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Incorporating High-Speed, Optimizing Scheduling into NASA’s EUROPA Planning Architecture AIAA 2012 June 19-21, 2012 Authors:Dick Stottler.

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Presentation on theme: "Incorporating High-Speed, Optimizing Scheduling into NASA’s EUROPA Planning Architecture AIAA 2012 June 19-21, 2012 Authors:Dick Stottler."— Presentation transcript:

1 Incorporating High-Speed, Optimizing Scheduling into NASA’s EUROPA Planning Architecture AIAA 2012 June 19-21, 2012 Authors:Dick Stottler David Presented by:Dick Stottler Copyright © 2012 by Stottler Henke Associates, Inc. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

2 Problem Description Gulf between Artificial Intelligence (AI) planning & scheduling Planning: cascading levels of action choice Scheduling: optimizing choice of resources/time windows larger number of activities and/or resources but with significantly less choice as to what those activities are Traditionally, advanced, robust, autonomous planners have not focused on the scheduling decisions And high-quality, optimizing schedulers have rarely been integrated with such planning systems Yet all planning ultimately must result in specific resources carrying out specific actions (activities) at specific times 2

3 EUROPA Background Extensible Universal Remote Operations Planning Arch. Developed at NASA Ames Research Center A powerful software platform for building configurable and extensible planners for a wide variety of domains. Expressive New Domain Description Language (NDDL) enables the formulation of complex problems High degree of modularity and plug-in architecture Impressive array of missions and research: Mars Exploration Rover (MER), Deep Space 1 (DS1), Earth- observing satellite scheduling (EOS), ISS scheduling, etc. Extending for Action Notation Modeling Language Higher level language 3

4 Aurora Background General Architecture for creating intelligent scheduling systems Architecture mimics human scheduling decision process Useful for generating algorithms that follow the same cognitive processes as human expert schedulers Typically algorithms are linear (or nearly so) and run very fast Like human schedulers, typically very little search or back- tracking (but usually some) Plug-ins for different steps in the scheduling process (preprocessing, ordering queue, selecting resources/time windows, propagating constraints, solving conflicts, post- processing optimization) Library of general and specific methods/heuristics for each EG Bottleneck No Planning (No task generation except for occasional insertion)

5 Scheduling in Aurora vs EUROPA EUROPAAurora PlanningNo Planning Schedule tasks earlyGenerate all tasks then sched. Search-basedLinear Lots of back-trackingVery little Good support for BTOnly swapping/shuffling/moving Least CommitmentMaximal commitment Little support for DMMuch Decision-Making support No post processingPost-processing common Difficult S/S/Moving

6 Simple Example 1 Resource E.g. Power 5 units 4 Sequences of tasks

7 Simple Example (continued)

8 Styles/Synergies of Joint Use Continuum: EUROPA generates tasks and EUROPA schedules tasks EUROPA generates tasks, solver includes Aurora Algorithms –Use least commitment, constraint prop, & back-tracking of EUROPA EUROPA generates tasks, sends tasks to Aurora for scheduling, Aurora does all constraints, no least commitment, can use EUROPA decision points to track major scheduling options EUROPA generates tasks, Aurora Schedules them EUROPA using Aurora information when it makes task generation decisions (bottlenecks (current or previous schedule)) Quick Infeasible feedback Aurora algorithms schedule much faster; better schedules

9 SCORE Application SCORE developed by NASA AMES using EUROPA for International Space Station (ISS) scheduling Currently Tasks input and manually scheduled EUROPA used to check constraints Screen Capture Temporal Constraints Resource and Condition Constraints Beyond Astronauts’ Time Resource Sets 14 step video – will only show a few steps

10 Scenario Background 130 June 14 th Activities Pulled from NASA files Require an astronaut resource Or Temporally constrained to such an activity Used the conditions and resources from the files Added additional constraints Focused on automatic scheduling and supporting constraints types/flexibility Did NOT implement User Interface Prototype UI is the Aurora default

11 ISS Scheduling Prototype Screen

12 Temporal constraints XFS = eXact Finish to Start. The first activity must end exactly when the second activity starts. FS = Finish to Start. The first activity must end before the second activity can start and there may or may not be a gap of time in-between. FS (0 – x minute Gap) = Finish to Start with an x minute gap. The first activity must end before the second activity can start and the gap in between can not be longer than x minutes and a 0 minute gap is preferred. FS (Minimum x minute Gap) Late-End t = The activity must end by t or sooner. Early-Start t = The activity can not start sooner than t. Concurrent = The two activities must occur at the same time.

13 Resource and Condition Constraints Resources/Quantities:Added Resources: HRFM_VIDEO_PORT4To prevent concurrency when KU_DL_BW_UNRESERVED93.5it appeared a resource existed. HRDL_TOTAL760PC_RESOURCE NODE_1_VIDEO_PORT1ORIENTATION_RESOURCE VIDEO_SYSTEM_TOTAL380EHS_TEST_RESOURCE NODE_3_VIDEO_PORT1 HRFM_DIGITAL_PORT1Condition: ALL_S_AVAIL = 1 LAB_CAMCORDER3 DIGITAL_TOTAL150 RACK_LAO1_HRL1 VSU_34 PC_RESOURCE: 1 ORIENTATION_RESOURCE: 1 EHS_TEST_RESOURCE:1

14 Resource Sets FE_3_4: FE_3 or FE_4: 367, 468, 443, 472, 424 -> 425 FE_5_6: FE_5 or FE_6: 434, 368, 441, 408 -> 411 -> 415 -> 419 Same Resource Constraint: In the case of a chain of activities where a specific resource is not specified but, instead, one of a resource set is specified, it often makes sense that whichever resource is chosen for one activity continues to be the one used for all activities in the chain

15 SCORE Demo Sequence Start (already a 417 ->FS (0-30min)->435): They start together Add 417->XFS->375 Rescheduling inserts it Delete it – they go back together Lengthen 425 from 15 to 45 min Less room so 472 and 443 move later Lengthen 472 from 20 to 30 –> moves from FE_3 to FE_4 Add 417 ->XFS -> 435 Make 435 early start of 14: will move later, and, 417 will move with it Make 392 (at FE_1) early start of 14: will move later, and (XFS chain), 385/390 move too Also, 395 (at ISS_CDR) moves since 392 concurrent constraint 391 moves too (XFS with 395) 15

16 EVA Demo Hand translated from PDDL from TRACLabs Test domain is simplified subset of the TRACLabs model. Tasks modeled at a high level, without spatial or equipment constraints. Bottleneck Implemented in EUROPA Combined with EUROPA’s constraint propagation nicely Picking Resources Originally Ordering Tasks Chain Complexity/Duration/# of Resources for ordering Post-Processing Optimization (Swap and Compress)

17 EVA tasks EVA 2 of ULF 6, Revision T (Dec 2010)

18 EVA tasks

19 EVA Prototype Results Default Europa solver: 1239 steps Run time: minutes Makespan: 6 hours, 25 minutes

20 EVA Prototype Results With bottleneck scheduling and postprocessing: 78 steps Run time: minutes Makespan: 5 hours, 35 minutes

21 EVA Prototype Results 6 hr horizon Default Europa solver: 6000 steps Run time: minutes Makespan: N/A (timeout)

22 EVA Prototype Results 6 hr horizon With bottleneck scheduling and postprocessing: 78 steps Run time: minutes Makespan: 5 hours, 34 minutes

23 23

24 Conclusions 3 rd application (recovery for an Electrical Power System) Different developer - just a few minutes to install our plugin 80 minutes/6600steps/no answer -> 4 minutes/200/optimal answer Can add high-quality scheduling algorithms to EUROPA EUROPA and Aurora’s algorithms are highly complementary EUROPA’s back-tracking search good for action generation Aurora’s near-linear algorithms are more suited for scheduling EUROPA’s back-tracking useful for small number of arbitrary major Aurora decisions Aurora has much better support for moving and shuffling tasks Aurora provides for a very explicit post-processing step EUROPA follows a least commitment strategy (can be expensive) –associated sophisticated constraint propagation –Easily computes data for some of Aurora’s more complex heuristics Aurora’s maximal commitment strategy is more runtime efficient 24


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