A Fuzzy Multi-Objective Sustainable and Agile Supply Chain Model Based on Digital Twin and Internet of Things with Adaptive Learning Under Environmental Uncertainty

Hamed Nozari, Agnieszka Szmelter-Jarosz, Dariusz Weiland

This paper presents an advanced, adaptive model for designing and optimizing agile and sustainable supply chains by integrating fuzzy multi-objective programming, Internet of Things (IoT), digital twin (DT) technologies, and reinforcement learning. Unlike conventional static models, the proposed framework utilizes real-time data and dynamically updates fuzzy parameters through a deep deterministic policy gradient (DDPG) algorithm. The model simultaneously addresses three conflicting objectives: minimizing cost, delivery time, and carbon emissions, while maximizing agility. To validate the model’s effectiveness, various optimization strategies including NSGA-II, MOPSO, and the Whale Optimization Algorithm are applied across small- to large-scale scenarios. Results demonstrate that the integration of IoT and DT, alongside adaptive learning, significantly improves decision accuracy, responsiveness, and sustainability. The model is particularly suited for high-volatility environments, offering decision-makers an intelligent, real-time support tool. Case study simulations further illustrate the model’s value in sectors such as urban logistics and humanitarian aid supply chains.