Stochastic Programming: Theory, Methods, and Applications

Dear Colleagues,

Uncertainty is the key ingredient in financial planning, airline scheduling, unit commitment in power systems, multi-player game problems, and so on. Today, dynamic decision making under uncertainty forms the foundation for numerous fundamental problems in operations research and management science. Stochastic programming is a powerful and widely adopted tool to cope with decision-making problems under uncertainty. It has seen recent advances with a far-reaching impact involving risk measures, distributionally robust optimization, and applications in areas ranging from energy and natural resources to economics and finance to statistics and machine learning.

This Special Issue aims to report the state of the art in theory, methods, and applications of stochastic programming. Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

(i) Theoretical analyses of dynamic stochastic programming, including structural analysis, stability analysis, asymptotic analysis, consistency, and rates of convergence;

(ii) Numerical algorithms for solving stochastic programming problems, including issues such as scenario generation or reduction, sampling methods such as sample (average) approximation, stochastic gradient methods, and decomposition techniques;

(iii) Applications of dynamic stochastic programming for the modeling and solution of academic problems such as multi-player game problems, machine learning, and practical problems such as financial planning, risk management, dynamic resource allocation, airline scheduling, and unit commitment in power systems.

I look forward to receiving your contributions.

Prof. Dr. Zhiping Chen
Prof. Dr. Yonggan Zhao
Guest Editors

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• stochastic programming
• stability
• asymptotic analysis
• rates of convergence
• scenario generation or reduction
• sampling methods
• stochastic gradient methods
• machine learning
• financial management
• unit commitment