An Intelligent Dynamic Clustering Framework with Neural Network-Based Product-to-Facility Assignment for Supply Chain Network Design Under Uncertainty

Authors

  • Amirhossein Amou Jafari Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
  • Kiana Salehi * Department of Industrial Engineering, University of Tehran, Tehran, Iran.
  • Mina Sadatakhavi Department of Industrial Engineering, University of Tehran, Tehran, Iran.

https://doi.org/10.22105/raise.v3i1.82

Abstract

Growing Supply Chain (SC) complexity, demand volatility, and environmental pressures have increasingly limited the effectiveness of traditional static and non-intelligent network design approaches. These limitations are particularly evident in multi-period settings characterized by uncertainty, where adaptive, data-driven decision mechanisms are required. This study develops an integrated framework for designing a multi-period stochastic bi-level SC network to improve operational efficiency, enhance resource-allocation flexibility, and minimize total network costs. First, an enhanced Constrained K-Means algorithm is developed to dynamically reconfigure service zones over time while accounting for capacity restrictions and cluster-balance requirements. Subsequently, an Artificial Neural Network (ANN) classifies products according to their physical and functional characteristics and determines whether they should be routed through the central warehouse or cross-docking centers. The outputs of these two data-driven modules are incorporated as structured inputs into a bi-level stochastic optimization model that jointly addresses location, allocation, and routing decisions under uncertainty. In addition, a dynamic cluster-improvement algorithm iteratively adjusts cluster configurations based on shortage rates, thereby strengthening network responsiveness and resilience. The proposed framework is evaluated through a real-world case study. The numerical results indicate cost reductions of up to approximately 32% in specific periods, together with more stable resource utilization and improved overall SC performance.

Keywords:

Supply chain network design, Dynamic clustering, Neural networks, Uncertainty, Machine learning, Constrained K-means

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Published

2026-03-09

Issue

Vol. 3 No. 1 (2026): Research Annals of Industrial and Systems Engineering

Section

Articles

How to Cite

Amou Jafari, A. ., Salehi, K. ., & Sadatakhavi, M. . (2026). An Intelligent Dynamic Clustering Framework with Neural Network-Based Product-to-Facility Assignment for Supply Chain Network Design Under Uncertainty. Research Annals of Industrial and Systems Engineering, 3(1), 27-38. https://doi.org/10.22105/raise.v3i1.82

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