@article{slocum_jones_fletcher_mcconnell_hodgson_taheri_wilson_2020, title={Improving chemotherapy infusion operations through the simulation of scheduling heuristics: a case study}, volume={2}, ISSN={2047-6965 2047-6973}, url={http://dx.doi.org/10.1080/20476965.2019.1709908}, DOI={10.1080/20476965.2019.1709908}, journal={Health Systems}, publisher={Informa UK Limited}, author={Slocum, Ryan F. and Jones, Herbert L. and Fletcher, Matthew T. and McConnell, Brandon M. and Hodgson, Thom J. and Taheri, Javad and Wilson, James R.}, year={2020}, month={Feb}, pages={1–16} }
 @article{kearby_winz_hodgson_kay_king_mcconnell_2020, title={Modeling and transportation planning for US noncombatant evacuation operations in South Korea}, volume={4}, ISSN={2399-6439 2399-6439}, url={http://dx.doi.org/10.1108/JDAL-05-2019-0010}, DOI={10.1108/JDAL-05-2019-0010}, number={1}, journal={Journal of Defense Analytics and Logistics}, publisher={Emerald}, author={Kearby, John A. and Winz, Ryan D. and Hodgson, Thom J. and Kay, Michael G. and King, Russell E. and McConnell, Brandon M.}, year={2020}, month={Feb}, pages={41–69} }
 @article{mcconnell_hodgson_kay_king_liu_parlier_thoney-barletta_wilson_2019, title={Assessing uncertainty and risk in an expeditionary military logistics network}, volume={7}, ISSN={1548-5129 1557-380X}, url={http://dx.doi.org/10.1177/1548512919860595}, DOI={10.1177/1548512919860595}, abstractNote={ Uncertainty is rampant in military expeditionary operations spanning high-intensity combat to humanitarian operations. These missions require rapid planning and decision-support tools to address the logistical challenges involved in providing support in often austere environments. The US Army’s adoption of an enterprise resource planning system provides an opportunity to develop automated decision-support tools and other analytical models designed to take advantage of newly available logistical data. This research presents a tool that runs in near-real time to assess risk while conducting capacity planning and performance analysis designed for inclusion in a suite of applications dubbed the Military Logistics Network Planning System, which previously only evaluated the mean sample path. Logistical data from combat operations during Operation Iraqi Freedom drive supply requisition forecasts for a contingency scenario in a similar geographic environment. A nonstationary queueing network model is linked with a heuristic logistics scheduling methodology to provide a stochastic framework to account for uncertainty and assess risk. }, journal={The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology}, publisher={SAGE Publications}, author={McConnell, Brandon M and Hodgson, Thom J and Kay, Michael G and King, Russell E and Liu, Yunan and Parlier, Greg H and Thoney-Barletta, Kristin and Wilson, James R}, year={2019}, month={Jul}, pages={154851291986059} }
 @article{schwartz_mcconnell_parlier_2019, title={How Data Analytics Will Improve Logistics Planning}, volume={51}, url={https://www.army.mil/article/223842/how_data_analytics_will_improve_logistics_planning}, number={3}, journal={Army Sustainment}, author={Schwartz, B. and McConnell, B.M. and Parlier, G.H.Jul–Sep}, year={2019}, pages={54–57} }
 @article{rogers_mcconnell_hodgson_kay_king_parlier_thoney barletta_2018, title={A Military Logistics Network Planning System}, volume={23}, DOI={10.5711/1082598323405}, abstractNote={This paper presents a proof of concept for a Military Logistics Network Planning System (MLNPS) to be used during mission planning to quickly identify a robust logistical footprint that can adequately sustain units deployed in an expeditionary environment. The logistical network is modeled using an efficient form of goal seeking deterministic discrete event simulation to process supply requisitions through the logistical network. The queueing information obtained from the simulation informs capacity adjustments to the network to maximize efficiency. This process of simulation and network tuning continues interactively until an adequate and robust logistical footprint is found. During the planning stages, the MLNPS can be used to identify and mitigate logistical problems instead of waiting to react to backlogs when the military’s operations would have already been affected. Designed to run as an app on the Army’s ERP system (GCSS-Army), the MLNPS can also be used during operations to inform commanders of expected operational impacts on logistics. Contingency operation scenarios are used to demonstrate the MLNPS’ capabilities.}, number={4}, journal={Military Operations Research}, author={Rogers, Matthew B. and McConnell, Brandon M. and Hodgson, Thom J. and Kay, Michael G. and King, Russell E. and Parlier, Greg and Thoney Barletta, Kristen}, year={2018}, pages={5–24} }
 @inproceedings{moore_mcconnell_wilson_2018, title={Simulation-based Evaluation On Integrating Additive Manufacturing Capability In A Deployed Military Environment}, DOI={10.1109/wsc.2018.8632474}, note={annote: Moore, T. A., McConnell, B. M., & Wilson, J. R. (2018). Simulation-based Evaluation On Integrating Additive Manufacturing Capability In A Deployed Military Environment. Proceedings of the 2018 Winter Simulation Conference, 3721–3729.}, booktitle={Proceedings of the 2018 Winter Simulation Conference}, publisher={IEEE}, author={Moore, T.A. and McConnell, B.M. and Wilson, J.R.}, year={2018}, pages={3721–3729} }
 @inproceedings{seminelli_wilson_mcconnell_2017, title={Implementing discrete event simulation to improve optometry clinic operations}, DOI={10.1109/WSC.2016.7822258}, booktitle={Proceedings - Winter Simulation Conference}, author={Seminelli, M.D. and Wilson, J.W. and McConnell, Brandon}, year={2017}, pages={2157–2168} }