Search Results - "Jenkins, Phillip R."

A multi-objective, bilevel programming methodology to identify spatiotemporal transportation distribution network vulnerabilities.

Authors: Long, Carson G. Lunday, Brian J. Jenkins, Phillip R.

Source: Annals of Operations Research; Jun2026, Vol. 361 Issue 3, p1345-1369, 25p

Bayesian Graph Traversal.

Authors: Caballero, William N. Jenkins, Phillip R. Banks, David et al.

Source: Decision Analysis (INFORMS). Mar2026, Vol. 23 Issue 1, p11-27. 17p.

On Large Language Models in National Security Applications.

Authors: Caballero, William N. Jenkins, Phillip R.

Source: Stat. Jun2025, Vol. 14 Issue 2, p1-10. 10p.

Approximate Dynamic Programming for Military Medical Evacuation Dispatching Policies.

Authors: Jenkins, Phillip R. Robbins, Matthew J. Lunday, Brian J.

Source: INFORMS Journal on Computing; Winter2021, Vol. 33 Issue 1, p2-26, 25p

Characterizing military medical evacuation dispatching and delivery policies via a self-exciting spatio-temporal Hawkes process model.

Authors: Frial, Virbon B. Jenkins, Phillip R. Robbins, Matthew J.

Source: Journal of the Operational Research Society; Jul2024, Vol. 75 Issue 7, p1239-1260, 22p

Spatiotemporal Network Vulnerability Identification for the Material Routing Problem: A Bilevel Programming Approach.

Authors: Long, Carson G. Lunday, Brian J. Jenkins, Phillip R.

Source: Military Operations Research. 2024, Vol. 29 Issue 4, p63-88. 26p.

Solving the military medical evacuation dispatching, preemptive rerouting, redeploying, and delivering problem via tree-based machine learning and approximate dynamic programming approaches.

Authors: Frial, Virbon B. Robbins, Matthew J. Jenkins, Phillip R.

Source: Expert Systems with Applications; Nov2025, Vol. 292, pN.PAG-N.PAG, 1p

An Air Force Pilot Training Recommendation System Using Advanced Analytical Methods.

Authors: Forrest, Nicholas C. Hill, Raymond R. Jenkins, Phillip R.

Source: INFORMS Journal on Applied Analytics. Mar/Apr2022, Vol. 52 Issue 2, p198-209. 12p.

Approximate dynamic programming for the military aeromedical evacuation dispatching, preemption-rerouting, and redeployment problem.

Authors: Jenkins, Phillip R. Robbins, Matthew J. Lunday, Brian J.

Source: European Journal of Operational Research; Apr2021, Vol. 290 Issue 1, p132-143, 12p

Examining military medical evacuation dispatching policies utilizing a Markov decision process model of a controlled queueing system.

Authors: Jenkins, Phillip R. Robbins, Matthew J. Lunday, Brian J.

Source: Annals of Operations Research; Dec2018, Vol. 271 Issue 2, p641-678, 38p

Responsible machine learning for United States Air Force pilot candidate selection.

Authors: Wasilefsky, Devin Caballero, William N. Johnstone, Chancellor et al.

Source: Decision Support Systems; May2024, Vol. 180, pN.PAG-N.PAG, 1p

Toward automated instructor pilots in legacy Air Force systems: Physiology-based flight difficulty classification via machine learning.

Authors: Caballero, William N. Gaw, Nathan Jenkins, Phillip R. et al.

Source: Expert Systems with Applications; Nov2023, Vol. 231, pN.PAG-N.PAG, 1p

Solving the joint military medical evacuation problem via a random forest approximate dynamic programming approach.

Authors: Rodriguez, Channel A. Jenkins, Phillip R. Robbins, Matthew J.

Source: Expert Systems with Applications; Jul2023, Vol. 221, pN.PAG-N.PAG, 1p

An approximate dynamic programming approach for solving an air combat maneuvering problem.

Authors: Crumpacker, James B. Robbins, Matthew J. Jenkins, Phillip R.

Source: Expert Systems with Applications; Oct2022, Vol. 203, pN.PAG-N.PAG, 1p

Poisoning finite-horizon Markov decision processes at design time.

Authors: Caballero, William N. Jenkins, Phillip R. Keith, Andrew J.

Source: Computers & Operations Research; May2021, Vol. 129, pN.PAG-N.PAG, 1p