Logistics Flow Optimization for Advanced Management of the Crisis Situation

Area of Science:

• Operations Research
• Supply Chain Management
• Artificial Intelligence

Background:

• Supply chains are critical for competitive advantage, involving optimization of information, financial, and goods flows.
• Crisis management in logistics is increasingly important, particularly highlighted by the COVID-19 pandemic.
• Supply chain environments are volatile, with rapid and unpredictable changes.

Purpose of the Study:

• To address decision-making challenges in supply chain information systems during crisis situations.
• To develop a framework integrating optimization methods and artificial intelligence for crisis logistics.
• To minimize stock shortage risks across the entire supply chain.

Main Methods:

• Development of mathematical models for supply chain optimization.
• Implementation of inter-agent cooperation protocols.
• Application of artificial intelligence paradigms to crisis management.

Main Results:

• Proposed models and protocols aim to reduce stockout risks.
• Enhanced decision-making capabilities for supply chains in volatile environments.
• Improved resilience and efficiency in crisis logistics.

Conclusions:

• The integration of optimization and AI provides a robust solution for supply chain challenges in crises.
• Minimizing stock shortages is achievable through advanced modeling and cooperation.
• This approach enhances supply chain performance and stability during unpredictable events.