Stochastic Optimization with Industrial Applications
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Randomized, Online, and Approximation Algorithms".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 80
Special Issue Editors
Interests: industrial net zero transition; automation in mining and beneficiation; hybrid systems and optimal control; stochastic optimization and games
Special Issues, Collections and Topics in MDPI journals
Interests: optimization theory; algorithms and its applications; interior-point methods; symmetric optimization; symmetric cone complementarity problem
Special Issues, Collections and Topics in MDPI journals
Interests: optimal control theory and methods
Special Issue Information
Dear Colleagues,
Stochastic optimization has emerged as an essential tool for addressing real-world industrial challenges characterized by intrinsic uncertainty in processes, demand, supply, and system performance. This Special Issue, “Stochastic Optimization with Industrial Applications”, aims to bring high-quality contributions that advance both the theory and practice of stochastic optimization, emphasizing industrial applicability. We invite submissions of papers that introduce innovative models, algorithms, and computational methods for addressing uncertainty in complex systems, along with case studies demonstrating successful applications across various sectors, including manufacturing, energy, logistics, supply chains, transportation, healthcare, and finance.
Submissions that highlight the integration of stochastic optimization with cutting-edge technologies, such as artificial intelligence, machine learning, big data analytics, and digital twins, are particularly encouraged. This Issue also investigates advanced methods such as simulation-based optimization, robust and chance-constrained programming, stochastic dynamic programming, and decomposition techniques. By showcasing innovative approaches and impactful applications, this Special Issue intends to serve as a platform for researchers and practitioners to exchange ideas, foster collaboration, and outline future directions for applying stochastic optimization to real-world industrial challenges.
Dr. Honglei Xu
Prof. Dr. Guoqiang Wang
Prof. Dr. Changjun Yu
Prof. Dr. Lei Wang
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. Algorithms is an international peer-reviewed open access monthly 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 1800 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 optimization
- industrial applications
- supply chain management
- energy systems
- manufacturing optimization
- transportation and logistics
- robust optimization
- chance-constrained programming
- federated optimization
- data-driven optimization
- intelligent decision making
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