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 Challenges • Officer Aviation (OA) scheduling context • Challenges in modelling • RAAF Historical Use of Simulation • Proposed Simulation concept/prototype • Prototype results • Next Steps • Discussion 2

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 . 3

Of Officer icer Aviat iation ion (OA OA) 10 courses per year 8 graduate types Delay? Delay? Delay? Delay? Delay? Delay? Delay? 426 Pilot ?? Delay? Delay? Delay? 99 339 401 Pilot 426 364 ACO ?? 339 ACO 364 249 339 1FTS AEWC ABM 90 days 97 99 99 249 FIX P3 AVMED ACO 2 days MPR 90 days C130 150 days 1472 1474 ACCAL 90 days 12 1107 97 214 ADFA FJ 1095 days BFTS 1474 1714 1SQN 0 12 12 12 117 days 377 1472 79 214 216 216 401 0-365 76 Recruit Targets DFR FSB OSB DFR 1472 1589 COMSURV 12 days days OCU AVMED 2FTS 25 days 2 days 185 days OCU 12 97 12 377 1589 1591 1591 1776 IOC 1714 ACO 1739 85 days Delay? Delay? 1776 Pilot 1801 1801 Pilot ?? 377 474 1739 ACO ?? SATC 214 394 Delay? Delay? JBACC 180 days 1472 1652 394 464 JBACAC Delay? Delay? 70 days Delay? 4

Modelling Challenge • Computational complexity • Multi-objective • Stochastic • Business rules (soft/hard constraints) 5

Use of Simulation History in RAAF • Task trainer • Cockpit/Maintenance simulators • Marshalling simulators • Mathematical simulation (Monte Carlo) • System dynamics • Discrete event • Intelligent agent 6

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 Pass Rate Pass Rate Pass Rate Pr(PassRate) Pr(PassRate) Pr(PassRate) 0 0.5 1 0 0.5 1 0 0.5 1 Pass Rate Pass Rate Pass Rate 7

Prototype Results • Business rules • Parameters • Objective basket • Pseudocode • Distributions • Pooling time 8

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 -11 -8 -6 Pr(0+) 0.94 0.93 0.93 Pr(10+) 0.33 0.52 0.04 9

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 10

Ques Questions ions 11