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International Conference on Applied Mathematical Modeling in Industrial Processes - (ICAMMIP-27)

28th - 29th April 2027 , Malaga - Spain

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Call For Papers

The (ICAMMIP-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 Applied Mathematics, 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:

  • Mathematical modeling in industrial processes
  • Optimization techniques for manufacturing systems
  • Applications of simulation in industry
  • Statistical process control in manufacturing
  • Mathematical modeling of production systems
  • Data analytics in industrial applications
  • Stochastic modeling in supply chain management
  • Applications of queuing theory in industry
  • Optimization in logistics and distribution
  • Mathematical modeling of quality control
  • Applications of operations research in industry
  • Statistical methods for production planning
  • Mathematical modeling of energy systems
  • Simulation techniques for industrial processes
  • Applications of machine learning in manufacturing
  • Statistical analysis of operational efficiency
  • Mathematical modeling of inventory systems
  • Optimization in resource allocation
  • Statistical methods for demand forecasting
  • Applications of mathematical modeling in industry

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.