Stochastic programming is a framework for modeling optimization problems that involve uncertainty.
Dr. Ekin`s focus is on the solution of expectation-based stochastic programs using simulation algorithms such as Markov chain Monte Carlo methods.
- Ph.D. thesis
- Essays in Simulation-based Stochastic Programming
- Ekin, T., Polson, N., Soyer, R. (2014) Augmented MCMC Simulation for Two-Stage Stochastic Programs with Recourse. Decision Analysis, 11(4), 250–264
- Aktekin, T., Ekin, T. (2016). Stochastic Call Center Staffing with Uncertain Arrival, Service and Abandonment Rates: A Bayesian Perspective. Naval Research Logistics, 63(6), 460–478.
- Ekin, T. (2017). Integrated Maintenance and Production Planning with Endogenous Uncertain Yield. Reliability Engineering and System Safety. OnlineFirst.
- Working Papers
- Ekin, T., Polson, N., Soyer, R. Augmented Nested Sampling for Stochastic Programs with Recourse and Endogenous Uncertainty. (under review).
- Ekin, T., Aktekin, T. Call Center Staffing Optimization with Uncertainty in Dependent Arrival, Service and Abandonment Rates. (under preparation).
- Ekin, T. (Principal), Musal, R. (Co-Principal), Grant, "Overpayment Models for Medical Audits: Multiple Scenarios", McCoy College of Business, Texas State University - San Marcos, $2,500.00, Funded. (sub: February 17, 2014, start: September 1, 2014, end: January 1, 2015).
- Ekin, T. (Principal), Grant, "Stochastic Call Center Staffing with Uncertain Arrival, Service and Abandonment Rates", Research Enhancement Grant, Texas State's Research Enhancement Program, Texas State University, $8,000.00, Funded. (sub: October 20, 2015, start: January 1, 2016, end: December 1, 2016).
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