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International Conference on Optimization and Mathematical Modeling in Applied Sciences - (ICOMMAS-27)

8th - 9th June 2027 , Budapest - Hungary

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Call For Papers

The (ICOMMAS-27) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Mathematical Modeling are explored to promote cross-domain knowledge exchange and collaborative innovation.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Optimization techniques in applied sciences
  • Mathematical modeling for engineering applications
  • Optimization models for resource management
  • Mathematical approaches to operations research
  • Modeling complex systems in applied sciences
  • Statistical methods in optimization problems
  • Mathematical modeling of supply chains
  • Applications of optimization in engineering
  • Mathematical frameworks for decision analysis
  • Modeling energy systems with optimization
  • Optimization in transportation and logistics
  • Mathematical modeling of production processes
  • Interdisciplinary approaches to optimization
  • Mathematical tools for financial optimization
  • Modeling environmental systems with optimization
  • Optimization techniques for network design
  • Mathematical modeling of risk in optimization
  • Applications of optimization in healthcare
  • Mathematical modeling of project management
  • Optimization in telecommunications and networks

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.