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 52
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
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
