the ongoing challenge - tutorial the illusion of capacity
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The Ongoing Challenge - Tutorial The Illusion Of Capacity. How do we determine what a FAB can produce in what time frame under what conditions? What Methods are available and used with what Frequency? How do they compare?. part 5 of 4. - PowerPoint PPT PresentationTRANSCRIPT
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
1
The Ongoing Challenge - Tutorial
The Illusion Of Capacity
Dr. Ken Fordyce & John Fournier, IBMProf. John Milne, Clarkson University & Dr. Harpal Singh, CEO Arkieva
** Dr. Horst Zisgen, IBM, Rich Burda, Gary Sullivan (IBM, retired), Peter Lyon (IBM retired), Prof Chi-Tai Wang NCU (Taiwan)
How do we determine what a FAB can produce in what time frame under what conditions?
What Methods are available and used with what Frequency?
How do they compare?
part 5 of 4
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Where we just visited & where we are going
• Just Visited - How can we do a better job of representing FAB capacity within traditional central planning models. This topic is broken into two parts:– Part 1 - describing the core challenge, simply describing the
current process and the basic nature of the question. – Part 2 – indentifying options to actually improve the
representation of FAB capacity within central planning • Example link EPOS with PROFIT• “clearing function” that modifies capacity available or cycle time• Heuristic estimate of resource entity
• Going to Visit - If we narrow our focus to just FAB planning and open to methods other than the traditional balance equations, what methods are available and how do they compare?
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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The Never Ending Loop
CapacityPlanningSystem
Client Demand
Capacity Limits
Wafer StartProfile
CentralPlanningEngine
EquipmentLoad Reports
Feedback Loop for Constrained Toolsets
CT Commit
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Typical FAB Questions
• Which lots are going to exit the FAB next week?• What is the near-term and long-term capability of a
toolset (or fab)?• What is the expected incoming WIP at a toolset?• Will toolset operational outs rise or fall in the near future?• What will be the effect of adding/removing a certain tool
from production?• What is the impact of expedites on cycletime?• Is the toolset’s current deployment adequate for
incoming WIP? • Where should resources be deployed today?• Is there a window of opportunity for equipment work?
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Typical FAB Questions
• What tool sets are “broken” (insufficient capacity for projected load), what are the options to fix broken tool sets, and what is the impact of these actions?
• Are the wafer start limits accurate?
• Is the cycle time commit accurate?
• When are the lots in the line exiting?
• When would a projected set of wafer starts exit?
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Emerging FAB Question
• What is the impact of complex process time windows– Capacity required– Cycle time impact– Near term lot completion estimates
• Different than “best practices” for schedule dispatch (not more important, just different)
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Short List of “methods” to answer questionstypically either start driven (forward) or demand driven (backward)
• Algebraic methods to allocate steady state workload to tool sets • Extending the algebraic method to handle deployment or cascade (essentially CAPS)• “race” track with / without limits (as wafers)• WIP Projector type logic working sectors, cycle times, and aggregate capacity• Daily output planning• SLIM• Rule Based simulation• Time Slide (TALC)• Queuing models• Queuing models and static capacity planning• queuing networks / fluid models (EPOS)• discrete event simulation (WIPSIM)• Clearing Functions• Column generation optimization type simulation• Combination of simulation and optimization• Clearing “function” incorporated into optimization• Transport equations via partial differential equations
ResearchCompare, contrastClassify best use
Examples WIPSIM vs EPOS
EPOS vs Transport Equations
New textProduction Planning and Control for
Semiconductor Wafer Fabrication FacilitiesMonch, Fowler, et al Springer
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Clearing Functions within LP
• Armbruster & Uzsoy 2012, “Continuous Dynamic Models, Clearing Functions, and Discrete-Event Simulation in Aggregate Production Planning”, Production Planning Tutorials in Operations Research”, isbn 978-0-9843378-3-5, dx.doi.org/10.1287/educ.1120.0102
– ACF: allocated Clearing Function pp. 10-13• Asmundsson et al, “Production planning models with resources
subject to congestion,” Naval Res. Logistics, vol. 56, no. 2, pp. 142–157, Mar. 2009.
• Asmundsson et al, “Tractable nonlinear production planning models for semiconductor wafer fabrication facilities,” IEEE Trans. Semicond. Manuf., vol. 19, no. 1, pp. 95–111, 2006
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
11
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
12
Sources
• Tibbits, B, 1993, “Flexible simulation of a complex semiconductor manufacturing line using a rule-based”, IBM R&D Journal
• Sullivan, G, 1992, “Daily Output Planning”, Expert Systems with Applications
• Sullivan, G, 1995, “Dynamically Generated Rapid Response Capacity Planning Model”
• Bermon, S., and S. Hood. 1999. Capacity optimization planning system (CAPS). Interfaces 29 (5): 31–50.
• Bagchi, S et al 2008. A Full-factory Simulator As A Daily Decision-support Tool For 300mm Wafer Fabrication Productivity, MASM 2008
• Zisgen, H. et al 2008. A Queueing Network Based System To Model Capacity And Cycle Time For Semiconductor Fabrication, MASM 2008
• Schelasin, R 2011. Using Static Capacity Modeling And Queuing Theory Equations To Predict Factory Cycle Time Performance In Semiconductor Manufacturing, MASM 2011
• Levy, J. et al 2010,”Method For Determining Amount of Product Released Into a Time Sensitive Operation”, MASM 2010
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
13
Sources• Armbruster & Uzsoy 2012, “Continuous Dynamic Models, Clearing Functions, and
Discrete-Event Simulation in Aggregate Production Planning”, Production Planning Tutorials in Operations Research”, isbn 978-0-9843378-3-5, dx.doi.org/10.1287/educ.1120.0102
• Kacar, Irdeem, & Uzsoy, 2011, “An Experimental Comparison of Production Planning Using Clearing Functions and Iterative Linear Programming-Simulation Algorithms”, IEEE Transactions on Semiconductor MFG,
• Irdeem, Kacar, & Uzsoy, 2010, An Exploratory Analysis of Two Iterative Linear Programming—Simulation Approaches for Production Planning, Ieee Transactions On Semiconductor Manufacturing, Vol. 23, No. 3, August 2010
• Asmundsson et al, “Production planning models with resources subject to congestion,” Naval Res. Logistics, vol. 56, no. 2, pp. 142–157, Mar. 2009.
• Asmundsson et al, “Tractable nonlinear production planning models for semiconductor wafer fabrication facilities,” IEEE Trans. Semicond. Manuf., vol. 19, no. 1, pp. 95–111, 2006
• H. Missbauer. Models of the transient behaviour of production units to optimize the aggregate material flow. International Journal of Production Economics 118(2):387{397, 2002.
• H. Missbauer and R. Uzsoy. Optimization models for production planning. K. Kempf,• P. Keskinocak, and R. Uzsoy, eds. Planning Production and Inventories in the
Extended Enter-• prise: A State of the Art Handbook. Springer Verlag, New York, 437{508,A47 2011.• [ Modigliani and F. E. Hohn. Production planning over time and the nature of the
expectation
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Presentations at MASM 2012
• Using Iterative Simulation to Incorporate Load-Dependent Lead Times in Master Planning Heuristics; Lars Moench and Thomas Ponsignon (Infineon). Abstract: In this paper, we consider heuristics for master planning in semiconductor manufacturing. While lead times are typically assumed as fixed in production planning, we use iterative simulation to take load-dependent lead times into account. An AutoSched AP simulation model of a semiconductor supply chain is used for implementing the scheme. Simulation results show that the iterative scheme converges fast and leads to less variable, more profitable production plans compared to planes obtained by the fixed lead time approach.
• Product Mix Optimization for a Semiconductor Fab: Modeling Approaches and Decomposition Techniques; Andreas Klemmt, Martin Romauch, Walter Laure (Infineon Technologies). Abstract: For optimizing a semiconductor fab we are aiming to match the production capabilities, capacities and the demand in the most profitable way. In this paper we address a linear model of the product mix problem considering product dependent demand limits (obligations and demand forecast) and profits while respecting the the capacity bounds of the production facility. Since the capacity consumption is highly depended on choosing from different production alternatives we are implicitly solving a static capacity planning problem for each product mix. This kind of planning approach is supported by the fluid flow concept of complete resource pooling in high traffic. We propose a general model that considers a wide range of objectives and we introduce a heuristic based on a decomposition of the static capacity planning problem. The computational study of the approaches is based on real world data and on randomly generated instances.
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
15
Presentations at MASM 2012
• An Evaluation of an Option Contract in Semiconductor Supply Chains Konstanze Knoblich (Infineon Technologies) and Cathal Heavey and Peter Williams (University of Limerick), Abstract: The purpose of this paper is to evaluate an option contract within a semiconductor supply chain consisting of one semiconductor manufacturer and one customer. In an option contract the customer pays an upfront fee (option price) for an option to purchase product. A simulation model is used to compare the performance of an option contract against a standard supply contract used in a semiconductor supply chain in terms of delivery performance and costs for the supply chain partners
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Time Target(s)
Zone of Control for Process Time Window ManagementBasic Flow
MA 1 MA 2 MA i MA nStart of ZOC End ZOC
Each Lot in the Process Window Zone of Control mustMake it through N manufacturing actions (MA) under the time target
This gets complicated QuicklyLet’s take a look
Dangerstarts
DangerEnds
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Time Target(s)
Zone of Control for Process Time Window Management - manufacturing activities and tool sets
MA 1 MA 2 MA i MA nStart of ZOC
Tool Set A
Tool Set B
Tool Set C
Tool Set D
End ZOC
Each MA is handled by a Different Tool GroupThe number and types of tools in each tool group vary
In traditional capacity analysis there is no cost in waitingExcept for the accumulation of cycle time
Now need to look at capabilities of the entire suite or zone of toolsForces additional idle without WIP
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Time Target(s)
MA 1 MA 2 MA i MA nStart of ZOC
ZOC lots ZOC lots ZOC lots ZOC lots
Tool Set A
Tool Set B
Tool Set C
Tool Set D
End ZOC
Process Time Window Lots (called ZOC lots) are “spread”across the zone of control
Zone of Control for Process Time Window Management - manufacturing activities, tool sets, and ZOC lots
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Time Target(s)
MA 1 MA 2 MA i MA nStart of ZOC
ZOC lots ZOC lots ZOC lots ZOC lots
Tool Set A
non ZOC lots
non ZOC lots
non ZOC lots
non ZOC lots
Tool Set B
Tool Set C
Tool Set D
End ZOC
Zone of Control for Process Time Window Management - manufacturing activities, tool sets, ZOC lots, non ZOC lotsNow the problem GETS Interesting. The tool sets must also process non
time window lots. Called non-ZOC lots
Can This fillIdle w/o WIP
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Core of Algebraic Method
• Algebra methods – the FAB plans to start three products (A, B, and C) at the rate of 10, 30, and 20 wafer starts per day. Product A requires 3 passes at photo, Product B required 4 passes, and C requires 2 passes. With some simple arithmetic I can determine the work load on the photo tools in terms of passes.
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Basics of FAB Planning
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Basics of FAB Planning
• Focus on matching assets with demand• Three major classes
– Aggregate FAB planning– Deployment or near term tool planning– WIP Projection
• Forward flow of starts dominate method as opposed to pulling to meet demand
• Wide variation in methods• Wide variation in how much FAB complexity of
deployment and operating curve is handled
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Basics of Algebraic Approach to Aggregate FAB Planning
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Basics of Aggregate Factory Planning
• focused on assessing the ability of the factory to meet certain demand looking to identify “broken” toolsets and creating the capacity inputs required by central planning.
• Demand is stated as a starts profile and a lead (cycle) time commit for each part.
• Various levels of sophistication in handling operating curve, deployment, mix variability, etc
• The key challenges for the factory planner are:– Determine if the workload can be allocated across the tools in
such a way that all of the workload can be allocate without violating capacity constraints
– If insufficient capacity exits• find the optimal mix of workload that can be met without violating
capacity constraints• find the optimal allocation that either minimizes additional capacity
needed incorporating some type of fair share of pain
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Factory load or starts
Statement
Representative Factory Routes often
aggregating parts into a family
Aggregate Factory Capacity Plan
Tool Set Character
istics
Reports on required utilization levels and capacity loss points
High level statement of capacity based specified
cycle time to limit demand on the factory
How additional capacity enables
the factory to handle more starts
Allocation of tools to
families
Factory Planning
Model has key
relationships
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Tool A Tool B Tool Cno tools covering
oper
oper001 1 1 0 2
oper002 1 1 0 2
oper003 0 1 1 2
oper004 0 0 1 1
oper005 0 1 1 2
oper006 1 0 0 1
oper007 1 0 0 1
number opers tool covers 4 4 3
Table 5.1: Deployment Information for PSO Group
Ingredient # 1Which Tools Can Handle Which Operations
1 – oper/tool link active0 – not allowed
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Tool A Tool B Tool Coper001 4 20 5oper002 15 20 6oper003 10 15 8oper004 10 9 20oper005 5 5 5oper006 8 10 10oper007 10 10 10
Table 5.3: RPT information
Ingredient # 2Raw Process Time (RPT) per widget per time unit
for Tool / Operation Pairing
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Tool A Tool B Tool Caverage daily
workloadoper001 4 20 999999 40oper002 15 20 999999 40oper003 999999 15 8 40oper004 999999 999999 20 40oper005 999999 5 5 50oper006 8 999999 999999 50oper007 10 999999 999999 50
capac avl 720 1152 1296
Table 5.4: Average Daily Demand and Capacity Available
Ingredient # 3Average Daily Work load for each operationBased on Starts Profile for some time unit
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Tool A Tool B Tool Caverage daily
workloadoper001 4 20 999999 40oper002 15 20 999999 40oper003 999999 15 8 40oper004 999999 999999 20 40oper005 999999 5 5 50oper006 8 999999 999999 50oper007 10 999999 999999 50
capac avl 720 1152 1296
Table 5.4: Average Daily Demand and Capacity Available
Ingredient # 4Average Capacity Available for each tool Based on tool analysis for some time unit
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Ingredient # 5 “cycle time tax” to reduce capacity available
to effective capacity availableTo account for required idle without WIP
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Tool A Tool B Tool Caverage daily
workloadoper001 4 20 999999 40oper002 15 20 999999 40oper003 999999 15 8 40oper004 999999 999999 20 40oper005 999999 5 5 50oper006 8 999999 999999 50oper007 10 999999 999999 50
capac avl 720 1152 1296
Table 5.4: Average Daily Demand and Capacity Available
Ingredients - operation to tool link - raw process time - capacity available - workload or demand - cycle time tax (not show in this example)
Assign “portions” of tools to operationsTo cover workload
And not exceed available capacity
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Tool A Tool B Tool C demand met constraint
average daily workload delta
oper001 23.0 17.0 0.0 40.0 ≤ 40 0.0oper002 0.0 30.0 0.0 30.0 ≤ 40 10.0oper003 0.0 10.0 30.0 40.0 ≤ 40 0.0oper004 0.0 0.0 40.0 40.0 ≤ 40 0.0oper005 0.0 10.0 30.0 40.0 ≤ 50 10.0oper006 40.0 0.0 0.0 40.0 ≤ 50 10.0oper007 30.0 0.0 0.0 30.0 ≤ 50 20.0
cap used 712.0 1140.0 1191.0 total unmet dmd 50.0constraint ≤ ≤ ≤cap avl 720.0 1152.0 1296.0delta 8.0 12.0 105.0
Table 5.5: Allocation Decision and Results where capacity limits are not violated
assign lots to tools to minimize unmet demand without exceeding capac avail or workload
Allocation Decision # 1
Demand on Oper001 (20) met as followsTool A takes 23 units and Tool B gets 17
Total Workload on Tool A is(23x4) + (40x8 ) +(30x10 ) =
92 + 320 + 300 = 712
Tool A Tool B Tool Caverage daily
workloadoper001 4 20 999999 40oper002 15 20 999999 40oper003 999999 15 8 40oper004 999999 999999 20 40oper005 999999 5 5 50oper006 8 999999 999999 50oper007 10 999999 999999 50
capac avl 720 1152 1296
Table 5.4: Average Daily Demand and Capacity Available
Workload on Tool A is 092 = 23 x 4Workload on Tool B is 340 = 17 x 20
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Tool A Tool B Tool C demand met constraint
average daily workload delta
oper001 23.0 17.0 0.0 40.0 ≤ 40 0.0oper002 0.0 30.0 0.0 30.0 ≤ 40 10.0oper003 0.0 10.0 30.0 40.0 ≤ 40 0.0oper004 0.0 0.0 40.0 40.0 ≤ 40 0.0oper005 0.0 10.0 30.0 40.0 ≤ 50 10.0oper006 40.0 0.0 0.0 40.0 ≤ 50 10.0oper007 30.0 0.0 0.0 30.0 ≤ 50 20.0
cap used 712.0 1140.0 1191.0 total unmet dmd 50.0constraint ≤ ≤ ≤cap avl 720.0 1152.0 1296.0delta 8.0 12.0 105.0
Table 5.5: Allocation Decision and Results where capacity limits are not violated
assign lots to tools to minimize unmet demand without exceeding capac avail or workload
Allocation Decision # 1
All capacity constraints metSome workload
Not met
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Tool A Tool B Tool C demand met constraintaverage daily workload delta
oper001 23.0 17.0 0.0 40.0 ≤ 40 0.0oper002 0.0 40.0 0.0 40.0 ≤ 40 0.0oper003 0.0 10.0 30.0 40.0 ≤ 40 0.0oper004 0.0 0.0 40.0 40.0 ≤ 40 0.0oper005 0.0 10.0 40.0 50.0 ≤ 50 0.0oper006 50.0 0.0 0.0 50.0 ≤ 50 0.0oper007 50.0 0.0 0.0 50.0 ≤ 50 0.0
cap used 991.8 1340.0 1241.0 total unmet dmd 0.0constraint ≤ ≤ ≤cap avl 720.0 1152.0 1296.0delta -271.8 -188.0 55.0
Table 5.6: Allocation Decision and Results where capacity limits are not violated
assign lots to tools to minimize unmet demand without exceeding capac avail or workload
Allocation Decision # 2
capacity constraints NOT met All workload met
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
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Clearing Functions within LP
• Armbruster & Uzsoy 2012, “Continuous Dynamic Models, Clearing Functions, and Discrete-Event Simulation in Aggregate Production Planning”, Production Planning Tutorials in Operations Research”, isbn 978-0-9843378-3-5, dx.doi.org/10.1287/educ.1120.0102
– ACF: allocated Clearing Function pp. 10-13• Asmundsson et al, “Production planning models with resources
subject to congestion,” Naval Res. Logistics, vol. 56, no. 2, pp. 142–157, Mar. 2009.
• Asmundsson et al, “Tractable nonlinear production planning models for semiconductor wafer fabrication facilities,” IEEE Trans. Semicond. Manuf., vol. 19, no. 1, pp. 95–111, 2006
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
44
Sources
• Tibbits, B, 1993, “Flexible simulation of a complex semiconductor manufacturing line using a rule-based”, IBM R&D Journal
• Sullivan, G, 1992, “Daily Output Planning”, Expert Systems with Applications
• Sullivan, G, 1995, “Dynamically Generated Rapid Response Capacity Planning Model”
• Bermon, S., and S. Hood. 1999. Capacity optimization planning system (CAPS). Interfaces 29 (5): 31–50.
• Bagchi, S et al 2008. A Full-factory Simulator As A Daily Decision-support Tool For 300mm Wafer Fabrication Productivity, MASM 2008
• Zisgen, H. et al 2008. A Queueing Network Based System To Model Capacity And Cycle Time For Semiconductor Fabrication, MASM 2008
• Schelasin, R 2011. Using Static Capacity Modeling And Queuing Theory Equations To Predict Factory Cycle Time Performance In Semiconductor Manufacturing, MASM 2011
• Levy, J. et al 2010,”Method For Determining Amount of Product Released Into a Time Sensitive Operation”, MASM 2010
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
45
Sources• Armbruster & Uzsoy 2012, “Continuous Dynamic Models, Clearing Functions, and Discrete-Event
Simulation in Aggregate Production Planning”, Production Planning Tutorials in Operations Research”, isbn 978-0-9843378-3-5, dx.doi.org/10.1287/educ.1120.0102
• Kacar, Irdeem, & Uzsoy, 2011, “An Experimental Comparison of Production Planning Using Clearing Functions and Iterative Linear Programming-Simulation Algorithms”, IEEE Transactions on Semiconductor MFG,
• Irdeem, Kacar, & Uzsoy, 2010, An Exploratory Analysis of Two Iterative Linear Programming—Simulation Approaches for Production Planning, Ieee Transactions On Semiconductor Manufacturing, Vol. 23, No. 3, August 2010
• Asmundsson et al, “Production planning models with resources subject to congestion,” Naval Res. Logistics, vol. 56, no. 2, pp. 142–157, Mar. 2009.
• Asmundsson et al, “Tractable nonlinear production planning models for semiconductor wafer fabrication facilities,” IEEE Trans. Semicond. Manuf., vol. 19, no. 1, pp. 95–111, 2006
• H. Missbauer. Models of the transient behaviour of production units to optimize the aggregate material flow. International Journal of Production Economics 118(2):387{397, 2002.
• H. Missbauer and R. Uzsoy. Optimization models for production planning. K. Kempf,• P. Keskinocak, and R. Uzsoy, eds. Planning Production and Inventories in the Extended Enter-• prise: A State of the Art Handbook. Springer Verlag, New York, 437{508,A47 2011.• [60] F. Modigliani and F. E. Hohn. Production planning over time and the nature of the
expectation
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
46
Presentations at MASM 2012
• Using Iterative Simulation to Incorporate Load-Dependent Lead Times in Master Planning Heuristics; Lars Moench and Thomas Ponsignon (Infineon). Abstract: In this paper, we consider heuristics for master planning in semiconductor manufacturing. While lead times are typically assumed as fixed in production planning, we use iterative simulation to take load-dependent lead times into account. An AutoSched AP simulation model of a semiconductor supply chain is used for implementing the scheme. Simulation results show that the iterative scheme converges fast and leads to less variable, more profitable production plans compared to planes obtained by the fixed lead time approach.
• Product Mix Optimization for a Semiconductor Fab: Modeling Approaches and Decomposition Techniques; Andreas Klemmt, Martin Romauch, Walter Laure (Infineon Technologies). Abstract: For optimizing a semiconductor fab we are aiming to match the production capabilities, capacities and the demand in the most profitable way. In this paper we address a linear model of the product mix problem considering product dependent demand limits (obligations and demand forecast) and profits while respecting the the capacity bounds of the production facility. Since the capacity consumption is highly depended on choosing from different production alternatives we are implicitly solving a static capacity planning problem for each product mix. This kind of planning approach is supported by the fluid flow concept of complete resource pooling in high traffic. We propose a general model that considers a wide range of objectives and we introduce a heuristic based on a decomposition of the static capacity planning problem. The computational study of the approaches is based on real world data and on randomly generated instances.
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
47
Presentations at MASM 2012
• An Evaluation of an Option Contract in Semiconductor Supply Chains Konstanze Knoblich (Infineon Technologies) and Cathal Heavey and Peter Williams (University of Limerick), Abstract: The purpose of this paper is to evaluate an option contract within a semiconductor supply chain consisting of one semiconductor manufacturer and one customer. In an option contract the customer pays an upfront fee (option price) for an option to purchase product. A simulation model is used to compare the performance of an option contract against a standard supply contract used in a semiconductor supply chain in terms of delivery performance and costs for the supply chain partners
Fordyce, Fournier, Milne, SinghIllusion of FAB Capacity in Central Planning – hunt for CAPAVAIL
48