Advanced Applications of Mathematical Modeling and Optimization in Logistics and Manufacturing

Dear Colleagues,

Mathematical modeling and optimization are essential tools for logistics and manufacturing in today’s global economy. These techniques can be used to solve a wide range of problems, including routing, scheduling, inventory management, and production planning. By using mathematical modeling and optimization, businesses can become more efficient, reduce costs, and improve customer satisfaction. Mathematical modeling and optimization are powerful tools that can improve the efficiency and effectiveness of logistics and manufacturing operations. In addition, machine learning, artificial intelligence, and digital twins are all playing increasingly important roles in logistics and manufacturing. These technologies are used to automate tasks, improve decision-making, and optimize operations. Digital twins are virtual representations of physical systems. They can simulate real-world processes and predict how such processes will behave in the future. Machine learning, artificial intelligence, and digital twins are all powerful technologies that have the potential to revolutionize logistics and manufacturing.

As the Guest Editor of this Special Issue, I invite practitioners, professionals, and researchers to submit studies of the application of optimization, simulation, and digital twin techniques to improve organizations’ logistics and manufacturing processes.

Prof. Dr. Jose Antonio Marmolejo-Saucedo
Guest Editor

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• mathematical programming
• deep neural network
• stochastic optimization
• combinatorial optimization
• digital supply chain twins
• optimization of supply chain
• heuristics and metaheuristics
• robust optimization
• forecasting
• machine learning
• optimization applications
• high-performance computing
• green supply chain
• packing problems optimization
• fuzzy systems
• discrete event simulation
• dynamic systems