UT Arlington Faculty: Victoria Chen, Bill Corley, Jay Rosenberger
Collaborators: Cristiano Cervellera, Seoung Bum Kim
Ph.D. Students: Julia Tsai (Georgia Tech 2002), Zehua Yang (2004), Aihong Wen (2005), Dachuan Shih (2006), Huiyuan Fan (2008), Prashant Tarun (2008), Bancha Ariyajunya, Diana Martinez, Surachai Charoensri, Subrat Sahu (2011), Bancha Ariyajunya (2012), Diana Martinez (2013), Asama Kulvanitchaiyanunt (2014), Zirun Zhang (2015), Ying Chen (2017), Ashkan Aliabadi Farahani (2019), Xinglong Ju (2019)
Funding: National Science Foundation 2008-11, 2011-15
Topics: Design and analysis of computer experiments (DACE) based ADP, Sequential algorithms, State transition modeling, Value function approximation
Description: Many problems require that decisions be made over time, making dynamic programming models particularly applicable. Examples include control of inventory systems, revenue management, mechanical operations, environmental systems, behavioral intervention programs, and adaptive medical treatment. Because exact solutions for these highly complex systems are computationally intractable, we have developed methods yielding approximate solutions to some of the largest problems in the literature. The COSMOS approach involves a statistical perspective using experimental design, computer experiments, and data mining to generate parsimonious approximations that model only the critical information. COSMOS applications that involve dynamic decision-making include: airline revenue management, joint with Sabre Holdings; environmentally-friendly airplane deicing decisions, joint with Dallas-Fort Worth International Airport; assignment of nurses to patients admitted during a shift, and ozone pollution control strategies. In particular, our methods are being adapted to handle multiple objectives, convexity vs. nonconvexity, and a mix of continuous and categorical variables.
- Ariyajunya, B., Y. Chen, V. C. P. Chen, and S. B. Kim (2017). “Data Mining for State Space Orthogonalization in Adaptive Dynamic Programming.” Expert Systems with Applications, 76, pp. 49–58. COSMOS Technical Report 16-02.
- Martinez, D. L., D. T. Shih, V. C. P. Chen, and S. B. Kim (2015). “A Convex Version of Multivariate Adaptive Regression Splines.” Computational Statistics and Data Analysis, 81, pp. 89–106. COSMOS Technical Report 12-01.
- Shih, D. T., S. B. Kim, V. C. P. Chen, J. M. Rosenberger, and V. L. Pilla (2014). “Efficient Computer Experiment-Based Optimization through Variable Selection.” Annals of Operations Research, Special Issue on Data Mining and Informatics, 216, pp. 287–305. COSMOS Technical Report 07-02.
- Tarun, P. K., V. C. P. Chen, and H. W. Corley (2014). “Divergence Behaviour of the Successive Geometric Mean Method of Pairwise Comparison Matrix Generation for a Multiple Stage, Multiple Objective Optimization Problem.” Journal of Multi-Criteria Decision Analysis, 21, pp. 197–208. COSMOS Technical Report 12-07.
- Fan, H.-Y., P. K. Tarun, and V. C. P. Chen (2013). “Adaptive Value Function Approximation for Continuous-State Stochastic Dynamic Programming.” Computers and Operations Research, 40, pp. 1076–1084. COSMOS Technical Report 11-06.
- Ariyajunya, B., V. C. P. Chen, and S. B. Kim (2010). “Orthogonalized Dynamic Programming State Space for Efficient Value Function Approximation.” In Proceedings of the 2010 IE Research Conference, Cancun, Mexico, June.
- Sahu, S., V. C. P. Chen, and C.-F. Lin (2009). “TreeMARS Models for a Decision Support System for Pain Management.” In Proceedings of the 2009 IE Research Conference, Miami, FL.
- Yang, Z., V. C. P. Chen, M. E. Chang, M. L. Sattler, and A. Wen (2009). “A Decision-Making Framework for Ozone Pollution Control.” Operations Research, 57(2), pp. 484-498. COSMOS Technical Report 05-04.
- Tarun, P. K., V. C. P. Chen, and H. W. Corley (2008). “Incorporating Decision Makers’ Inputs in a Dynamic Multiple Stage, Multiple Objective Model.” In Proceedings of the 2008 IE Research Conference, Vancouver, BC, Canada.
- Cervellera, C., A. Wen, and V. C. P. Chen (2007). “Neural Network and Regression Spline Value Function Approximations for Stochastic Dynamic Programming.” Computers and Operations Research, 34, pp. 70-90. Technical Report COSMOS 04-05. The University of Texas at Arlington. Arlington, TX.
- Tarun, P. K., V. C. P. Chen, and H. W. Corley (2007). “A Dynamic Multiple Stage, Multiple Objective Optimization Model.” In Proceedings of the 2007 IE Research Conference, Nashville, TN.
- Yang, Z., V. C. P. Chen, M. E. Chang, T. E. Murphy, and J. C. C. Tsai (2007). “Mining and Modeling for a Metropolitan Atlanta Ozone Pollution Decision-Making Framework.” IIE Transactions, Special Issue on Data Mining, 39, pp. 607–615. COSMOS Technical Report 04-06.
- Cervellera, C., V. C. P. Chen, and A. Wen (2006). “Optimization of a Large-Scale Water Reservoir Network by Stochastic Dynamic Programming with Efficient State Space Discretization.” European Journal of Operational Research, 171, pp. 1139-1151. COSMOS Technical Report 04-04.
- Chen, V. C. P., K.-L. Tsui, R. R. Barton, and M. Meckesheimer (2006). “Design, Modeling, and Applications of Computer Experiments.” IIE Transactions, 38, pp. 273-291.
- Shih, D. T., V. C. P. Chen, and S. B. Kim (2006). “Convex version of Multivariate Adaptive Regression Splines for Optimization.” In Proceedings of the 2006 IE Research Conference, Orlando, FL.
- Tsai, J. C. C. and V. C. P. Chen (2005). “Flexible and Robust Implementations of Multivariate Adaptive Regression Splines within a Wastewater Treatment Stochastic Dynamic Program.” Quality and Reliability Engineering International, 21, pp. 689-699.