Optimization Letters - SI: Advances in Computational Optimization and Their Modern Applications
Motivation
‘Optimization’ and ‘Computing’ are highly interconnected research domains. This intertwinement has become even more highlighted in recent years with the resurgence of learning-based approaches such as machine learning, deep learning, and reinforcement learning and the emergence of concepts including crowdsourcing, smart and connected communities/systems, and autonomous and electric vehicles in various application domains. Therefore, this special issue seeks to rapidly disseminate concise and short high-quality articles (limited to a total of ten journal pages) to the community on recent advances in computational optimization and their modern applications.
Topics
This special issue of Optimization Letters (this opens in a new tab) is open to all areas of computational optimization, from theoretical research to practical applications, with a particular emphasis on machine learning problems and integrations of machine learning and computational optimization. Topics of interest include, but are not limited to:
- Optimization methods in machine learning
- The interface between optimization and artificial intelligence
- Reinforcement learning approaches for optimization problems
- Exact and approximate optimization algorithms assisted by machine learning approaches
- Constraint programming and decision diagrams
- Computational stochastic optimization
- Computational optimization and open-source solvers
- Computational methods in integer programming, mixed-integer linear programming, and mixed-integer nonlinear programming
- Heuristic search methods
- Multi-objective optimization
- Computational approaches for modern operations research problems in various applications: logistics, scheduling, production, transportation, energy, humanitarian logistics, healthcare, social sciences, robotics, etc.
Submissions
Manuscripts should be prepared using Springer’s LaTeX macro package (this opens in a new tab) and submitted through the journal's editorial manager https://www.editorialmanager.com/optl (this opens in a new tab).
Please use article type: S.I.: AdvCompModAppl2022
Each paper will be peer-reviewed according to the editorial policy of Optimization Letters. Articles should be original, unpublished, and not currently under consideration for publication elsewhere.
Important Dates
Submission Deadline: December 1st, 2022 (We encourage early submissions)
Guest Editors
- Hadi Charkhgard, University of South Florida, U.S.A., hcharkhgard@usf.edu (this opens in a new tab)
- Jinkyoo Park, KAIST, Korea, jinkyoo.park@kaist.ac.kr (this opens in a new tab)
- Changhyun Kwon, University of South Florida, U.S.A., chkwon@usf.edu (this opens in a new tab)
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