Combining interactive and automated scheduling

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1 Combining interactive and automated scheduling Todor Dimitrov, Michael Baumann Dresden, Fraunhofer IOSB 1

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 Fraunhofer IOSB 2

A similar problem already solved: Navigation Automated scaling, different types: 2D, 3D, air photos, street view Current position is always known Different maps for different

route estimation and evaluation Drivers should know their favored routes Automated rerouting (on wrong turn, on traffic jam) Relevant traffic data continuously observed (avg.

3 A similar problem already solved: Navigation Automated scaling, different types: 2D, 3D, air photos, street view Current position is always known 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 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 Street = machine car = job Fraunhofer IOSB 3

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

different systems for rough and detailed scheduling Production data collection helps observing the current state (if present and correctly used) In practice

4 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 Fraunhofer IOSB 4

Improvement demand in scheduling Today Many production management (in ERP or MES or on machine) GUI on a PC with keyboard and mouse Table view The last who saves his schedule wins / Collaboration

5 Improvement demand in scheduling Today Many production management (in ERP or MES or on machine) GUI on a PC with keyboard and mouse Table view The last who saves his schedule wins / Collaboration over phone Real time production data collection Batch or manual triggered scheduler Tom orrow Single system GUI on mobile devices using touch screen / beamers with body gestures Ergonomic graphical components Intelligent multi user collaboration Real time production scheduling Intelligent cooperative algorithms Fraunhofer IOSB 5

6 GUI technics for ergonomic use Methods of visual analytics for representation of granularity levels day week month quarter of stock forecast Always up-to-date (over data push) Fraunhofer IOSB 6

7 GUI technics for ergonomic use 2 multiuser / multiple devices Navigation between objects, drill down over time / resource / job hierarchy job operation machine customer material time Fraunhofer IOSB 7

8 Cooperation algorithms humans Is it possible? Cooperation scenarios Algorithm suggestion? algorithm as adviser seek for bad parts to optimize advise Human interaction? algorithm as assistant correct, fulfill learn justify, advise Fraunhofer IOSB 8

9 Cooperation algorithms humans 2 No AI algorithms needed, possible with the today s algorithms Algorithms assume much more factors! Urgent conflicts r 1 r 2 Relevance to the current time Human interactions Schedule stability vs. optimal solution r 3 r 4 r 5 r 6 t o t Algorithms behave as a human planner more predictable and acceptable for human users Use the 100% available computational time. Heuristics use < 1%! Fraunhofer IOSB 9

10 Example benchmarks of the electronic industry Test case 1: 70 machines 2223 jobs, operations (over production period of 3 months) Objective: Reduce tardiness and changeover time Test case 2: 25 machines, 6 workers 88 jobs, operations Objective: Reduce avg. lead time Restriction: no tardiness Fraunhofer IOSB 10

11 Changeover times [days] Test case 1 Ist Real Ablauf flow Tardiness [days] TOP Pläne TOP Schedules Fraunhofer IOSB 11

12 Test case 2 Case Real flow 18,39 Avg. net lead time [days] (improvement) Scheduler XXX 14,83 (19,34%) ProVis.APS 11,88 (19,93%) Lower Bound* 5,03 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 * Using unlimited capabilities Fraunhofer IOSB 12

13 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 Fraunhofer IOSB 13

14 Thank you for attention Dr.-Ing. Michael Baumann Tel: Mail: Todor Dimitrov Tel: Mail: Fraunhofer IOSB Fraunhoferstr Karlsruhe Web Fraunhofer IOSB 14

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