Constraint Based Scheduling and Optimization: From Research to Application
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Transcript of Constraint Based Scheduling and Optimization: From Research to Application
![Page 1: Constraint Based Scheduling and Optimization: From Research to Application](https://reader036.fdocuments.net/reader036/viewer/2022062308/56812c58550346895d90e125/html5/thumbnails/1.jpg)
27th Nov 2001 Univ. Nebraska 1
Constraint Based Scheduling and Optimization:
From Research to Application
Brian Drabble
Computational Intelligence Research Laboratory
www.cirl.uoregon.edu
&
On Time Systems, Inc
www.otsys.com
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27th Nov 2001 Univ. Nebraska 2
Overview
• Constraint based scheduling• Algorithms
– LDS and Schedule Pack– Squeaky Wheel Optimization
• Applications– Aircraft assembly– Ship construction
• Future Directions• Summary
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Constraint Based Scheduling
• Problem characteristics• Search based techniques
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27th Nov 2001 Univ. Nebraska 43
Problem Characteristics
–Task details:• resource requirements
• deadlines/release times
• value
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27th Nov 2001 Univ. Nebraska 54
Problem Characteristics
–Task details–Resource characteristics:
• type• capacity• availability• speed, etc.
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27th Nov 2001 Univ. Nebraska 65
Problem Characteristics
¨ Task details¨ Resource characteristics¨ Precedences:
– necessary orderings between tasks
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27th Nov 2001 Univ. Nebraska 76
Problem Characteristics
–Constraints:• setup costs• exclusions• reserve capacity• union rules/business rules
¨ Task details¨ Resource characteristics¨ Precedences
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27th Nov 2001 Univ. Nebraska 87
Problem Characteristics
–Constraints
–Optimization criteria:• makespan, lateness, cost,
throughput
¨ Task details
¨ Resource characteristics¨ Precedences
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Optimization Techniques
• Operations Research (OR)– LP/IP solvers
• seem to be near the limits of their potential
• Artificial Intelligence (AI)– search-based solvers
• performance increasing dramatically• surpassing OR techniques for many problems
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Search-based Techniques
• Systematic– explore all possibilities
• Depth-First Search• Limited Discrepancy Search
• Nonsystematic– explore only “promising” possibilities
• WalkSAT• Schedule Packing
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27th Nov 2001 Univ. Nebraska 1110
Heuristic Search
– A heuristic prefers some choices over others– Search explores heuristically preferred options
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27th Nov 2001 Univ. Nebraska 1211
Limited Discrepancy Search
– Better model of how heuristic search fails
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27th Nov 2001 Univ. Nebraska 1312
Limited Discrepancy Search– LDS-n deviates from heuristic exactly n times
on path from root to leaf
LDS-1LDS-0
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27th Nov 2001 Univ. Nebraska 1413
Schedule Packing– Post-processing to exploit opportunities
1 1
2 2
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27th Nov 2001 Univ. Nebraska 1514
Schedule Packing– schedule longest chains first
• starting from right
1 1
2 2
1 1
2 2
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Schedule Packing– repeat, starting from the left
1 1
2 2
1 1
2 2
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27th Nov 2001 Univ. Nebraska 17
Squeaky Wheel Optimization
• Key insight: scheduling involves two major decisions:– which task to assign next– where to assign it in the schedule
• Create a dual search space– priority space– schedule space
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Priority Space
P
P’
S
S’
Priority Space Solution Space
• Coupled search space
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27th Nov 2001 Univ. Nebraska 19
Architecture• Construct Analyze Prioritize loop
P
P’
S
S’
Priority Space Solution Space
Construct
AnalyzePrioritize
Construct
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27th Nov 2001 Univ. Nebraska 20
Construction• Construct a solution taking each task in
sequence
P
P’
S
S’
Priority Space Solution Space
Construct
AnalyzePrioritize
Construct
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Analysis
• Assign blame problem elements, relatively simple
P
P’
S
S’
Priority Space Solution Space
Construct
AnalyzePrioritize
Construct
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Prioritization
• Adjust priority sequence according to blame
P
P’
S
S’
Priority Space Solution Space
Construct
AnalyzePrioritize
Construct
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27th Nov 2001 Univ. Nebraska 23
Large Coherent Moves
• High priority tasks handled well lower tasks fill in.
P
P’
S
S’
Priority Space Solution Space
Construct
AnalyzePrioritize
Construct
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27th Nov 2001 Univ. Nebraska 24
Squeaky Wheel Optimization
Construct
Mission 1234
AAR 234
SEAD 34
Mission 4567
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27th Nov 2001 Univ. Nebraska 25
Squeaky Wheel Optimization
Analyze
“High attrition rate”
“Outside target time window”
“Low success rate”
“Not attacked”
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Squeaky Wheel Optimization
Prioritize
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Squeaky Wheel Optimization
Prioritize
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Squeaky Wheel Optimization
Construct
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Scalability
0
5
10
15
20
25
0 50 100 150 200 250 300Number of Tasks
% Over Best Solution
TABULP/IPSWO
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27th Nov 2001 Univ. Nebraska 3016
Applications
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27th Nov 2001 Univ. Nebraska 3117
Aircraft Assembly
McDonnell Douglas / Boeing
– ~570 tasks, 17 resources, various capacities
– MD’s scheduler took 2 days to schedule
– needed:
• better schedules (1 day worth $200K–$1M)
• rescheduler that can get inside production cycles
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27th Nov 2001 Univ. Nebraska 3218
Problem Specification
– Task/precedence specification• mostly already existed for regulatory reasons
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27th Nov 2001 Univ. Nebraska 3319
Problem Specification
– Task/precedence specification• mostly already existed for regulatory reasons
– Resource capacity profiles• labor profile available from staffing information• others determined from SOPs, etc.
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27th Nov 2001 Univ. Nebraska 3420
Problem Specification
– Task/precedence specification• mostly already existed for regulatory reasons
– Resource capacity profiles• labor profile available from staffing information• others determined from SOPs, etc.
– Optimization criterion• simple makespan minimization
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27th Nov 2001 Univ. Nebraska 3521
Problem Specification
– Task/precedence specification• mostly already existed for regulatory reasons
– Resource capacity profiles• labor profile available from staffing information• others determined from SOPs, etc.
– Optimization criterion• simple makespan minimization
– Solution checker• available from in-house scheduling efforts
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27th Nov 2001 Univ. Nebraska 3622
The Optimizer
• LDS to generate seed schedules• Schedule packing to optimize
– intensification improves convergence speed
• etc.
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27th Nov 2001 Univ. Nebraska 3723
Performance
– ~570 tasks, 17 resources, various capacities• about 1 second to first solution• about 1 minute to within 2% of best known• about 30 minutes to best schedule known
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27th Nov 2001 Univ. Nebraska 3824
Performance
– ~570 tasks, 17 resources, various capacities• about 1 second to first solution• about 1 minute to within 2% of best known• about 30 minutes to best schedule known
– 10-15% shorter makespan than best in-house• 4 to 6 days shorter schedules
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27th Nov 2001 Univ. Nebraska 3925
Performance
– ~570 tasks, 17 resources, various capacities• about 1 second to first solution• about 1 minute to within 2% of best known• about 30 minutes to best schedule known
– 10-15% shorter makespan than best in-house• 4 to 6 days shorter schedules
– 2 orders of magnitude faster scheduling• scheduler runs inside production cycle• less need for rescheduler
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27th Nov 2001 Univ. Nebraska 4026
Extensions
Boeing:– multi-unit assembly– interruptible tasks– persistent assignments– multiple objectives
• e.g., time to first completion, average makespan, time to completion
• fast enough to use for “what-iffing”– discovered improved PM schedule
• Noise is your friend!!!
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27th Nov 2001 Univ. Nebraska 4127
Submarine Construction
General Dynamics / Electric Boat– 7000 activities per hull, approx 125 resource types– Electric Boat’s scheduler takes 6 weeks– needed:
• cheaper schedules• faster schedules to deal with contingencies
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27th Nov 2001 Univ. Nebraska 42
Problem Specification
• reschedule shipyard operations to reduce wasted labor expenses
• efficient management of labor profiles– reduce overtime and idle time– hiring and RIF costs
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Optimizer
• ARGOS is new technology developed specifically with these goals in mind
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• Labor costs of existing schedule: $155m• Time to produce existing schedule: ~6 weeks
• 15% reduction in cost, 50x reduction in schedule development time
Performance: One Boat
Iteration Time Savings
1 2 min 8.4% $13.0M7 10 min 11.4% $17.7M20 34 min 11.8% $18.2M
Ultimate ~24hrs 15.5% $24.0M
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27th Nov 2001 Univ. Nebraska 45
Performance: Whole Yard
• All hulls, about 5 years of production• Estimated cost of existing schedule: $630M
• No existing software package can deal with the yard coherently
Iteration Time Savings
1 24 min 7.8% $49M7 60 min 10.2% $65M20 4 hours 10.7% $68M
Ultimate 4 days 11.5% 73M
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27th Nov 2001 Univ. Nebraska 46
Extensions
• Shared resources– dry dock– cranes
• Sub-assemblies– provided by different yards and suppliers
• Repair– dealing with new jobs
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Future Applications
• Workflow management– STRATCOM checklist manager– IBM
• E-Business– supply chain management
• Military– air expeditionary forces– logistics
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Future Work
• Robustness• Distributed scheduling• Common task description
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Penalty Box Scheduling
• Sub-set of the tasks with higher probability of success.– 90% probability of destroying 90% of the targets?– 96% probability of destroying 75% of the targets?
• Inability to resource leads to a task “squeak” • Blame score related to user priority and
“uniqueness”• Reduce the target percentage until no
significant improvement is found
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Semi-Flexible Constraints
• The time constraints provided by the users tended to be ad-hoc and imprecise– heuristics based on sortie rate, no of targets, etc– this is what we did last time so it must be right!!
• Not a preference– this is what I want until you can prove otherwise!!
• Two algorithms were investigated– pointer based– ripple based
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Semi-Flexible Constraints: Pointer Based
“Attack the IAD before power system”
IAD-E
0 3000 6000
Time (Minutes)
Power-EIAD-L Power-L
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Semi-Flexible Constraints: Pointer Based
“Attack the IAD before power system”
IAD-E
0 3000 6000
Time (Minutes)
Power-EIAD-L Power-L
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Semi-Flexible Constraints: Pointer Based
“Attack the IAD before power system”
IAD-E
0 3000 6000
Time (Minutes)
Power-EIAD-L Power-L
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Semi-Flexible Constraints: Ripple Based
IAD-E
0 3000 6000
Time (Minutes)
Power-EIAD-L Power-L
“Attack the IAD before power system”
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Semi-Flexible Constraints: Ripple Based
IAD-E
0 3000 6000
Time (Minutes)
Power-E Power-L
“Attack the IAD before power system”
IAD-L
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27th Nov 2001 Univ. Nebraska 56
Semi-Flexible Constraints: Ripple Based
IAD-E
0 3000 6000
Time (Minutes)
Power-E Power-L
“Attack the IAD before power system”
IAD-L
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27th Nov 2001 Univ. Nebraska 57
Semi-Flexible Constraints: Ripple Based
IAD-E
0 3000 6000
Time (Minutes)
Power-LPower-E
“Attack the IAD before power system”
IAD-L
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Common Task Model
Plan Ready Fly Execute Recover “Drop 120, MK-84s from 3 B-52s at location X,Y at 22.00 on D+5”
30 mins 20 mins 40 mins 5 mins 60mins
P R F E RB-52 Flight
P R F E RAAR
P R F E RAWACS
P R F E RBomb Depot
P R F E RCAP Flight Information & Control
P R F E RWeapon Loader
P R F E RSEAD Flight
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Example Problem
• The AWACS aborts on take off!
P R F E RB-52 Flight
P R F E RAAR
P R F E RAWACS
P R F E RBomb Depot
P R F E RCAP Flight
P R F E RWeapon Loader
P R F E RSEAD Flight
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Summary
Advances in search technology: Tasks Resources Type Feasible?
– 1993: 64 6 Job Shop X – 1996: ~570 17 RCPS barely– 1999: 1000s dozens RCPS – 2001: 10000s hundreds RCPS
• Search works!– search-based technology has matured– large, real-world, problems are solvable– tech-transfer path is short
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Questions
?