Name
Title | ||
|---|---|---|
A stochastic Programming Approach to Optimal recruitment in Australian Naval Aviation Training. |
Abstract | ||
|---|---|---|
We tackle some of the challenges of optimal recruitment strategies of pilots and other aircrew for Royal Australian Navy aviation. Aside from financial costs, too many students in the training system can create bottlenecks and harm morale, while too few poses a risk to delivering operational capability. We propose a stochastic programming approach that can determine the minimum number of students to recruit and how to distribute those students amongst the training system, in order to meet a capability target with a prescribed probability. This approach is parallelizable and reasonably computationally efficient, and utilizes multiple randomized trials of a greedy method to seek a global minimum. Our work provides insights into the relationship between minimization of entry recruitment numbers and minimization of total student numbers in the system, as well as the relationship between risk constraints and convergence to local minima.
|
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/WSC.2018.8632347 | WSC |
Field | DocType | ISSN |
Markov process,Systems engineering,Computer science,Training system,Harm,Operations research,Aviation,Maxima and minima,Greedy algorithm,Minification,Stochastic programming | Conference | 0891-7736 |
ISBN | Citations | PageRank |
978-1-5386-6570 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cameron Pike | 1 | 0 | 0.34 |
| Ana Novak | 2 | 0 | 1.35 |
| B. Moran | 3 | 111 | 21.09 |
| David Kirszenblat | 4 | 0 | 0.68 |
| Brendan Hill | 5 | 0 | 0.68 |