Two-stage stochastic programming for interdisciplinary pain management

Na WangChina Business Network, Shanghai, China;

Jay RosenbergerUniversity of Texas at Arlington, Industrial, Manufacturing, & Systems Engineering, Arlington, United States;

Gazi Md Daud IqbalUniversity of Texas at Arlington, Industrial, Manufacturing, & Systems Engineering, Arlington, United States; Correspondence[email protected]

Victoria ChenUniversity of Texas at Arlington, Industrial, Manufacturing, & Systems Engineering, Arlington, United States;

Robert J GatchelUniversity of Texas at Arlington, Psychology, Arlington, United States;

Carl NoeUT Southwestern Medical Center, Pain Management and Anesthesiology, Dallas, United States

Aera Kim LeBoulluecUniversity of Texas at Arlington, Industrial, Manufacturing, & Systems Engineering, Arlington, United States;

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Gazi Md Daud Iqbal, Jay Rosenberger, Victoria Chen, Robert Gatchel & Carl Noe. (2021) Multi-objective two-stage stochastic programming for adaptive interdisciplinary pain management with piecewise linear network transition models. IISE Transactions on Healthcare Systems Engineering 11:3, pages 240-254.
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Victoria C. P. Chen, Yuan Zhou, Alireza Fallahi, Amith Viswanatha, Jingmei Yang, Feng Liu, Nilabh S. Ohol, Yasaman Ghasemi, Ashkan Aliabadi Farahani, Jay M. Rosenberger & Jeffrey B. Guild. (2022) An Optimization Framework to Study the Balance Between Expected Fatalities Due to COVID-19 and the Reopening of U.S. Communities. IEEE Transactions on Automation Science and Engineering 19:2, pages 586-602.
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Two-stage stochastic programming for interdisciplinary pain management

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