Stochastic Modelling and Optimization
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".
Deadline for manuscript submissions: 24 March 2026 | Viewed by 36
Special Issue Editors
Interests: stochastic optimization; robust optimization; optimization theory and methods; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Interests: optimization method and its applications; stochasitic programming; robust optimization
Special Issues, Collections and Topics in MDPI journals
Interests: stochastic optimization; stochastic dominance; distributionally robust optimization; optimization theory and methods
Special Issue Information
Dear Colleagues,
Stochastic modeling and optimization have emerged as pivotal tools for addressing complex, real-world problems across diverse fields such as finance, engineering, and energy systems. These methodologies provide decision makers with robust solutions that are essential for managing risk and enhancing efficiency.
The increasing availability of data and the growing recognition of uncertainty have further underscored the importance of advanced mathematical techniques and efficient optimization algorithms in stochastic modeling and optimization. This Special Issue seeks to showcase cutting-edge research on stochastic modeling, optimization methodologies, and their applications, fostering interdisciplinary collaboration and driving innovation. By bridging theoretical advancements with practical problem solving, it aims to create a platform for researchers to share insights and contribute to the advancement of stochastic modeling and optimization.
We invite researchers to submit their latest findings on all aspects of stochastic modeling, optimization methods, and their practical applications. The scope of this Special Issue covers a broad range of topics, including, but not limited, to stochastic optimization, robust optimization, dynamic programming, large-scale optimization, nonconvex optimization, and their integration with data-driven approaches. We also welcome submissions highlighting applications in diverse domains such as finance, logistics, healthcare, engineering, transportation, manufacturing, energy systems, machine learning, and reinforcement learning, with the goal of addressing real-world challenges effectively.
Dr. Jia Liu
Dr. Shen Peng
Dr. Yu Mei
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- stochastic modeling
- stochastic optimization
- robust optimization
- distributionally robust optimization
- dynamic programming
- convex optimization
- nonconvex optimization
- data-driven optimization
- machine learning
- reinforcement learning
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