Dr. Matthew J. Robbins, Professor of Operations Research

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Dr. Matthew JD Robbins is a Professor of Operations Research in the Department of Operational Sciences, Air Force Institute of Technology (AFIT). His research focuses on the advancement of operations research and artificial intelligence techniques for solving computational stochastic optimization problems. In particular, Dr. Robbins is interested in the design, development, and testing of approximate dynamic programming and reinforcement learning algorithms that utilize simulation, mathematical programming, statistical machine learning, and design of experiment techniques to solve large-scale sequential decision-making problems under uncertainty. His applied research interests involve problems related to defense, to include the dispatch of medical evacuation assets, the routing of military distribution assets, the control of autonomous mobile sensors and weapons systems, and the management of workforce systems. He currently serves as an associate editor for the journals Military Operations Research and Naval Research Logistics. He also serves as an elected council member of the INFORMS Military and Security Society.

Education

Ph.D., Industrial Engineering, University of Illinois, 2010

Advisor: Sheldon H. Jacobson

(IIE Pritsker Doctoral Dissertation Award - 1st Place)

M.S., Operations Research, Air Force Institute of Technology, 2005

(Distinguished Graduate)

B.S., Computer Systems Engineering, University of Arkansas, 1999

Awards

2019 Best Paper Award, Omega - The International Journal of Management Science (jointly awarded for work with co-author Brian J. Lunday)

2018  Richard H. Barchi Prize, Military Operations Research Society (jointly awarded for work with co-authors Phillip R. Jenkins, Nathaniel D. Bastian, and Brian J. Lunday)

2014  Outstanding Young Member Award, INFORMS Cincinnati-Dayton Chapter

2011  Pritsker Doctoral Dissertation Award  -  First Place, Institute of Industrial Engineers

2005  Distinguished Graduate, Master of Science in Operations Research Graduate Program, Air Force Institute of Technology,

2005  Inductee, Omega Rho International Honor Society for OR/MS

2000  Distinguished Graduate, USAF Intelligence Officer School

Publications

Google Scholar Page

1. Frial*, V.B., Jenkins, P.R., Robbins, M.J. (2023). Characterizing military medical evacuation dispatching and delivery policies via a self-exciting spatio-temporal Hawkes process model. Journal of the Operational Research Society (To appear, accepted 2 Jul 23).
2. Liles^, J.M., Robbins, M.J., and Lunday, B.J. (2023). Solving Nonstationary Markov Decision Processes via Contextual Decomposition: A Military Air Battle Management Application. Expert Systems with Applications, 233, 120949. DOI: https://doi.org/10.1016/j.eswa.2023.120949. 
3. Rodriguez*, C.A., Jenkins, P.R., and Robbins, M.J. (2023). Solving the Joint Military Medical Evacuation Problem via a Random Forest Approximate Dynamic Programming Approach. Expert Systems with Applications, 221, 119751. DOI: https://doi.org/10.1016/j.eswa.2023.119751.
4. Liles*, J.M., Robbins, M.J., and Lunday, B.J. (202X). Improving Defensive Air Battle Management by Solving a Stochastic Dynamic Assignment Problem via Approximate Dynamic Programming. European Journal of Operational Research. (To Appear, Accepted 14 Jun 22.) DOI: https://doi.org/10.1016/j.ejor.2022.06.031.
5. Haywood^, A.B., Lunday, B.J., Robbins, M.J., and Pachter, M.N. (202X). Enterprise Resource Location-Allocation for Intruder Detection and Interdiction. Military Operations Research. (To Appear, Accepted 20 Nov 2021.)
6. Hoecherl^, J.C., Robbins, M.J., Borghetti, B.J., and Hill, R.R. (2022). Partially Autoregressive Machine Learning: Development and Testing of Methods to Predict United States Air Force Retention. Computers and Industrial Engineering, 171, 108424. DOI: https://doi.org/10.1016/j.cie.2022.108424.
7. Crumpacker*, J.B., Robbins, M.J., and Jenkins, P.R. (2022). An Approximate Dynamic Programming Approach for Solving an Air Combat Maneuvering Problem. Expert Systems with Applications, 203, 117448. DOI: https://doi.org/10.1016/j.eswa.2022.117448.
8. Haywood^, A.B., Lunday, B.J., and Robbins, M.J. (2022). Intruder Detection and Interdiction Modeling: A Bilevel Programming Approach for Ballistic Missile Defense Asset Location. Omega, 110, 102640. DOI: https://doi.org/10.1016/j.omega.2022.102640.
9. Haywood^, A.B., Lunday, B.J., Robbins, M.J., and Pachter, M.N. (2021). The Weighted Intruder Path Covering Problem. European Journal of Operational Research, 297 (1), 347—358. DOI: https://doi.org/10.1016/j.ejor.2021.05.038.
10. Jenkins^, P.R., Robbins, M.J., and Lunday, B.J. (2021). Approximate Dynamic Programming for the Military Aeromedical Evacuation Dispatching, Preemption-Rerouting, and Redeployment Problem. European Journal of Operational Research, 290 (1), 132—143. DOI: https://doi.org/10.1016/j.ejor.2020.08.004.
11. Jenkins^, P.R., Robbins, M.J., and Lunday, B.J. (2021). Approximate Dynamic Programming for Military Medical Evacuation Dispatching Policies. INFORMS Journal on Computing, 33 (1), 2—26. DOI: https://doi.org/10.1287/ijoc.2019.0930.
12. Summers*, D.S., Robbins, M.J., and Lunday, B.J. (2020). An Approximate Dynamic Programming Approach for Comparing Firing Policies in a Networked Air Defense Environment. Computers & Operations Research, 117 (May), 104890. DOI: https://doi.org/10.1016/j.cor.2020.104890.
13. McKenna*, R.S., Robbins, M.J., Lunday, B.J., and McCormack*, I.M. (2020). Approximate Dynamic Programming for the Military Inventory Routing Problem.  Annals of Operations Research, 288 (1), 391—416. DOI: https://doi.org/10.1007/s10479-019-03469-8.
14. Jenkins^, P.R., Lunday, B.J., and Robbins, M.J. (2020). Robust, Multi-Objective Optimization for the Military Medical Evacuation Location-Allocation Problem. Omega, 97 (December), 102088. DOI: https://doi.org/10.1016/j.omega.2019.07.004.
15. Robbins, M.J., Jenkins^, P.R., Bastian, N.D., and Lunday, B.J. (2020). Approximate Dynamic Programming for the Aeromedical Dispatching Problem: Value Function Approximation Utilizing Multiple Level Aggregation. Omega, 91 (March), 102020. (2018 Barchi Prize, MORS.) DOI: https://doi.org/10.1016/j.omega.2018.12.009.
16. Jenkins*, P.R., Robbins, M.J., and Lunday, B.J. (2018). Examining Military Medical Evacuation Dispatching Policies Utilizing a Markov Decision Process Model of a Controlled Queueing System. Annals of Operations Research, 271 (2), 641-678. DOI: https://doi.org/10.1007/s10479-018-2760-z.
17. Lunday, B.J. and Robbins, M.J. (2019). Collaboratively-developed Vaccine Pricing and Stable Profit Sharing Mechanisms. Omega, 84, 102-113. (2019 Best Paper Award, Omega) DOI: https://doi.org/10.1016/j.omega.2018.04.007.
18. Nystrom*, J.K., Robbins, M.J., Deckro, R.F., and Morris, J.F. (2018). Simulating Attacker and Defender Strategies within a Dynamic Game on Network Topology. Journal of Simulation, 12 (4), 307-331. DOI: https://doi.org/10.1057/s41273-017-0054-0.
19. Schofield*, J.A., Zens*, C.L., Hill, R.R., and Robbins, M.J. (2018). Utilizing Reliability Modeling to Analyze United States Air Force Officer Retention. Computers and Industrial Engineering, 117, 171-180. DOI: https://doi.org/10.1016/j.cie.2018.01.013.
20. Widrick*, R.S., Nurre, S.G., and Robbins, M.J. (2018). Optimal Policies for the Management of an Electric Vehicle Battery Swap Station. Transportation Science, 52 (1), 59-79. DOI: https://doi.org/10.1287/trsc.2016.0676.
21. Boardman*, N.T., Lunday, B.J., and Robbins, M.J. (2017). Heterogeneous Surface-to-Air Missile Defense Battery Location: A Game Theoretic Approach. Journal of Heuristics, 23 (6), 417-447. DOI: https://doi.org/10.1007/s10732-017-9350-0.
22. Davis*, M.T., Robbins, M.J., and Lunday, B.J. (2017). Approximate Dynamic Programming for Missile Defense Interceptor Fire Control. European Journal of Operational Research, 259 (3), 873-886. DOI: https://doi.org/10.1016/j.ejor.2016.11.023.
23. Lunday, B.J. and Robbins, M.J. (2016). Informing Pediatric Vaccine Procurement Policy via the Pediatric Formulary Design, Pricing, and Production Problem. IIE Transactions, 48 (12), 1112-1126. DOI: http://dx.doi.org/10.1080/0740817X.2016.1198064.
24. Rettke*, A.J., Robbins, M.J., and Lunday, B.J. (2016). Approximate Dynamic Programming for the Dispatch of Military Medical Evacuation Assets. European Journal of Operational Research, 254 (3), 824-839. DOI: https://doi.org/10.1016/j.ejor.2016.04.017.
25. Han*, C.Y., Lunday, B.J., and Robbins, M.J. (2016). A Game Theoretic Model for the Optimal Location of Integrated Air Defense System Missile Batteries. INFORMS Journal on Computing, 28 (3), 405-416. DOI: https://doi.org/10.1287/ijoc.2016.0690.
26. Keneally*, S.K., Robbins, M.J., and Lunday B.J. (2016). A Markov Decision Process Model for the Optimal Dispatch of Military Medical Evacuation Assets. Health Care Management Science, 19 (2), 111-129. DOI: https://doi.org/10.1007/s10729-014-9297-8.
27. Robbins, M.J. and Lunday, B.J. (2016). A Bilevel Formulation of the Pediatric Vaccine Pricing Problem. European Journal of Operational Research, 248 (2), 634-645. DOI: https://doi.org/10.1016/j.ejor.2015.06.075.
28. Colombi, J.M., Robbins, M.J., Burger*, J.A., and Weber, Y.S. (2015). Interface Evaluation for Open System Architectures Using Multiobjective Decision Analysis. Military Operations Research, 20 (2), 55-69.
29. Behzad, B., Jacobson, S.H., and Robbins, M.J. (2015). A Symmetric Capacity-Constrained Differentiated Oligopoly Model for the United States Pediatric Vaccine Market with Linear Demand. IIE Transactions, 47 (11), 1252-1266. DOI: http://dx.doi.org/10.1080/0740817X.2015.1009759.
30. Robbins, M.J. and Jacobson, S.H. (2015). Analytics for Vaccine Economics and Pricing: Insights and Observations. Expert Review of Vaccines, 14 (4), 605-616. DOI: http://dx.doi.org/10.1586/14760584.2015.985662.
31. Guzman*, J.D., Deckro, R.F., Robbins, M.J., Morris, J.F., and Ballester, N.A. (2014). An Analytical Comparison of Social Network Measures. IEEE Transactions on Computational Social Systems,  1 (1), 35-45. DOI: https://doi.org/10.1109/TCSS.2014.2307451.
32. Robbins, M.J., Jacobson, S.H., Shanbhag, U.V., and Behzad, B. (2014). The Weighted Set Covering Game: A Vaccine Pricing Model for Pediatric Immunization. INFORMS Journal on Computing, 26 (1), 183-198. DOI: https://doi.org/10.1287/ijoc.2013.0556.
33. Bernardoni*, B.J., Deckro, R.F., and Robbins, M.J. (2013). Using Social Network Analysis to Inform Stabilization Efforts. Military Operations Research, 18 (4), 37-60.
34. Jacobson, S.H., King, D.M., Ryan, K.C., and Robbins, M.J. (2012). Assessing the Long Term Benefit of Banning the Use of Hand-Held Wireless Devices While Driving. Transportation Research Part A: Policy and Practice, 46 (10), 1586-1593. DOI: https://doi.org/10.1016/j.tra.2012.08.007.
35. Robbins, M.J. and Jacobson, S.H. (2011). Pediatric Vaccine Procurement Policy: The Monopsonist’s Problem. Omega, 39 (6), 589-597. DOI: https://doi.org/10.1016/j.omega.2010.12.004.
36. Robbins, M.J., Jacobson, S.H., and Sewell, E.C. (2010). Pricing Strategies for Combination Pediatric Vaccines and their Impact on Market Share: Pediarix or Pentacel? Health Care Management Science, 13 (1), 54-64. DOI: https://doi.org/10.1007/s10729-009-9109-8.
37. Nikolaev, A.G., Robbins, M.J., and Jacobson, S.H. (2010). Evaluating the Impact of Legislation Prohibiting Hand-Held Cell Phone Use While Driving. Transportation Research Part A: Policy and Practice, 44 (3), 182-193. DOI: https://doi.org/10.1016/j.tra.2010.01.006.

* denotes MS student author.

^ denotes PhD student author.