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ORCIDMatthew J. Robbins
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2020 – today
- 2026
[j24]William N. Caballero, Phillip R. Jenkins
, David Banks, Matthew J. Robbins:
Bayesian Graph Traversal. Decis. Anal. 23(1): 11-27 (2026)- 2025
- [j23]Virbon B. Frial, Matthew J. Robbins, Phillip R. Jenkins:
Solving the military medical evacuation dispatching, preemptive rerouting, redeploying, and delivering problem via tree-based machine learning and approximate dynamic programming approaches. Expert Syst. Appl. 292: 128582 (2025) - [i1]William N. Caballero, Phillip R. Jenkins, David Banks, Matthew J. Robbins:
Bayesian Graph Traversal. CoRR abs/2503.05963 (2025) - 2024
- [j22]Virbon B. Frial, Phillip R. Jenkins, Matthew J. Robbins:
Characterizing military medical evacuation dispatching and delivery policies via a self-exciting spatio-temporal Hawkes process model. J. Oper. Res. Soc. 75(7): 1239-1260 (2024) - 2023
- [j21]Joseph M. Liles IV, Matthew J. Robbins, Brian J. Lunday:
Improving defensive air battle management by solving a stochastic dynamic assignment problem via approximate dynamic programming. Eur. J. Oper. Res. 305(3): 1435-1449 (2023) - [j20]Channel A. Rodriguez, Phillip R. Jenkins, Matthew J. Robbins:
Solving the joint military medical evacuation problem via a random forest approximate dynamic programming approach. Expert Syst. Appl. 221: 119751 (2023) - [j19]Joseph M. Liles IV, Matthew J. Robbins, Brian J. Lunday:
Solving nonstationary Markov decision processes via contextual decomposition: A military air battle management application. Expert Syst. Appl. 233: 120949 (2023) - 2022
- [j18]Joseph C. Hoecherl, Matthew J. Robbins, Brett J. Borghetti, Raymond R. Hill:
Partially autoregressive machine learning: Development and testing of methods to predict United States Air Force retention. Comput. Ind. Eng. 171: 108424 (2022) - [j17]Adam B. Haywood, Brian J. Lunday, Matthew J. Robbins, Meir Pachter:
The weighted intruder path covering problem. Eur. J. Oper. Res. 297(1): 347-358 (2022) - [j16]James B. Crumpacker, Matthew J. Robbins, Phillip R. Jenkins:
An approximate dynamic programming approach for solving an air combat maneuvering problem. Expert Syst. Appl. 203: 117448 (2022) - 2021
- [j15]Phillip R. Jenkins, Matthew J. Robbins, Brian J. Lunday:
Approximate dynamic programming for the military aeromedical evacuation dispatching, preemption-rerouting, and redeployment problem. Eur. J. Oper. Res. 290(1): 132-143 (2021) - [j14]Phillip R. Jenkins, Matthew J. Robbins, Brian J. Lunday:
Approximate Dynamic Programming for Military Medical Evacuation Dispatching Policies. INFORMS J. Comput. 33(1): 2-26 (2021) - [c2]Emily S. Graves, Phillip R. Jenkins, Matthew J. Robbins:
Analyzing the Impact of Triage Classification Errors on Military Medical Evacuation Dispatching Policies. WSC 2021: 1-12 - 2020
- [j13]Rebekah S. McKenna, Matthew J. Robbins, Brian J. Lunday, Ian M. McCormack:
Approximate dynamic programming for the military inventory routing problem. Ann. Oper. Res. 288(1): 391-416 (2020) - [j12]Daniel S. Summers, Matthew J. Robbins, Brian J. Lunday:
An approximate dynamic programming approach for comparing firing policies in a networked air defense environment. Comput. Oper. Res. 117: 104890 (2020)
2010 – 2019
- 2018
- [j11]Phillip R. Jenkins, Matthew J. Robbins, Brian J. Lunday:
Examining military medical evacuation dispatching policies utilizing a Markov decision process model of a controlled queueing system. Ann. Oper. Res. 271(2): 641-678 (2018) - [j10]Jill A. Schofield, Christine L. Zens, Raymond R. Hill, Matthew J. Robbins:
Utilizing reliability modeling to analyze United States Air Force officer retention. Comput. Ind. Eng. 117: 171-180 (2018) - [j9]Jared K. Nystrom, Matthew J. Robbins, Richard F. Deckro, James F. Morris:
Simulating attacker and defender strategies within a dynamic game on network topology. J. Simulation 12(4): 1-25 (2018) - [j8]Rebecca S. Widrick, Sarah G. Nurre, Matthew J. Robbins:
Optimal Policies for the Management of an Electric Vehicle Battery Swap Station. Transp. Sci. 52(1): 59-79 (2018) - 2017
- [j7]Michael T. Davis, Matthew J. Robbins, Brian J. Lunday:
Approximate dynamic programming for missile defense interceptor fire control. Eur. J. Oper. Res. 259(3): 873-886 (2017) - [j6]Nicholas T. Boardman, Brian J. Lunday, Matthew J. Robbins:
Heterogeneous surface-to-air missile defense battery location: a game theoretic approach. J. Heuristics 23(6): 417-447 (2017) - 2016
- [j5]Matthew J. Robbins, Brian J. Lunday:
A bilevel formulation of the pediatric vaccine pricing problem. Eur. J. Oper. Res. 248(2): 634-645 (2016) - [j4]Aaron J. Rettke, Matthew J. Robbins, Brian J. Lunday:
Approximate dynamic programming for the dispatch of military medical evacuation assets. Eur. J. Oper. Res. 254(3): 824-839 (2016) - [j3]Chan Y. Han, Brian J. Lunday, Matthew J. Robbins:
A Game Theoretic Model for the Optimal Location of Integrated Air Defense System Missile Batteries. INFORMS J. Comput. 28(3): 405-416 (2016) - [c1]Joseph C. Hoecherl, Matthew J. Robbins, Raymond R. Hill, Darryl K. Ahner:
Approximate dynamic programming algorithms for United States Air Force officer sustainment. WSC 2016: 3075-3086 - 2014
- [j2]Matthew J. Robbins, Sheldon H. Jacobson, Uday V. Shanbhag, Banafsheh Behzad:
The Weighted Set Covering Game: A Vaccine Pricing Model for Pediatric Immunization. INFORMS J. Comput. 26(1): 183-198 (2014) - [j1]Joshua D. Guzman, Richard F. Deckro, Matthew J. Robbins, James F. Morris, Nicholas A. Ballester:
An Analytical Comparison of Social Network Measures. IEEE Trans. Comput. Soc. Syst. 1(1): 35-45 (2014)
Coauthor Index
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