Optimizing Assembly Line Balancing with Carbon Footprint and Spatial Constraints: A Customized Q-Learning Algorithm
DOI:
https://doi.org/10.23055/ijietap.2025.32.5.11113Abstract
Against the backdrop of the accelerating transformation of global manufacturing towards intelligence and sustainability, assembly lines have become key to improving production efficiency and flexibility. However, they are often constrained by increasingly stringent carbon footprint regulations. This study focuses on the balancing problem of assembly lines with spatial constraints, aiming to minimize both cycle time and carbon footprint. Given the NP-hard nature of the problem, a mixed-integer programming model was developed, and a Q-Learning algorithm with a restart mechanism was used to solve medium to large-scale problems for efficient resource allocation and carbon reduction. The experimental results demonstrate that the Q-learning algorithm with a restart mechanism exhibits significant performance advantages compared to other heuristics, such as random search and standard Q-learning. Specifically, it achieves a 100% superiority over the compared algorithms in two key metrics: the ratio of non-dominated solutions (Rp) and the convergence degree of non-dominated solutions (Cp). Additionally, it shows a 70% advantage in the spread metric (Sp).
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