Advanced Mathematical Programming and Applications in Operations Research
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 20 July 2025 | Viewed by 417
Special Issue Editor
Special Issue Information
Dear Colleagues,
Advanced mathematical programming plays a critical role in solving complex decision-making and optimization challenges within operations research. This field encompasses a wide range of methods, including linear and nonlinear programming, integer programming, and stochastic models, that are used to address real-world problems across various sectors such as supply chain management, finance, and transportation.
Recent advancements in computational tools and algorithms have enabled the development of more sophisticated models that can handle larger datasets, dynamic environments, and multi-objective scenarios. In particular, hybrid approaches that combine mathematical programming techniques with data mining, machine learning, and heuristic methods have shown great promise in improving solution quality and decision efficiency.
We invite researchers and practitioners to submit original research that explores the development, application, and theoretical contributions of advanced mathematical programming in operations research. Submissions showcasing practical implementations and case studies that solve real-world problems are highly encouraged.
Prof. Dr. Sung Won Cho
Guest Editor
Manuscript Submission Information
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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
- operations research
- mathematical programming
- computational optimization
- data-driven optimization
- multi-objective optimization
- computational algorithms
- supply chain management
- machine learning
- heuristic methods
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