Call for paper for Special Issue

 | Last update: 2025/01/20 | 
International Journal of Industrial Engineering & Production Research (IJIEPR)

Call for Papers – Special Issue
 
Artificial Intelligence-based Approaches in Supply Chain Management:
Methodologies, Applications, and Case Studies

Overview
The International Journal of Industrial Engineering & Production Research (IJIEPR) announces a Special Issue dedicated to advancing research on Artificial Intelligence-based Approaches in Supply Chain Management. This issue seeks high-quality scholarly contributions that demonstrate how AI can enhance the efficiency, resilience, adaptability, and intelligence of modern supply chains.
As highlighted in the official call, “Supply chain systems are becoming increasingly complex due to globalization, market volatility, demand uncertainty, sustainability concerns, geopolitical disruptions, and accelerated digital transformation.” AI technologies, including machine learning, deep learning, reinforcement learning, generative AI, natural language processing, intelligent agents, and predictive analytics, are creating new opportunities to address these challenges.
This Special Issue welcomes methodological innovations, hybrid intelligent frameworks, algorithmic developments, decision-support systems, and real-world case studies. Submissions should emphasize scientific rigor, practical relevance, interpretability, robustness, scalability, and managerial implications.

Topics of Interest
Submissions may address, but are not limited to:
  • AI-driven demand forecasting and sales prediction
  • Machine learning for inventory management and replenishment
  • AI-based procurement, sourcing, and supplier selection
  • Intelligent production planning and scheduling
  • AI applications in transportation, logistics, and distribution
  • Last-mile delivery optimization using AI
  • Reinforcement learning for dynamic decision-making
  • Generative AI for planning, scenario analysis, and decision support
  • Explainable and trustworthy AI in supply chain management
  • AI for risk management, disruption prediction, and resilience
  • Sustainable, green, and circular supply chains enabled by AI
  • AI-supported warehouse and fulfillment systems
  • AI applications in healthcare, food, retail, manufacturing, and humanitarian supply chains
  • Predictive maintenance and asset monitoring
  • Digital twins and real-time supply chain control
  • Hybrid AI–optimization approaches
  • Multi-agent systems and autonomous coordination
  • Human–AI collaboration in decision processes
  • Data-driven supply chain network design and reconfiguration
  • Industrial case studies demonstrating practical AI implementations
Strong, original, and insightful studies with clear practical implications for decision-makers are especially encouraged.

Expected Contributions
Submissions should contain:
  • Highlight methodological rigor while emphasizing real-world applicability.
  • Provide implications for decision-makers, policymakers, or stakeholders.
  • Describe validation, simulation, or practical application in a real-world situation.

Submission & Review Process
All manuscripts will undergo IJIEPR’s standard peer-review procedures to ensure academic quality and relevance.

Guest Editors
Ebrahim Teimoury, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Email: teimouryiust.ac.ir

Seyyed-Mahdi Hosseini-Motlagh, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Email: motlaghiust.ac.ir

Mohammad Reza Ghatreh Samani, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.
Email: samanimodares.ac.ir

Important Dates
  • Submission Open Date: October 01, 2026
  • Submission Due Date: December 20, 2026
  • First Review Completion: January 30, 2027
  • Revised Manuscripts Due: February 20, 2027
Papers will be subject to a strict review process under the supervision of the Guest Editors. Accepted papers will be published online individually, before print publication.

                                                                                                        




















 

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