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RESEARCH POSTER PRESENTATION DESIGN 2015
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A1
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C3 D3
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E2 B2
C2D2
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B1
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AB3 AB
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AD 3 AE4
AF 4AE 3
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X3
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P1
W1
K3
L3
K1
AF1
AJ1
AI1
AN2
AM2
0.00065
0.0006
0.00037
0.00036
0.0017
0.0012
0.0012
4.115m
0.0003
0.0003
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0.0003
0.0003
0.0005
0.0006
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0.00038
0.0015 0.0005
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0.0006 0.0006
0.0025
0.0014 0.00140.0013
0.0006 0.0006
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0.0005 0.0005
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03
0.0003
0.00090.0009
0.0015
0.00100.0010
0.0003
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0.0009
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0.0003
0.0013
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0.0006
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0.0003 0.0003
0.0003
0.00036
0.0037
0.0006
0.00065
0.00038
0.0017
0.0015 0.0005
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0.00060.0006
0.0014
0.0025
0.0014 0.0005
0.0005
0.0015
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0.0006
0.00065
0.00060.0006
0.00038
0.0015
0.00038
0.00065
0.0006
0.00037
0.00037
0.0002
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0.0006
0.0012
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0.0003
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0.00036
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03
0.0017
L1
AD1
M1
AB1
N1
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O1
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0.0003 0.0001 0.0005
0.0008 0.00080.0008 0.0008
0.0001
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AE1
AC1
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Y10.0013 0.00130.00130.0013
0.0007 0.0004 0.0005
0.00050.0005
0.0004
0.00070.0007
0.0003
0.0015
BAY11 0.0005 BAY12 0.0006 BAY13
0.0006 BAY14 0.0001 BAY15 BAY16 BAY17 BAY180.0006 0.0006 0.0001 0.0003
BAY116 BAY115 BAY114 BAY113 BAY112 BAY111 BAY110 BAY190.0003 0.0005 0.0009 0.0006 0.0004 0.0006
0.00060.0005
M3
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0.0001
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P3
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0.0007 0.0004 0.0005
0.00050.0005
0.0004BAY31 0.0003 BAY32 0.0006 BAY35 0.0006 BAY36 0.0001 BAY310 BAY312 BAY313 BAY3150.0006 0.0006 0.0001 0.0003
BAY33 BAY34 BAY37 BAY38 BAY39 BAY311 BAY314 BAY3160.0003 0.0005 0.0009 0.0006 0.0004 0.0006 0.00060.0005M4
N4
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0.00030.00010.0005
0.00080.0008 0.00080.0008
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0.0006
O4
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V4
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0.00070.00040.0005
0.0005 0.0005
0.0004 BAY4160.0005BAY4140.0006BAY4120.0006BAY4110.0001BAY47BAY45BAY44
BAY42
0.00060.00060.0001
0.0003
BAY415BAY413BAY410BAY49BAY48BAY46BAY43BAY410.0003
0.00050.00090.00060.00040.00060.0006 0.0005
AG2
AF2
AB2
AC2
U2
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V2
W2
0.00030.00010.0005
0.00080.0008 0.00080.0008
0.0001
0.0006
AE2
AD2
R2
S2 0.00130.0013 0.0013 0.0013
0.00070.00040.0005
0.0005 0.0005
0.0004 BAY2160.0005BAY2130.0006BAY2120.0006BAY2110.0001BAY27BAY26BAY24BAY22 0.00060.00060.00010.0003
BAY215BAY214BAY210BAY29BAY28BAY25BAY23BAY21 0.00030.00050.00090.00060.00040.00060.0006 0.0005
AQ1
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48 secs
i. Linear (current practice): = 10 +
ii. Piece-wise Linear:
= 10 + , 20 + (10) ,
iii. Big M:
= 10 + ,,
n number of vehicles on an edge t time taken to travel an empty edge e(n) time to traverse jammed edge with n vehicles
Routing of automated material handlingsystems (AMHS) vehicles in wafer fabs.
Increasing number of FOUP pickup/delivery requests are causing:
Increase in number of vehiclesIncrease in vehicle congestionIncrease in wait time for FOUPs
1. HomogeneousSimulations:Equally probable pickup/delivery requests.
2. HeterogeneousSimulations:Doubly probable pickup/delivery from any one bay.
Pickup Protocols continued:
c. Reservation with ETA Policy:
Every scheduling cycle, busy vehicles withestimated time of arrival under somethreshold, get reserved by lots in queuethat are nearest to their current location(via current destination).
Any idle reserved vehicle is re-reserved bynearer lots in queue.
d. Reservation without ETA Policy:
Same as Reservation with ETA excepttheres no ETA threshold.
INTRODUCTION TOOLS
OBJECTIVES
15 server (vehicle) stylized system Stable pickup request arrival rate -
determined by trial and error Stability - vehicle utilization 98% Graphical system stability verification:
a. FIFO:Idle vehicle picks up a lot from the queuethat has waited the longest.
b. Nearest Lot Policy:Pickup lot thats nearest in terms of thetime an idle vehicle will take to travel to it.
METHODOLOGY (1/3) METHODOLOGY (3/3)
ONGOING WORK
Python simulate & test algorithms AutoCAD design stylized fab
Microsoft Visio - Toy lattice networks for building &testing simulator:
- Network representation of stylized fab
METHODOLOGY (2/3)
John J Hasenbein a, Shreya Gupta ba: Associate Professor, [email protected]; b: PhD Candidate, [email protected]; OR/IE Group, Department of Mechanical Engineering, The University of Texas at Austin
Improving Scheduling and Control of the OHTC Controller in Wafer Fab AMHS Systems
Lot 10, 40 secs
Lot 11, 10 secs
Lot 12, 33 secs
Lot n, 27 secs
(Lots in queue
Time from vehicle 3)
, Sequence in which vehicles become idle
3
8
5
7
Lot 10, NA
Lot 11, NA
Lot 12, NA
Lot n, NA
(Lots in queue
Time from vehicle 3)
, Sequence in which vehicles become idle
3
8
5
7
Lot 10, 300 secs
Lot 11, 10 secs
Lot n, 27 secs
(Lots in queue
Time fromvehicle 3)
, Sequence in which vehicles become idle
3
8
7
1st reservation of vehicles with ETA under 4 mins
ETA, 3.3 mins
ETA, 3.7 mins
ETA, 1.0 min
Scheduling Cycle 1 (Cycle time = 3mins)
Lot 10, 28 secs
Lot 11, reserved
Lot n, 5 secs
(Lots in queue
Time from vehicle 8)
, Sequence in which vehicles become idle
3
8
7
2nd reservation of vehicles with ETA under 4 mins
ETA, reserved
ETA, 3.7 mins
ETA, 1.0 min
Lot 10, 400 secs
Lot 11, 10 secs
Lot 12, 330 secs
Lot n, 27 secs
(Lots in queue
Time from vehicle 3)
, Sequence in which vehicles become idle
3
1
9
7
1st reservation of vehicles with no ETA threshold
Scheduling Cycle 1 (Cycle time = 3mins)
Lot 13, 120 secs 8
Additional vehicles available for reservationbetween vehicles 3 & 8
No. of vehiclesthreshold
Tim
e
No. of vehiclesthreshold
Tim
e
Bay 125%
Bay 225%
Bay 425%
Bay 325%
Bay 150%
Bay 216.67%
Bay 316.67%
Bay 416.67%
PICKUP PROTOCOLS
PARAMETER ESTIMATION ROUTING ALGORITHMS
Symmetric & unidirectional
Asymmetric & bidirectional
No. of vehiclesthreshold
M
Tim
e
>
>
B
C
A
D
B
Sub-optimal route decidedusing the actual distancebetween nodes D & B.
Optimal route decided usingstate dependent time (orcost) between nodes D & B.
From ToA B 18 secs 0B C 18 secs 0C D 18 secs 0D B 18 secs 3B A 18 secs 1
Network State
Edges Edge Travel Time
48 secs36 secs
18 secs
State Dependent Edge Travel Time
18 secs18 secs
A
D C
36 secs
0.035
0.016
A B C D
H G F E
I J K L
0.005
0.015
0.007
0.019
0.013
0.010
0.006
0.005
0.023
0.002
0.007
0.012
0.014
0.020
0.007
0.014
0.013
0.004
0.022 0.0140.014 0.0030.011 0.0020.008
0.005 0.0120.020 0.0250.013 0.0080.015
A B C D
H G F E
I J K L
0.005 0.005 0.005
0.0050.005 0.005
0.0050.005 0.005
0.005 0.005 0.005 0.005
0.005 0.005 0.005 0.005
FAB CONTROLLER
MCS
OHTC
1 2 N
Improve throughput via better routingalgorithms for the over hoist transportcontrol (OHTC) system.
Enhance state dependent cost functionsused in Dijkstra's algorithm.
State of the network at time t is thenumber of vehicles on the various edges ofthe network.
vehicles
Sheet1
EdgesEdge Travel TimeNetwork StateState Dependent Edge Travel Time
FromTo
AB18 secs018 secs
BC18 secs018 secs
CD18 secs018 secs
DB18 secs348 secs
BA18 secs136 secs
Slide Number 1