Advanced Scheduling and Optimization: Cutting the Costs of Manufacturing

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Advanced Scheduling and Optimization Cutting
the Costs of Manufacturing

• Brian Drabble
• Computational Intelligence Research Laboratory
• www.cirl.uoregon.edu
• drabble_at_cirl.uoregon.edu
• On Time Systems, Inc
• www.otsys.com

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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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Problem Characteristics

• Task details
• resource requirements
• deadlines/release times
• value

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Problem Characteristics

• Task details
• Resource characteristics
• type
• capacity
• availability
• speed, etc.

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Problem Characteristics

• Task details
• Resource characteristics
• Precedences
• necessary orderings between tasks

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Problem Characteristics

• Constraints
• setup costs
• exclusions
• reserve capacity
• union rules/business rules

• Task details
• Resource characteristics
• Precedences

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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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Heuristic Search

• A heuristic prefers some choices over others
• Search explores heuristically preferred options

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Limited Discrepancy Search

• Better model of how heuristic search fails

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Limited Discrepancy Search

• LDS-n deviates from heuristic exactly n times on
  path from root to leaf

LDS-1
LDS-0
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Schedule Packing

• Post-processing to exploit opportunities

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2
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Schedule Packing

• schedule longest chains first
• starting from right

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2
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Schedule Packing

• repeat, starting from the left

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2
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Squeaky Wheel Optimization
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Squeaky Wheel Optimization
Analyze
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Squeaky Wheel Optimization
Prioritize
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Squeaky Wheel Optimization
Prioritize
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Squeaky Wheel Optimization
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Scalability
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Applications
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Aircraft Assembly

• McDonnell Douglas / Boeing
• 570 tasks, 17 resources, various capacities
• MDs scheduler took 2 days to schedule
• needed
• better schedules (1 day worth 200K1M)
• rescheduler that can get inside production cycles

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Problem Specification

• Task/precedence specification
• mostly already existed for regulatory reasons

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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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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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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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The Optimizer

• LDS to generate seed schedules
• Schedule packing to optimize
• intensification improves convergence speed
• etc.

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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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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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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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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

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Submarine Construction

• General Dynamics / Electric Boat
• 7000 activities per hull, approx 125 resources
• Electric Boats scheduler takes 6 weeks
• needed
• cheaper schedules
• faster schedules of contingencies

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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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Performance One Boat

• Labor costs of existing schedule 155m
• Time to produce existing schedule 6 weeks
• 15 reduction in cost, 50x reduction in schedule
  development time

Iteration Time Savings
1 2 min 8.4 13.0M 7
10 min 11.4 17.7M 20
34 min 11.8 18.2M Ultimate 24hrs
15.5 24.0M
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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 49M 7
60 min 10.2 65M 20 4 hours
10.7 68M Ultimate 4 days 11.5 73M
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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
0
3000
6000
Time (Minutes)
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Semi-Flexible Constraints Pointer Based
Attack the IAD before power system
0
3000
6000
Time (Minutes)
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Semi-Flexible Constraints Pointer Based
Attack the IAD before power system
0
3000
6000
Time (Minutes)
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Semi-Flexible Constraints Ripple Based
Attack the IAD before power system
0
3000
6000
Time (Minutes)
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Semi-Flexible Constraints Ripple Based
Attack the IAD before power system
0
3000
6000
Time (Minutes)
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Semi-Flexible Constraints Ripple Based
Attack the IAD before power system
0
3000
6000
Time (Minutes)
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Semi-Flexible Constraints Ripple Based
Attack the IAD before power system
0
3000
6000
Time (Minutes)
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Common Task Model
Information Control
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Example Problem (2)

• The AWACS aborts on take off!

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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
?