International Journal of Supply and Operations Management

International Journal of Supply and Operations Management

Reinforcement Learning for Blockchain-Enabled Supply Chain Network Design

Articles in Press

Document Type : Research Paper

Authors
Sara Hasheminezhad 1
Mahsa Moayed 1
Ardavan Babaei 1
Erfan Babaee Tirkolaee 2
1 Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
2 Department of Industrial Engineering, Istinye University, Istanbul, Turkey
10.22034/ijsom.2026.111012.3553
Abstract
Objective: The objective of this study is to examine the optimal design of blockchain-enabled supply chain networks using reinforcement learning (RL). The goal is to develop an integrated network framework considering simultaneously supply chain connectivity, blockchain node activation and cross-layer integration decisions, to minimize the total network cost under operational constraints.
Methods: The problem is formulated as a combinatorial optimization problem and modeled as a two-layer network of supply chain entities and blockchain nodes. A Q-learning framework is used to explore feasible network configurations in the presence of stochastic costs. The model includes various cost factors, connectivity requirements, constraints for blockchain activation, and penalty mechanisms to ensure feasible solutions.
Results: The results of experiments show that the proposed RL-based method is able to find out the cost-effective network designs in accordance with network connectivity constraints. The convergence behavior is shown to be stable throughout different runs, and the method converges within an earlier stage of training. The sensitivity analysis indicates that the increment of minimum number of active blockchain nodes will increase the cost of the designed network, highlighting the trade-off between blockchain deployment requirements and economic efficiency. The resulting network structure turns out to be feasible, connected, and interpretable.
Conclusion: Based on the findings, the research proves the feasibility of applying reinforcement learning to blockchain-enabled supply chain network design problems. The proposed framework provides an approach that can be utilized evaluating blockchain integration strategies and balancing implementation costs with operational requirements. The research further creates a base for further research involving larger-scale networks, dynamic environments, and multi-objective optimization settings.
Keywords
blockchain technology
supply chain management
reinforcement learning
Q-learning
optimization
Subjects
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International Journal of Supply and Operations Management

Articles in Press, Accepted Manuscript
Available Online from 03 July 2026