Randomized rounding: a technique for provably good algorithms and algorithmic proofs
Combinatorica - Theory of Computing
Journal of the ACM (JACM)
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Scenario Reduction Algorithms in Stochastic Programming
Computational Optimization and Applications
Adjustable robust solutions of uncertain linear programs
Mathematical Programming: Series A and B
A Robust Optimization Approach to Dynamic Pricing and Inventory Control with no Backorders
Mathematical Programming: Series A and B
Computational complexity of stochastic programming problems
Mathematical Programming: Series A and B
Retailer-Supplier Flexible Commitments Contracts: A Robust Optimization Approach
Manufacturing & Service Operations Management
A Robust Optimization Approach to Inventory Theory
Operations Research
Confidence level solutions for stochastic programming
Automatica (Journal of IFAC)
Bounds for multistage stochastic programs using supervised learning strategies
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Computational Optimization and Applications
Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning
INFORMS Journal on Computing
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Stochastic programming with step decision rules (SPSDR) aims to produce efficient solutions to multistage stochastic optimization problems. SPSDR, like plain multistage Stochastic Programming (SP), operates on a Monte Carlo ''computing sample'' of moderate size that approximates the stochastic process. Unlike SP, SPSDR does not strive to build a balanced event tree out of that sample. Rather, it defines a solution as a special type of decision rule, with the property that the decisions at each stage are piecewise constant functions on the sample of scenarios. Those pieces define a partition of the set of scenarios at each stage t, but the partition at t+1 need not be refinement of the partition at t. However, the rule is constructed so that the non-anticipativity condition is met, a necessary condition to make the rules operational. To validate the method we show how to extend a non-anticipatory decision rule to arbitrary scenarios within a very large validation sample of scenarios. We apply three methods, SPSDR, SP and Robust Optimization, to the same 12-stage problem in supply chain management, and compare them relatively to different objectives and performance criteria. It appears that SPSDR performs better than SP in that it produces a more accurate estimate (prediction) of the value achieved by its solution on the validation sample, and also that the achieved value is better.