Decomposition Algorithms for Stochastic Programming on a Computational Grid
Computational Optimization and Applications
Applying the Minimum Risk Criterion in Stochastic Recourse Programs
Computational Optimization and Applications
Computational Optimization and Applications
A Novel Sampling Approach to Combinatorial Optimization Under Uncertainty
Computational Optimization and Applications
Network Flows Heuristics for Complementary Cell Suppression: An Empirical Evaluation and Extensions
Inference Control in Statistical Databases, From Theory to Practice
Towards Stochastic Constraint Programming: A Study of Online Multi-choice Knapsack with Deadlines
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Bounds for probabilistic integer programming problems
Discrete Applied Mathematics - Workshop on discrete optimization DO'99, contributions to discrete optimization
A multistage stochastic programming algorithm suitable for parallel computing
Parallel Computing - Special issue: Parallel computing in numerical optimization
Journal of Global Optimization
A stochastic programming approach to scheduling in TAC SCM
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Boosted sampling: approximation algorithms for stochastic optimization
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
The role of stochastic programming in communication network design
Computers and Operations Research
Optimal Security Liquidation Algorithms
Computational Optimization and Applications
Treasury Management Model with Foreign Exchange Exposure
Computational Optimization and Applications
On a stochastic sequencing and scheduling problem
Computers and Operations Research
Simultaneous Optimal Control and Discrete Stochastic Sensor Selection
HSCC '09 Proceedings of the 12th International Conference on Hybrid Systems: Computation and Control
Integrated simulation and optimization for wildfire containment
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Intelligent financial decision model of natural disasters risk control
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Production planning in furniture settings via robust optimization
Computers and Operations Research
Approximation algorithms for 2-stage stochastic optimization problems
FSTTCS'06 Proceedings of the 26th international conference on Foundations of Software Technology and Theoretical Computer Science
A branch-and-cluster coordination scheme for selecting prison facility sites under uncertainty
Computers and Operations Research
Sequential pairing of mixed integer inequalities
IPCO'05 Proceedings of the 11th international conference on Integer Programming and Combinatorial Optimization
On two-stage stochastic minimum spanning trees
IPCO'05 Proceedings of the 11th international conference on Integer Programming and Combinatorial Optimization
A stochastic model for the implementation of postponement strategies in global distribution networks
Decision Support Systems
Mathematical and Computer Modelling: An International Journal
An improved Benders decomposition applied to a multi-layer network design problem
Operations Research Letters
The stochastic p-median problem with unknown cost probability distribution
Operations Research Letters
Designing decision support systems for value-based management: A survey and an architecture
Decision Support Systems
A preconditioning technique for Schur complement systems arising in stochastic optimization
Computational Optimization and Applications
Stochastic vehicle routing with recourse
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
The power of recourse for online MST and TSP
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
Geometry of online packing linear programs
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part II
Hardness results for the probabilistic traveling salesman problem with deadlines
ISCO'12 Proceedings of the Second international conference on Combinatorial Optimization
Random fuzzy multi-objective linear programming: Optimization of possibilistic value at risk (pVaR)
Expert Systems with Applications: An International Journal
Benders Decomposition for the Hop-Constrained Survivable Network Design Problem
INFORMS Journal on Computing
Parallel distributed-memory simplex for large-scale stochastic LP problems
Computational Optimization and Applications
Scenario grouping in a progressive hedging-based meta-heuristic for stochastic network design
Computers and Operations Research
Open vehicle routing problem with demand uncertainty and its robust strategies
Expert Systems with Applications: An International Journal
Scheduling a dynamic aircraft repair shop with limited repair resources
Journal of Artificial Intelligence Research
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The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems.In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods.The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition:"The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998)