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Outline
- Overview of Scheduling and Challenges
- Officer Aviation (OA) scheduling context
- Challenges in modelling
- RAAF Historical Use of Simulation
- Proposed Simulation concept/prototype
- Prototype results
- Next Steps
- Discussion
Aim Provide a strategic overview of how simulation can enhance - - PowerPoint PPT Presentation
Aim Provide a strategic overview of how simulation can enhance - - PowerPoint PPT Presentation
SIMULATION IN SCHEDULING Presentation to ADFA Conference FLTLT Lee Gordon-Brown Adrian Xavier 28 Sep 2017 Aim Provide a strategic overview of how simulation can enhance individual training scheduling Outline Overview of Scheduling and
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Scheduling
Overview Focus Question: How can AFTG improve scheduling to deliver Effective, Efficient and Essential education and training to meet Air Force current and future needs? Background: AFTG - education and training delivered to RAAF, Army and Navy personnel involving 327 different courses, > 8000 students/pa many with multiple annual course sessions. Current Challenges:
- scheduling challenges for staff and required resources,
- inefficient pooling of personnel waiting for courses,
- no formal model or assessment of scheduling options,
- limited integration with workforce planning.
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Of Officer icer Aviat iation ion (OA OA)
10 courses per year
DFR Delay? Delay?
1474 1714
OCU 1FTS Recruit Targets DFR FSB OSB 12 days ADFA 1095 days IOC 85 days 0-365 days AVMED 2 days BFTS 117 days 2FTS 185 days COMSURV 25 days ACO 150 days ABM 90 days MPR 90 days ACCAL 90 days AEWC FIX P3 C130 SATC JBACC 180 days JBACAC 70 days FJ 1SQN 79 76 OCU
12 12 12 12 1107 99 339 214 216 97 99 12 97 97 214
AVMED 2 days
99 249 249 339 216 401 339 ACO 364 401 Pilot 426 12 377 377 1472 377 474 1472 1589 1472 1474 1589 1591 1591 1776 214 394 1714 ACO 1739 1776 Pilot 1801 394 464 364 ACO ?? 426 Pilot ?? 1739 ACO ?? 1801 Pilot ?? 1472 1652
Delay? Delay? Delay? Delay? Delay? Delay? Delay? Delay? Delay? Delay? Delay? Delay? Delay? Delay? Delay?
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8 graduate types
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Modelling Challenge
- Computational complexity
- Multi-objective
- Stochastic
- Business rules (soft/hard constraints)
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Use of Simulation
History in RAAF
- Task trainer
- Cockpit/Maintenance simulators
- Marshalling simulators
- Mathematical simulation (Monte Carlo)
- System dynamics
- Discrete event
- Intelligent agent
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Simulation Concepts
- Data relationships
- Business rules
- Input variables, parameters
- Output variables (many)
Prototype
- Score on pooling
- Report on everything
- 3 courses, resources, instructors
- 10000 replications for feasible calendar
0.5 1 Pr(PassRate) Pass Rate
Pass Rate
0.5 1 Pr(PassRate) Pass Rate
Pass Rate
0.5 1 Pr(PassRate) Pass Rate
Pass Rate 7
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Prototype Results
- Business rules
- Parameters
- Objective basket
- Pseudocode
- Distributions
- Pooling time
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Report
Course C1 C2 C3 Annual Demand (students) 100 120 80 Annual Enrols Mean 143.09 161.60 119.99 S.D 3.80 6.92 2.04 Max 151 173 130 Min 131 150 115 Panel Size Mean 16.00 21.01 14.99 S.D 1.22 1.78 0.71 Max 18 24 16 Min 14 18 14 Panels Mean 8.94 7.69 8.00 S.D 0.23 0.46 0.05 Max 9 8 9 Min 8 7 8 Delta (Output-DLOC) Mean 7.21 9.39 4.04 S.D 5.11 6.50 3.03 Max 24 27 14 Min
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Pr(0+) 0.94 0.93 0.93 Pr(10+) 0.33 0.52 0.04
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Next Next Steps eps
- AnyLogic (2017)
- Discrete event
- Intelligent agents (students, instructors)
- 3 Wings (ATW, GTW, RAAFCOL)
- Validation against ATP
- Evaluation of current ATP as scenario
- Output variable basket (2018)
- Maintenance
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