Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

AI-Powered Multi-Objective Predictive Analytics for Smart Supply Chain Risk

Document Type : Research Paper

Author
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Abstract
This research presents an intelligence-driven and multi-objective framework for forecasting and risk management in smart supply chains. In this framework, data from IoT sensors, digital twin models, and historical records are collected in the data layer and processed in the predictive analytics layer by machine learning models to predict demand, delay, and risk. Then, the multi-objective NSGA-II algorithm is used to balance the three main objectives (cost reduction, risk reduction, and delivery time improvement). The results show that the proposed framework is able to provide a set of Pareto solutions with a reasonable balance between economic and operational objectives. Pareto front analysis and trade-off relationships indicate the stability of the model against parameter fluctuations and its ability to support intelligent decision-making. Overall, the proposed model is an efficient tool for optimizing decisions in dynamic and uncertain supply chain environments.
Keywords
Subjects

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Volume 17, Issue 2 - Serial Number 2
Spring 2025
Pages 263-275

  • Receive Date 04 January 2024
  • Revise Date 02 April 2024
  • Accept Date 05 May 2024