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Systems & Industrial Engineering _:_ Güzin Bayraksan _:_ The University of Arizona | |
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Keller, B.D. and G. Bayraksan, “Multiple Resource Constrained Job Scheduling under Uncertainty: A Stochastic Programming Approach,” under review. (paper [pdf]) Bayraksan, G. and D. P. Morton, “A Sequential Sampling Procedure for Stochastic Programming,” conditionally accepted to Operations Research. (paper [pdf]) Bayraksan, G. and D. P. Morton, “Assessing Solution Quality in Stochastic Programs,” Mathematical Programming, 108: 495-514, 2006. (preprint [pdf]) Chung, G., K. Lansey and G. Bayraksan, “Reliable Water Supply System Design under Uncertainty,” conditionally accepted to Journal of Environmental Modelling and Software. Bayraksan, G., D. P. Morton and A. Partani “Simulation-Based Optimality Tests for Stochastic Programs,” Planning Under Uncertainty: Stochastic Programming, G. Infanger and G. B. Dantzig (eds), Kluwer Series on Advances in Mathematical Programming, forthcoming. (paper [pdf]) Bayraksan, G. and D.P. Morton, “Sequential Sampling for Solving Stochastic Programs,” Proceedings of the 2007 Winter Simulation Conference, 2007. Bayraksan, G. and D. P. Morton, “Testing Solution Quality in Stochastic Programming: A Single Replication Procedure,” Proceedings of the 16th Symposium of IASC on Computational Statistics, Physica-Verlag/Springer, Prague, Czech Republic, 2004. (paper [pdf]) Monte Carlo simulation-based methods for Stochastic Programming: * Fixed-Width Sequential Stopping Rules for A Class of Stochastic Programs (abstract) * A Sampling-Based Sequential Approximation Method (abstract) * A Probability Metrics Approach for Bias and Variance Reduction in Optimality Gap Estimation (abstract) Modeling and Optimization of Complex Stochastic Systems: * Multiple Resource Constrained Job Scheduling under Uncertainty * Water Reuse System with two-stage Stochastic Integer Programming
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