Deakin University
Browse

File(s) under permanent embargo

Evaluation of tender solutions for aviation training using discrete event simulation and best performance criteria

conference contribution
posted on 2016-02-16, 00:00 authored by A Novak, L Tracey, V Nguyen, Michael JohnstoneMichael Johnstone, Vu LeVu Le, Douglas CreightonDouglas Creighton
This paper describes a novel discrete event simulation (DES) methodology for the evaluation of aviation training tenders where performance is measured against “best performance” criteria. The objective was to assess and compare multiple aviation training schedules and their resource allocation plans against predetermined training objectives. This research originated from the need to evaluate tender proposals for the Australian Defence Aviation Training School that is currently undergoing aviation training consolidation and helicopter rationalization. We show how DES is an ideal platform for evaluating resource plans and schedules, and discuss metric selection to objectively encapsulate performance and permit an unbiased comparison. DES allows feasibility studies for each tender proposal to assure they satisfy system and policy constraints. Consequently, to create an objective and fair environment to compare tendered solutions, what-if scenarios have been strategically examined to consider improved implementations of the proposed solutions.

History

Event

Winter Simulation Conference (2015: Huntington Beach, Calif.)

Pagination

2680 - 2691

Publisher

IEEE

Location

Huntington Beach, Calif.

Place of publication

Piscataway, N.J.

Start date

2015-12-06

End date

2015-12-09

ISBN-13

9781467397414

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2015, IEEE

Title of proceedings

WSC2015: Proceedings of the 2015 Winter Simulation Conference

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC