CN107133703A - A kind of online batch processing method of incompatible workpiece group based on requirement drive - Google Patents
A kind of online batch processing method of incompatible workpiece group based on requirement drive Download PDFInfo
- Publication number
- CN107133703A CN107133703A CN201710405175.4A CN201710405175A CN107133703A CN 107133703 A CN107133703 A CN 107133703A CN 201710405175 A CN201710405175 A CN 201710405175A CN 107133703 A CN107133703 A CN 107133703A
- Authority
- CN
- China
- Prior art keywords
- msub
- mrow
- state
- action
- decision
- 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.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 11
- 238000004519 manufacturing process Methods 0.000 claims abstract description 61
- 238000012545 processing Methods 0.000 claims abstract description 36
- 238000000034 method Methods 0.000 claims abstract description 18
- 230000005012 migration Effects 0.000 claims abstract description 9
- 238000013508 migration Methods 0.000 claims abstract description 9
- 238000003754 machining Methods 0.000 claims abstract description 4
- 230000009471 action Effects 0.000 claims description 36
- 238000003860 storage Methods 0.000 claims description 6
- 230000004044 response Effects 0.000 claims description 5
- 230000003139 buffering effect Effects 0.000 claims 1
- 239000002699 waste material Substances 0.000 abstract description 4
- 230000008569 process Effects 0.000 abstract description 3
- 238000004378 air conditioning Methods 0.000 abstract 1
- 230000000875 corresponding effect Effects 0.000 description 4
- 239000011521 glass Substances 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 229960000074 biopharmaceutical Drugs 0.000 description 2
- 239000011248 coating agent Substances 0.000 description 2
- 238000000576 coating method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000002184 metal Substances 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- 238000010923 batch production Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 238000007514 turning Methods 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
- 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
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- 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
- G06Q10/0633—Workflow analysis
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Manufacturing & Machinery (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of online batch processing method of incompatible workpiece group based on requirement drive, including：1st, the on-line machining process description that workpiece is reached at random first is semi-Markov decision processes, and initializes the control scheme list of online production system；2nd, k=1 is made, Q value tables are initialized；3rd, k-th of decision-making moment, the current state of observing system, and be processed according to control scheme list control system；4th, the accumulative cost produced in state migration procedure is calculated, and by difference formula and Q values more new formula, updates Q value tables；5th, control scheme list is updated by Q values table；6th, k+1 is assigned to k, and return to step 3, controls online production system to be processed with the control scheme list after renewal, untill the control scheme list no longer changes.The present invention can carry out effective vehicle air-conditioning to the production and processing system under requirement drive, so as to reduce the waste of the resources of production and inventory space while stochastic demand is met.
Description
Technical field
The invention belongs to production scheduling technical field, specifically a kind of incompatible workpiece group based on requirement drive exists
Line batch processing method.
Background technology
Scheduling plays important role in most of manufacturing systems and information processing environment.Manufacturing
In industry, rational scheduling scheme can greatly improve productivity effect and resource utilization, production cost be saved, so as to strengthen enterprise
Competitiveness, promote manufacture industry development.The batch processing problem of incompatible workpiece group belongs to scheduling problem, and it is deposited extensively
It is in production and living, such as steel production, bio-pharmaceuticals, metal coating and cargo transport.It is wide due to production scheduling problems
General property and importance, domestic and foreign scholars are conducted in-depth research to the field；But, past research is concentrated mainly on workpiece
The problem of machining information is determined.Lozano et al. is using security protection glass manufacture as research background, preheating, processing for security protection glass
Control is optimized with the batch process such as cooling, wherein identical degree of protection treats that product belongs to compatible workpiece group, and
Assuming that the processing time of workpiece determines to understand, document Lozano A J, MedagliaA.Scheduling ofparallel are seen
machines with sequence-dependentbatches andproduct incompatibilities in an
automotive glass facility[J].Journal ofScheduling,2014,17(6):521-540。
Due to the scheduling problem in real production environment be typically multiple constraint, multiple target, it is uncertain excellent with randomness
Change problem.The scheduling scheme obtained by deterministic schedule method applies the real production environment for having randomness in schedule information
In, production system will be caused to run steadily in the long term, waste and the inferior phenomenon of low production efficiency is processed.It in addition, there will be life
Production system is generally to maximize system production efficiency as target；But in modernization market, customer or downstream manufacturers are to product
With personalized and Random demand, so production capacity surplus will occur in the simple enterprise for turning to target with production efficiency maximum
With production wasting phenomenon.
The content of the invention
The present invention is that, to solve the weak point that above-mentioned prior art is present, proposition is a kind of based on the incompatible of requirement drive
The online batch processing method of workpiece group, to which effective on-line optimization control can be carried out to the production and processing system under requirement drive
System, so as to reduce the waste of the resources of production and inventory space while stochastic demand is met.
The present invention adopts the following technical scheme that to solve technical problem：
A kind of the characteristics of of the invention incompatible workpiece group based on requirement drive online batch processing method be applied to by
In the online production system that conveyer belt, workpiece, automatic transportation equipment, caching storehouse, batch processing machine and warehouse for finished product are constituted；Described
In online production system, workpiece is reached at random, and is stored in the buffer pool and is waited the batch processing machining, is processed
Into Workpiece storage in the warehouse for finished product；Assuming that respectively there are m incompatible workpiece groups in the buffer pool and warehouse for finished product, institute is made
State and belong to the Number of Jobs of j-th of incompatible workpiece group in buffer pool for bfj, belong to j-th of incompatible work in the warehouse for finished product
The Number of Jobs of part group is bkj；Respectively take that a workpiece formed from the m incompatible workpiece groups of the warehouse for finished product it is a set of into
Product can respond the demand reached at random；The online batch processing method of incompatible workpiece group based on requirement drive is
Carry out as follows：
Step 1, define the state space Φ of the online production system=s | s=(bf1,bf2,…,bfm,bk1,
bk2,…,bkm), wherein, s is any state in the state space Φ, s ∈ Φ；
The actionable space for defining the online production system is D={ v0,v1,…,vj,…,vm}；v0Represent the batch processing
Machine waits a workpiece to reach in buffer pool, vjRepresent that described j-th of incompatible workpiece group of batch processing machine choice is added
Work；
The decision-making moment for defining the online production system is the batch processing machine idle, and has a workpiece to reach institute
At the time of stating buffer pool, or at the time of completing processing for the batch processing machine；
Step 2, the action taken using the online production system under all historic states initialize control plan
Sketch form；
Step 3, defined variable k, and k=1 is initialized, Boltzmann constant K and temperature T is set；
It is State-Action to being worth to define the element in Q value tables, and the element initialized in the Q values table is " 0 "；
Step 4, k-th of decision-making moment in the online production system, observe the current shape of the online production system
State is simultaneously designated as sk, make the current state s at k-th of decision-making momentkCorresponding state is designated as s ' in Q value tables, then sk=
s′；Make the current state s at k-th of decision-making momentkUnder the action taken be designated as K-th decision-making moment
Current state skUnder, any action v is selected from actionable space Dr, and with probabilityVr is assigned to
With probabilityBy vs′It is assigned toWherein, Q (s ', vr) represent k-th of decision-making moment state sk=
Take action v during s 'rState-Action to value；Q(s′,vs′) represent k-th of decision-making moment state skTaken action under=s '
vs′State-Action to value；Take actionAfterwards, current state s of the system at+1 decision-making moment of kthkIt is transferred to next shape
State sk+1, by NextState sk+1Corresponding state is designated as s " in Q value tables, then sk+1=s "；Wherein, s ', s " ∈ Φ；
Step 5, the current state s of the online production system from k-th of decision-making moment is calculated using formula (1)k, take row
It is dynamicIt is transferred to the state s at+1 decision-making moment of kthk+1State migration procedure in the accumulative cost that produces
In formula (1), N represents to produce the sum of stochastic demand in the state migration procedure；R represents that the state was shifted
The number of times that stochastic demand meets with a response in journey, and have R=min (min (bk1,bk2,…,bkm),N)；W represents that a demand is not obtained
To the punishment cost produced by response；LjRepresent the cost of j-th of incompatible workpiece group of unit interval storage in the warehouse for finished product；
ΔtiThe time interval of ith demand and i+1 time demand is represented, k-th of decision-making moment and i+1 time demand are represented during i=0
Time interval, the R times demand and the time interval at+1 decision-making moment of kth are represented during i=R；
Step 6, using the difference formula and Q values more new formula shown in formula (2) and formula (3), update in the Q values table currently
State skUnder take actionState-Action to valueCurrent state s after being updatedkUnder take action's
State-Action is to value
In formula (2), Δ Tk,k+1Represent the time interval at k-th of decision-making moment and+1 decision-making moment of kth；Represent the
Institute is stateful before k decision-making moment takes action v when being s 's′It is transferred to the accumulative cost that is produced during NextState s "
Mean estimates；vrRepresent any action in the actionable space D；Q(sk+1,vr) represent to be transferred to+1 decision-making moment of kth
State sk+1Under take any action vrState-Action to value；
In formula (3),For the current state s at k-th of decision-making momentkUnder take actionLearning Step；
Often the minimum State-Action of row constitutes current line to the action corresponding to value in Q value tables after step 7, selection update
Dynamic set, and update the control scheme list using current action collection；
Step 8, k+1 is assigned to k, and return to step 4, untill the control scheme list no longer changes, so that with
Final control scheme list carries out online batch processing to m incompatible workpiece groups.
Compared with prior art, the beneficial effects of the present invention are：
1st, the present invention is learnt to maximize satisfactory rate of information demand and the storage cost for minimizing workpiece as optimization aim by Q
Method optimizes control to the online batch processing problem of incompatible workpiece group；Compared to merely to maximize production efficiency as mesh
Target is produced and processed, and the present invention reduces the waste of the resources of production and inventory space while stochastic demand is met.
2nd, the present invention is using buffer pool and warehouse for finished product as the united state of online production system；With batch processing machine idle, and
At the time of having a workpiece arrival buffer pool, or it is the decision-making moment that batch processing machine, which is completed at the time of processing,；Workpiece is arrived at random
The online production process reached is described as semi-Markov decision processes, and takes corresponding action according to the real-time status of system；
Therefore the present invention can effectively handle the online batch processing problem of incompatible workpiece group that workpiece and demand are reached at random.
3rd, the present invention optimizes control, phase by Q learning methods to the online batch processing problem of incompatible workpiece group
Than theoretical method for solving, the present invention does not need the complete parameter of model, and can be according to the process of actual production system
Carry out on-line study；
4th, the production scheduling method that the present invention is provided, realizes simple and effect substantially, available for bio-pharmaceuticals, metal coating
Deng the production scheduling problems with incompatible workpiece group.
Brief description of the drawings
Fig. 1 is the inventive method flow chart；
Fig. 2 is the schematic diagram of online production system of the present invention.
Embodiment
In the present embodiment, a kind of online batch processing method of incompatible workpiece group based on requirement drive, applied to such as Fig. 2
The online production that shown conveyer belt 1, workpiece 2, automated handling equipment 4, caching storehouse 5, batch processing machine 6 and warehouse for finished product 7 are constituted
In system；In online production system, workpiece 2 is reached at random along conveyer belt 1, and workpiece 2 is belonging respectively to m incompatible workpiece groups；
Incompatible workpiece group refers to that the workpiece for belonging to different groups can not be arranged at a collection of middle processing.The workpiece 2 reached at random is solid
Surely pick a little at 3, the medium pending handling machine 6 of buffer pool 5 is picked by automated handling equipment 4 and processed, the work machined
Part 2 is stored in warehouse for finished product 7；And the finite capacity of batch processing machine 6.Assuming that respectively there is m in buffer pool 5 and warehouse for finished product 7
Incompatible workpiece group, makes and belongs to the Number of Jobs of j-th of incompatible workpiece group in buffer pool 5 for bfj, belong to jth in warehouse for finished product 7
The Number of Jobs of individual incompatible workpiece group is bkj；A workpiece is respectively taken to be formed from m incompatible workpiece groups of warehouse for finished product 7
A set of finished product can respond the demand reached at random.
As shown in figure 1, the online batch processing method of incompatible workpiece group based on requirement drive of being somebody's turn to do is to enter as follows
OK：
Step 1, define the state space Φ of online production system=s | s=(bf1,bf2,…,bfm,bk1,bk2,…,
bkm), wherein, s is any state in state space Φ, s ∈ Φ；
The actionable space for defining online production system is D={ v0,v1,…,vj,…,vm}；v0Represent batch processing machine 6 etc.
Treat that a workpiece 2 is reached in buffer pool 5, vjRepresent that batch processing machine 6 selects j-th of incompatible workpiece group to be processed；
The decision-making moment for defining online production system is batch processing machine idle, and have workpiece reach buffer pool when
At the time of carving, or processing completed for batch processing machine；
Step 2, the action taken using online production system under all historic states initialize control scheme list；
Control scheme list is such asIt is shown, whereinRepresent in state snUnder the action taken,
And sn∈ Φ,N represents the state sum in state space Φ.The historical data of online production system can be existing
Control scheme list, or obtained control scheme list is emulated by historical production data.
Step 3, defined variable k, and initialize k=1；Boltzmann constant K and temperature T is set；
It is State-Action to being worth to define the element in Q value tables, and the element initialized in Q value tables is " 0 "；Q value tables
Shape
Formula is such asIt is shown, the row correspondence shape in table
State, row correspondence action.
At k-th of decision-making moment of step 4, online production system, buffer pool 5 and warehouse for finished product are obtained by equipment such as sensors
The storage condition of workpiece 2 in 7, and obtain the current state s of online production systemk；Make the current state s at k-th of decision-making momentkIn Q
Corresponding state is designated as s ' in value table, then sk=s '；Make the current state s at k-th of decision-making momentkUnder the action taken be designated asIn the current state s at k-th of decision-making momentkUnder, any action v is selected from actionable space Dr, and with probabilityBy vrIt is assigned toWith probabilityBy vs′It is assigned toWherein, Q (s ', vr)
Represent the state s at k-th of decision-making momentkTake action v during=s 'rState-Action to value；Q (s ', vs′) represent to determine for k-th
The state s at plan momentkTake action v under=s 's′State-Action to value；Take actionAfterwards, system is in+1 decision-making of kth
The current state s at momentkIt is transferred to NextState sk+1, by NextState sk+1Corresponding state is designated as s " in Q value tables, then
sk+1=s "；Wherein, s ', s " ∈ Φ；
Step 5, formula (1) is utilized to calculate current state s of the online production system from k-th of decision-making momentk, take action
It is transferred to the state s at+1 decision-making moment of kthk+1State migration procedure in the accumulative cost that produces
In formula (1), N represents to produce the sum of stochastic demand in state migration procedure；R represents random in state migration procedure
The number of times that demand meets with a response, and have R=min (min (bk1,bk2,…,bkm),N)；W represents that a demand does not meet with a response institute
The punishment cost of generation；LjRepresent the cost of j-th of incompatible workpiece group of unit interval storage in warehouse for finished product；ΔtiRepresent ith
The time interval of demand and i+1 time demand, represents k-th of decision-making moment and the time interval of i+1 time demand during i=0；i
The R times demand and the time interval at+1 decision-making moment of kth are represented during=R；
Step 6, using the difference formula and Q values more new formula shown in formula (2) and formula (3), update current state in Q value tables
skUnder take actionState-Action to valueCurrent state s after being updatedkUnder take actionShape
State-action is to value
In formula (2), Δ Tk,k+1Represent the time interval at k-th of decision-making moment and+1 decision-making moment of kth；Represent the
Institute is stateful before k decision-making moment takes action v when being s 's′It is transferred to the accumulative cost that is produced during NextState s "
Mean estimates；vrRepresent any action in actionable space D；Q(sk+1,vr) represent to be transferred to the shape at+1 decision-making moment of kth
State sk+1Under take any action vrState-Action to value；
In formula (3),For the current state s at k-th of decision-making momentkUnder take actionLearning Step；
Often the minimum State-Action of row constitutes current line to the action corresponding to value in Q value tables after step 7, selection update
Dynamic set, and update control scheme list using current action collection；
Step 8, k+1 is assigned to k, and return to step 4, untill control scheme list no longer changes, so that with final
Control scheme list online batch processings are carried out to the incompatible workpiece groups of m.
Claims (1)
1. a kind of online batch processing method of incompatible workpiece group based on requirement drive, it is characterized in that applied to by conveyer belt,
In the online production system that workpiece, automatic transportation equipment, caching storehouse, batch processing machine and warehouse for finished product are constituted；In the online life
In production system, workpiece is reached at random, and is stored in the buffer pool and is waited the batch processing machining, the work machined
Part is stored in the warehouse for finished product；Assuming that respectively there are m incompatible workpiece groups in the buffer pool and warehouse for finished product, the buffering is made
The Number of Jobs for belonging to j-th of incompatible workpiece group in storehouse is bfj, belong to j-th incompatible workpiece group in the warehouse for finished product
Number of Jobs is bkj；The a set of finished product for respectively taking a workpiece to be formed from m incompatible workpiece groups of the warehouse for finished product can
Respond the demand reached at random；The online batch processing method of incompatible workpiece group based on requirement drive is by as follows
Step is carried out：
Step 1, define the state space Φ of the online production system=s | s=(bf1,bf2,…,bfm,bk1,bk2,…,
bkm), wherein, s is any state in the state space Φ, s ∈ Φ；
The actionable space for defining the online production system is D={ v0,v1,…,vj,…,vm}；v0Represent the batch processing machine
A workpiece is waited to reach in buffer pool, vjRepresent that described j-th of incompatible workpiece group of batch processing machine choice is processed；
The decision-making moment for defining the online production system is the batch processing machine idle, and there have a workpiece to reach to be described slow
At the time of rushing storehouse, or at the time of completing processing for the batch processing machine；
Step 2, the action taken using the online production system under all historic states initialize control scheme list；
Step 3, defined variable k, and k=1 is initialized, Boltzmann constant K and temperature T is set；
It is State-Action to being worth to define the element in Q value tables, and the element initialized in the Q values table is " 0 "；
Step 4, k-th of decision-making moment in the online production system, observe the current state of the online production system simultaneously
It is designated as sk, make the current state s at k-th of decision-making momentkCorresponding state is designated as s ' in Q value tables, then sk=s '；Order
The current state s at k-th of decision-making momentkUnder the action taken be designated asIt is current k-th decision-making moment
State skUnder, any action v is selected from actionable space Dr, and with probabilityBy vrIt is assigned toWith general
RateBy vs′It is assigned toWherein, Q (s ', vr) represent k-th of decision-making moment state skDuring=s '
Take action vrState-Action to value；Q(s′,vs′) represent k-th of decision-making moment state skTake action v under=s 's′'s
State-Action is to value；Take actionAfterwards, current state s of the system at+1 decision-making moment of kthkIt is transferred to NextState
sk+1, by NextState sk+1Corresponding state is designated as s " in Q value tables, then sk+1=s "；Wherein, s ', s " ∈ Φ；
Step 5, the current state s of the online production system from k-th of decision-making moment is calculated using formula (1)k, take action
It is transferred to the state s at+1 decision-making moment of kthk+1State migration procedure in the accumulative cost that produces
<mrow>
<mi>f</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
<mo>,</mo>
<msub>
<mi>v</mi>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
</msub>
<mo>,</mo>
<msub>
<mi>s</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<mi>R</mi>
</munderover>
<msub>
<mi>&Delta;t</mi>
<mi>i</mi>
</msub>
<mo>&CenterDot;</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msub>
<mi>L</mi>
<mi>j</mi>
</msub>
<mo>&CenterDot;</mo>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>bk</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<mi>i</mi>
<mo>,</mo>
<mn>0</mn>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>W</mi>
<mo>&CenterDot;</mo>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>N</mi>
<mo>-</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<mo>(</mo>
<mrow>
<msub>
<mi>bk</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<msub>
<mi>bk</mi>
<mn>2</mn>
</msub>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>bk</mi>
<mi>m</mi>
</msub>
</mrow>
<mo>)</mo>
<mo>,</mo>
<mn>0</mn>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (1), N represents to produce the sum of stochastic demand in the state migration procedure；R is represented in the state migration procedure
The number of times that stochastic demand meets with a response, and have R=min (min (bk1,bk2,…,bkm),N)；W represents that a demand is not rung
Answer produced punishment cost；LjRepresent the cost of j-th of incompatible workpiece group of unit interval storage in the warehouse for finished product；Δti
Represent the time interval of ith demand and i+1 time demand, represented during i=0 k-th of decision-making moment and i+1 time demand when
Between be spaced, the R time demand and the time interval at+1 decision-making moment of kth are represented during i=R；
Step 6, using the difference formula and Q values more new formula shown in formula (2) and formula (3), update current state in the Q values table
skUnder take actionState-Action to valueCurrent state s after being updatedkUnder take actionShape
State-action is to value
<mrow>
<msub>
<mi>c</mi>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
</msub>
<mo>=</mo>
<mi>f</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
<mo>,</mo>
<msub>
<mi>v</mi>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
</msub>
<mo>,</mo>
<msub>
<mi>s</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>&Delta;T</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mover>
<mi>&eta;</mi>
<mo>~</mo>
</mover>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
</msub>
<mo>+</mo>
<munder>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
<mrow>
<msup>
<mi>v</mi>
<mi>r</mi>
</msup>
<mo>&Element;</mo>
<mi>D</mi>
</mrow>
</munder>
<mi>Q</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>s</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>,</mo>
<msup>
<mi>v</mi>
<mi>r</mi>
</msup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>Q</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
<mo>,</mo>
<msub>
<mi>v</mi>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msup>
<mi>Q</mi>
<mo>&prime;</mo>
</msup>
<mrow>
<mo>(</mo>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
<mo>,</mo>
<msub>
<mi>v</mi>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
</msub>
<mo>)</mo>
</mrow>
<mo>:</mo>
<mo>=</mo>
<mi>Q</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
<mo>,</mo>
<msub>
<mi>v</mi>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>&gamma;</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
<mo>,</mo>
<msub>
<mi>v</mi>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
</msub>
<mo>)</mo>
</mrow>
<msub>
<mi>c</mi>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (2), Δ Tk,k+1Represent the time interval at k-th of decision-making moment and+1 decision-making moment of kth；Represent at k-th
Institute is stateful before the decision-making moment takes action v when being s 's′It is transferred to the flat of the accumulative cost that is produced during NextState s "
Equal estimate；vrRepresent any action in the actionable space D；Q(sk+1,vr) represent to be transferred to+1 decision-making moment of kth
State sk+1Under take any action vrState-Action to value；
In formula (3),For the current state s at k-th of decision-making momentkUnder take actionLearning Step；
Often the minimum State-Action of row collects to the current action of action composition corresponding to value in Q value tables after step 7, selection update
Close, and the control scheme list is updated using current action collection；
Step 8, k+1 is assigned to k, and return to step 4, untill the control scheme list no longer changes, so that with final
Control scheme list online batch processings are carried out to the incompatible workpiece groups of m.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710405175.4A CN107133703A (en) | 2017-06-01 | 2017-06-01 | A kind of online batch processing method of incompatible workpiece group based on requirement drive |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710405175.4A CN107133703A (en) | 2017-06-01 | 2017-06-01 | A kind of online batch processing method of incompatible workpiece group based on requirement drive |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107133703A true CN107133703A (en) | 2017-09-05 |
Family
ID=59734288
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710405175.4A Pending CN107133703A (en) | 2017-06-01 | 2017-06-01 | A kind of online batch processing method of incompatible workpiece group based on requirement drive |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107133703A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108215202A (en) * | 2018-01-15 | 2018-06-29 | 合肥工业大学 | A kind of 3D printing control method in batches for considering print quality |
| CN111260144A (en) * | 2020-01-20 | 2020-06-09 | 合肥工业大学 | Method for solving single-machine batch scheduling problem under condition of random arrival of different workpieces |
| CN111517112A (en) * | 2020-04-21 | 2020-08-11 | 合肥工业大学 | Improved TP + LCM full-lamination system and control method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030220828A1 (en) * | 2002-05-23 | 2003-11-27 | Chih-An Hwang | Polymer production scheduling using transition models |
| CN102938102A (en) * | 2012-10-19 | 2013-02-20 | 北京理工大学 | Cross-operation unit scheduling method with batching machine |
| CN104635772A (en) * | 2014-12-08 | 2015-05-20 | 南京信息工程大学 | Method for adaptively and dynamically scheduling manufacturing systems |
-
2017
- 2017-06-01 CN CN201710405175.4A patent/CN107133703A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030220828A1 (en) * | 2002-05-23 | 2003-11-27 | Chih-An Hwang | Polymer production scheduling using transition models |
| CN102938102A (en) * | 2012-10-19 | 2013-02-20 | 北京理工大学 | Cross-operation unit scheduling method with batching machine |
| CN104635772A (en) * | 2014-12-08 | 2015-05-20 | 南京信息工程大学 | Method for adaptively and dynamically scheduling manufacturing systems |
Non-Patent Citations (1)
| Title |
|---|
| 李博川: "两品种工件混流的多站点CSPS系统最优控制研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108215202A (en) * | 2018-01-15 | 2018-06-29 | 合肥工业大学 | A kind of 3D printing control method in batches for considering print quality |
| CN108215202B (en) * | 2018-01-15 | 2020-02-11 | 合肥工业大学 | 3D printing batch control method considering printing quality |
| CN111260144A (en) * | 2020-01-20 | 2020-06-09 | 合肥工业大学 | Method for solving single-machine batch scheduling problem under condition of random arrival of different workpieces |
| CN111517112A (en) * | 2020-04-21 | 2020-08-11 | 合肥工业大学 | Improved TP + LCM full-lamination system and control method |
| CN111517112B (en) * | 2020-04-21 | 2021-09-24 | 合肥工业大学 | Improved TP + LCM full-lamination system and control method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liang et al. | Intelligent manufacturing systems: a review | |
| CN103942610B (en) | The polymorphic configuration optimization method of reconfigurable manufacturing system of task based access control | |
| CN110598941A (en) | Bionic strategy-based dual-target scheduling method for particle swarm optimization manufacturing system | |
| CN114186791B (en) | Multi-model small-batch-oriented dynamic scheduling method for assembly and production of complex equipment products | |
| CN107133703A (en) | A kind of online batch processing method of incompatible workpiece group based on requirement drive | |
| CN111882215A (en) | Individual customized flexible job shop scheduling method containing AGV | |
| Yamazaki et al. | Design method of material handling systems for lean automation—Integrating equipment for reducing wasted waiting time | |
| CN111861167A (en) | Online dynamic scheduling method of production line based on decomposition multi-objective optimization algorithm | |
| CN108197833A (en) | A kind of complete Real-time dispatch system and dispatching method towards discrete workshop | |
| CN111401611B (en) | Route optimization method for routing inspection point of chemical plant equipment | |
| Liao et al. | Optimization and simulation of job-shop supply chain scheduling in manufacturing enterprises based on particle swarm optimization | |
| CN116880424A (en) | Multi-robot scheduling method and device based on multi-objective optimization | |
| CN118011978A (en) | Flexible flow shop dynamic scheduling and maintenance optimization method considering random interference | |
| Wang et al. | Genetic algorithm in flexible work shop scheduling based on multi-objective optimization | |
| CN111667091A (en) | Intelligent control method of conveying system based on particle swarm algorithm | |
| CN117647960A (en) | Workshop scheduling method, device and system based on deep reinforcement learning | |
| CN112418478A (en) | Low-carbon scheduling model and energy-saving optimization method under flexible flow shop | |
| CN117314055A (en) | Intelligent manufacturing workshop production-transportation joint scheduling method based on reinforcement learning | |
| CN115018180B (en) | Super heuristic scheduling method and system for energy-saving distribution and processing of tin process raw materials | |
| CN112926792B (en) | Dynamic scheduling method and system for welding workshop order change based on rolling time window | |
| CN116300756A (en) | Double-target optimal scheduling method and system for flexible manufacturing workshop with transportation robot | |
| CN115629584A (en) | Order scheduling optimization method based on improved crow search algorithm | |
| CN116861571A (en) | Machining procedure selection method for manufacturing and machining island of metal mold | |
| CN115062936A (en) | Automobile assembly line dynamic periodic material distribution scheduling method considering regional responsibility system | |
| Liu et al. | Scheduling optimization of flexible flow shop |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170905 |