGV for having executed task rests on the process unit for just having executed task nearby, but it is logical not influence other AGV Row；
(5) process unit handle the time of each workpiece be determine and it is known, time be included in processing the time in；
(6) haulage time between any two points of workshop is known；
(7) process unit inputoutput buffer is considered unlimited；
(8) ignore process unit and AGV failure；
(9) defaulting every AGV trolley will not collide in operation.
Goal in research is to find feasible scheduling scheme under above-mentioned constraint condition, so that Maximal Makespan is most short.
min{maxCi, i=1,2 ..., 5 (1)
C in formulaiIndicate workpiece JiCompletion date, i.e. its last procedure Oi,n_iThe time of completion.The completion of workpiece Time includes process time and haulage time.
In formula, haulage timeJ ∈ (1,2 ..., n_i-1), including two parts load time and idle time.
In formulaIndicate JiA workpiece uses VkAGV is from process unit si,jTo process unit si,j+1Load Time,Indicate JiA workpiece uses VkAGV is from process unit si,jTo process unit si,j+1Idle time.
Consider coding and decoding complexity and process between successive the constraint relationship, the present invention using a kind of process+plus The standby coding mode of tooling.If workpiece to be processed number is n, workpiece niManufacturing procedure be mjWhen, chromosome length is2 times of i.e. total process.By taking test case data as an example, workpiece to be processed number is 5, and chromosome length 26 is false If given chromosome [2 153122134435311322231112 1], then chromosome First half indicates processing sequence of all workpiece on process unit, i.e. process chromosome, the of first 1 expression workpiece 1 One procedure, the second operation work of second 1 expression workpiece 1, third 1 indicate the third procedure of workpiece 1.Workpiece 2,3, 4,5 similarly.1 indicates to correspond to the position of the optional process unit table of workpiece, i.e. process unit chromosome, such as work in latter half First procedure of part 3, optional process unit are M1、M3、M4, then its process unit table is [1 3 4], and 2 corresponding are at this time Process unit M3。
The present invention generates multiple initial populations using random search mode, each initial population according to basic genetic algorithmic into Change.Firstly the need of progress fitness value calculation when evolution.Due to only considered process unit and workpiece in chromosome, not Consider therefore the scheduling of AGV when calculating fitness value, needs to start AGV scheduling rule.The present invention is using two kinds of scheduling rule Then, it randomly chooses：
(1) any regular：More than one of currently available vehicle, and all at the same place；
(2) shortest path rule：More than one of currently available vehicle, and not at the same place, since AGV speed is solid Fixed, path length can be substituted by haulage time, be calculated reference formula (3).
Steps are as follows for heuristic A GV scheduling rule：
Step 3 is decoded according to initial population, generates workpiece process；
Step 4 is according to process, from left to right, determines whether the process needs AGV to transport, and if being, continues, otherwise turns to walk Rapid 3；
2.1 determine the quantity of current idle AGV and position, and selection scheduling rule specifies the AGV carried；
2.2 mobile selected AGV point pickups from current location to workpiece, then arrive processing stand；
After 2.3 reach processing stand, workpiece is put down, is parked at processing stand, other AGV is not interfered to run；
Step 3 determination is last procedure, if being, completes AGV distribution, otherwise return step 2.
The present invention relates to the selection about genetic algorithm operator part it is as follows：Selection operator uses roulette method, hands over It pitches operator and uses more former generation's crossover mechanisms, since chromosome includes process part and process unit selected section, in order to visit The more spaces of rope, crossover operation carry out twice, are operated to process part, are selected process unit Part is operated.Mutation operator uses cross and variation, and immigrant's operator will be in the worst individual source population in target population Optimum individual replaces.
Finally, carrying out selection using elitism strategy forms elite population, neighborhood search is carried out to elite population.Neighborhood search Mode is insertion and exchange.The concrete operations of insertion：A process chromosome is randomly choosed, process chromosome is inserted it into In any other positions, and the corresponding process unit of this process is changed to process time the smallest process unit, other are constant. The concrete operations of exchange：Any two position in process chromosome is randomly choosed, process swaps, and two-step is corresponding Process unit be changed to process time the smallest process unit, other are constant.After neighborhood search, fitness value is calculated, if greatly Before search, then population is changed, otherwise constant.
The method of the present invention carries out emulation experiment on matlab, uses corresponding fortune in layout 1 and Fig. 4 in Fig. 3 first The defeated time finds out optimal scheduling in conjunction with Fig. 2, and optimal result is as shown in Figure 8 and Figure 9.
To verify validity of the method for the present invention under various layouts, carry out multiple comparison test, as a result as shown in figure 9, Wherein question number first digit indicates workpiece and process unit data, the second digital representation layout.It, can be with by analysis See, the solution quality that the method for the present invention is found out is higher.
The above description is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all utilizations Description of the invention and accompanying drawing content the method are applied directly or indirectly in other relevant technology neighborhoods, similarly wrap It includes in scope of patent protection of the invention.

Claims (8)

1. a kind of AGV cooperates with Optimization Scheduling with process unit, it is characterised in that include the following steps：
Step 1 is established based on the Solid Warehouse in Flexible Manufacturing Workshop operation mathematical model for using AGVS；
Step 2 solves the mathematical model that step 1 is established with improved hybrid genetic algorithm on multiple populations.
2. a kind of AGV according to claim 1 cooperates with Optimization Scheduling with process unit, which is characterized in that step 1 Described in mathematical model be：
Under constraint condition, feasible scheduling scheme is found, so that Maximal Makespan is most short,
min{maxCi, i=1,2 ..., n (1)
C in formulaiIndicate workpiece JiCompletion date, i.e. its last procedure Oi,n_iThe time of completion, the completion date packet of workpiece Include process time and haulage time；
In formula, haulage timeJ ∈ (1,2 ..., n_i-1), including two parts load time and idle time,
In formulaIndicate JiA workpiece uses VkAGV is from process unit si,jTo process unit si,j+1Load time,Indicate JiA workpiece uses VkAGV is from process unit si,jTo process unit si,j+1Idle time.
3. a kind of AGV according to claim 2 cooperates with Optimization Scheduling with process unit, which is characterized in that setting There is n workpiece to be processed Ji(i=1,2 ..., n), process M on m platform process unitj(j=1,2 ..., m), workpiece JiBy Ni Procedure forms Oi,l(l=1,2 ..., n_i) has successive the constraint relationship between process, and each workpiece has a plurality of machining process route It can be selected that every procedure Oi,lMultiple devices processing in m platform process unit may be selected, with process unit si,j∈{M1,M2,..., MmIndicate process Oi,lThe process unit of selection, pi,jIndicate process Oi,lIn process unit si,jOn process time；Process unit Between need AGV to transport, z AGV Vk(k=1,2 ..., z),Indicate JiA workpiece uses VkAGV is from processing Equip si,jTo process unit si,j+1Haulage time；It is scheduled optimization under the following conditions：
(1) every process unit can only process a workpiece every time, and once processing, not interrupt；Each workpiece is same Time can only be processed by a process unit；
(2) AGV quantity it is known that and every AGV can only transport a workpiece every time, run with fixed rate；
(3) enter each work station output buffer after one procedure of the every completion of workpiece, wait scheduling system lower together for its distribution Process process unit and transport AGV；
(4) AGV for having executed task rests on the process unit for just having executed task nearby, but it is current not influence other AGV；
(5) process unit handle the time of each workpiece be determine and it is known, time be included in processing the time in；
(6) haulage time between any two points of workshop is known；
(7) process unit inputoutput buffer is considered unlimited；
(8) ignore process unit and AGV failure；
(9) defaulting every AGV trolley will not collide in operation.
4. a kind of AGV according to claim 1 cooperates with Optimization Scheduling with process unit, which is characterized in that step 2 Improvement hybrid genetic algorithm on multiple populations steps are as follows：
Step 2.1 encodes in such a way that process and process unit combine, and multiple initial populations are randomly generated；
Each population of step 2.2 is based on basic genetic algorithmic and heuristic A GV scheduling rule is evolved, setting immigrant's operator, realization kind Group's interconnection；
Step 2.3 forms elite population using elitism strategy；
Step 2.4 carries out neighborhood search to elite population；
Step 2.5 judges whether algorithm terminates, according to termination condition, if reaching condition terminates to optimize；Otherwise 2.2 continuation are gone to step It executes.
5. a kind of AGV according to claim 4 cooperates with Optimization Scheduling with process unit, which is characterized in that step Heuristic A GV scheduling rule in 2.2 is：
A. any regular：More than one of currently available vehicle, and all at the same place；
B. shortest path rule：More than one of currently available vehicle, and not at the same place, since AGV speed is fixed, road Electrical path length can be substituted by haulage time, be calculated identical as the formula (3).
6. a kind of AGV according to claim 5 cooperates with Optimization Scheduling with process unit, which is characterized in that described Heuristic A GV scheduling rule regular random uses, and specifically used steps are as follows：
Step 2.2.1 is decoded according to initial population, generates workpiece process；
Step 2.2.2 is according to process, from left to right, determines whether the process needs AGV to transport, and if being, continues, otherwise turns to walk Rapid step 2.2.3；
2.2.2.1 quantity and the position of current idle AGV are determined, selection scheduling rule specifies the AGV carried；
2.2.2.2 selected AGV point pickup from current location to workpiece is moved, then arrives processing stand；
2.2.2.3 after reaching processing stand, workpiece is put down, is parked at processing stand, other AGV is not interfered to run；
Step 2.2.3 determination is last procedure, if being, completes AGV distribution, otherwise return step 2.2.2.
7. a kind of AGV according to claim 4 cooperates with Optimization Scheduling with process unit, which is characterized in that step 2.2 the operator of middle basic genetic algorithmic is set as：Selection operator uses roulette method, and crossover operator uses more former generation's intersecting machines System, crossover operation twice, be once to operate to process part, grasped to process unit selected section Make.Mutation operator uses cross and variation, migrates operator for the optimum individual generation in the worst individual source population in target population It replaces.
8. a kind of AGV according to claim 4 cooperates with Optimization Scheduling with process unit, which is characterized in that step Neighborhood search mode is insertion and exchange in 2.4；
The concrete operations of insertion：A process chromosome is randomly choosed, any other positions in process chromosome are inserted it into, And the corresponding process unit of this process is changed to process time the smallest process unit, other are constant；
The concrete operations of exchange：Any two position in process chromosome is randomly choosed, process swaps, and two-step pair The process unit answered is changed to process time the smallest process unit, other are constant；
After neighborhood search, fitness value is calculated, if before great-than search, then population is changed, otherwise constant.
CN201810358871.9A 2018-04-20 2018-04-20 A kind of AGV cooperates with Optimization Scheduling with process unit Pending CN108876090A (en)

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