Trainable Monte Carlo-MLP for cost uncertainty in resilient supply chain optimization with additive manufacturing implementation challenges

Abstract

The integration of Additive Manufacturing (AM) has the potential to transform Supply Chain (SC) dynamics, but its implementation introduces risks that require careful management. This paper presents an innovative optimization framework for evaluating AM integration within SCs through two policies aimed at creating resilience. By exploring the intersection of AM and Traditional Manufacturing (TM), the study focuses on restructuring SCs for full or partial product production using AM techniques. To enhance SC resilience against material shortages, smart contracts with buffer suppliers are employed. The main objective is to reduce operational and conventional costs while optimizing SC performance. To address cost uncertainty, this research introduces a novel Monte Carlo (MC) and Machine Learning (ML) hybrid approach, termed MCML. This method leverages MCML-Particle Swarm Optimization (MCML-PSO) and MCML-Genetic Algorithm (MCML-GA) for optimization. A real-world case study validates the model, showing that it reduces costs and improves the accuracy of cost uncertainty estimation compared to standalone TM and AM approaches. Various methods, including PSO, GA, MC-PSO, and MC-GA, were evaluated, with MCML-PSO demonstrating the best performance in minimizing total costs. This study highlights the benefits of integrating AM into SCs, emphasizing the importance of precise cost uncertainty estimation. The proposed model offers valuable insights for decision-makers, helping them design resilient and efficient SCs while mitigating the risks associated with AM technology.