Modelling and Optimization in Supply Chain Management
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 21
Special Issue Editor
Special Issue Information
Dear Colleagues,
We are pleased to announce a Special Issue on “Modelling and Optimization in Supply Chain Management”, to be published in Mathematics. This Special Issue aims to explore contemporary advances in modeling and optimization techniques within supply chain management (SCM). Recognizing the increasing complexity and interconnectedness of modern supply chains, we seek contributions that address both strategic and tactical planning aspects through integrated modeling approaches.
We welcome original research and comprehensive reviews that delve into areas such as multi-echelon inventory systems, demand forecasting, production planning, distribution logistics, and risk management. Emphasis is placed on methodologies that enhance decision support systems, making them more reflective of real-world scenarios.
Submissions employing advanced mathematical models, simulation techniques, and optimization algorithms to solve practical SCM problems are particularly encouraged.
Additionally, studies that incorporate emerging technologies, such as artificial intelligence and machine learning, to improve supply chain resilience and efficiency will be highly regarded.
Through this Special Issue, we aim to bridge the gap between theoretical modeling and practical application, fostering a deeper understanding of how integrated approaches can lead to more robust and adaptable supply chains.
Prof. Dr. Rafael Guillermo García-Cáceres
Guest Editor
Manuscript Submission Information
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Keywords
- supply chain management
- integrated modeling
- optimization techniques
- strategic planning
- tactical planning
- decision support systems
- inventory management
- production planning
- distribution logistics
- risk management
- simulation
- artificial intelligence
- machine learning
- resilience
- efficiency
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