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© Fraunhofer IOSB 1
Todor Dimitrov, Michael Baumann
Combining interactive and automated scheduling
Dresden, 24.01.2013
© Fraunhofer IOSB 2
Content
Motivation: A similar problem already solved
Old school and today’s scheduling and improvement demand
GUI technics for ergonomic use
Cooperation algorithms humans
Evaluation examples of electronic industry
Summary
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A similar problem already solved:Navigation
Automated scaling, different types: 2D, 3D, air photos, street view
Current position is always known
Automated rerouting (on wrong turn, on traffic jam)
Relevant traffic data continuously observed (avg. speed, traffic flow)
Multi-criteria routing: time, price, scenic roads, gas prices
Assuming what driver may favor, learning from other drivers
Different maps for different scales
Need to ask about the current position sometimes
Must stop and search for alternate route / ask people
Relevant traffic data only if the right radio station
Difficult route estimation and evaluation
Drivers should know their favored routes
Street = machine car = job
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Old school and today’s scheduling
Production schedule on a paper somewhere in the shop floor
Need to observe the current production progress / ask workers about the state
Hard to adapt the schedule on breakdowns / tardiness
Hard to create good schedules
Hard to optimize with different goals
Computer-aided plan board, different systems for rough and detailed scheduling
Production data collection helps observing the current state (if present and correctly used)
In practice batch runs with human supervision
Simple heuristics produce schedules that need oft to be improved by a human
Still hard to optimize with different goals
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Improvement demand in scheduling
Today Tomorrow
Many production management(in ERP or MES or on machine)
Single system
GUI on a PC with keyboard and mouse
GUI on mobile devices using touch screen / beamers with body gestures
Table view Ergonomic graphical components
The last who saves his schedule wins / Collaboration over phone
Intelligent multi user collaboration
Real time production data collection Real time production scheduling
Batch or manual triggered scheduler Intelligent cooperative algorithms
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quartermonth
week
GUI technics for ergonomic use
Methods of visual analytics for representation
of granularity levels
of stock forecast
Always up-to-date (over data push)
day
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GUI technics for ergonomic use 2
multiuser / multiple devices
Navigation between objects, drill down over time / resource / job hierarchy
joboperation
machine
materialcustomer
time
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Cooperation algorithms humans
Is it possible?
Cooperation scenarios
Algorithm suggestion? algorithm as adviser
Human interaction? algorithm as assistant
71
3
7
1
3
4
7
1
3
4
advise
correct, fulfill
justify, advise
learn
seek for “bad” partsto optimize
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Cooperation algorithms humans 2
No AI algorithms needed, possible with the today’s algorithms
Algorithms assume much more factors!
Urgent conflicts
Relevance to the current time
Human interactions
Schedule stability vs. optimal solution
Algorithms behave as a human planner more predictable and acceptable for human users
Use the 100% available computational time. Heuristics use < 1%!
𝑟1𝑟2
𝑟3𝑟4𝑟5𝑟6
𝑡𝑡𝑜
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Example benchmarks of the electronic industry
70 machines
2223 jobs, 13184 operations (over production period of 3 months)
Objective: Reduce tardiness and changeover time
25 machines, 6 workers
88 jobs, 12000 operations
Objective: Reduce avg. lead time
Restriction: no tardiness
Test case 1:
Test case 2:
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Test case 1
Ist Ablauf TOP Pläne
Ch
an
geo
ver
tim
es
[days
]
Tardiness [days]
TOP SchedulesReal flow
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Test case 2
Case Avg. net lead time [days] (improvement)
Real flow 18,39
Scheduler XXX 14,83 (19,34%)
ProVis.APS 11,88 (19,93%)
Lower Bound* 5,03
* Using unlimited capabilities
KPI Value [days]
Avg. net lead time 11,88
Max. net lead time 19,19
Avg. earliness 5,08
Max earliness 13,47
Non running jobs only
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Summary
On the way to the navigation system for the production
Scheduler to become advisor and assistant
More ergonomic GUIs nowadays
GUI and algorithms to be “more” real time capable
Part of the functions already implemented in various systems
High quality production optimization can turn into the most important feature of MES in (near) the future
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Thank you for attention
Dr.-Ing. Michael BaumannTel: +49 721 6091-374Mail: [email protected]
Todor DimitrovTel: +49 721 6091-470Mail: [email protected]
Fraunhofer IOSBFraunhoferstr. 176131 Karlsruhe
Webwww.provis-aps.dewww.iosb.fraunhofer.de/ILT
