Call for Papers
Special Issue on Integrating Artificial Intelligence in Supply Chain with Operations Research Models
Guest Editors:
Ciwei Dong, Professor, School of Business Administration, Zhongnan University of Economics and Law, China;
dongciwei@zuel.edu.cn
Bin Shen, Professor, Glorious Sun School of Business and Management, Donghua University, China;
binshen@dhu.edu.cn
Xin Wen (Windy), Assistant Professor, Department of Industrial & Systems Engineering, The Hong Kong Polytechnic University, Hong Kong;
windy.wen@polyu.edu.hk
Important Dates:
- Paper submission deadline: November 1, 2025
- Completion of the first round review: February 1, 2026
- Completion of the second round review: May 31, 2026
- Final submission due: July 31, 2026
- Tentative publication date: September 30, 2026
With the rapid development of Artificial Intelligence (AI) technology, its integration in supply chain management with Operations Research (OR) models is becoming a core driver for businesses to enhance efficiency, optimize resource allocation, reduce costs, and navigate increasingly complex market environments. The introduction of AI breaks the limitations of traditional supply chain management approaches, fostering intelligent, automated, and data-driven transformations. Through advanced machine learning algorithms, data analytics, and optimization techniques, AI may enhance OR models to provide novel solutions for supply chain management, enabling businesses to maintain efficient operations and competitiveness in a dynamic marketplace.
AI has a profound impact on various aspects of supply chain with OR applications. In demand forecasting, AI analyzes vast amounts of historical data, market trends, and external environmental factors to achieve more accurate demand predictions, helping businesses optimize production plans and inventory management. Compared to traditional methods, AI can identify potential demand fluctuations, ensuring precise resource allocation and reducing inventory buildup and stockouts. In production scheduling, AI monitors the production process in real time, dynamically adjusting production plans based on equipment operational status, order requirements, and external factors. This optimizes resource utilization, improves production efficiency, and reduces equipment failure rates. Especially in the field of intelligent manufacturing, AI applications drive highly optimized production through automation and data-driven processes. In logistics management, AI enhances the precision of transportation routes, route planning, and distribution decisions. AI can combine real-time traffic data, weather changes, transportation costs, and other factors to intelligently optimize logistics routes, reducing transportation costs and improving on-time delivery rates. Meanwhile, AI can automatically adjust routes and transportation plans based on real-time data, ensuring the smooth operation and stability of the global logistics network. In risk management, AI analyzes large-scale global data related to political, economic, and environmental factors, accurately identifying potential supply chain risks and helping businesses proactively develop response strategies, thereby improving the resilience and stability of supply chains.
This special issue will explore the integration of AI and supply chain management with OR models, covering cutting-edge research on data-driven optimization, demand forecasting, production scheduling, logistics efficiency, decision support systems, and risk assessment. Particularly in the face of global crises and volatile markets, we will examine how AI, combined with OR methodologies, can offer innovative and efficient solutions for supply chain management. We invite scholars and industry experts worldwide to submit original research on integrating AI into supply chain management using OR models. Topics for this special issue include but are not limited to:
- The integration of AI with OR models for supply chain demand forecasting
- AI-based intelligent production scheduling and optimization
- AI applications in logistics management: route optimization and cost reduction
- The role of AI in smart manufacturing and Industry 4.0
- AI-enabled supply chain collaboration and information-sharing frameworks
- AI and OR applications in supply chain risk management: Prediction and resilience strategies
- AI-driven supply chain decision support systems: Optimizing from strategy to tactics
- The role of AI and OR in sustainable supply chain management
- Deep learning-enhanced OR optimization models for supply chains
- AI applications in emergency supply chain management: responding to global crises