STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Production Research & Management Review Article

Integrating Genetic Algorithms with Lean Manufacturing for Enhanced Production Efficiency

  1. Anushka Mukharjee Department of Engineering, United College of Engineering and Research, Prayagraj

Abstract

Lean manufacturing is a well-established philosophy focusing on the systematic reduction of waste and the ongoing development of value supplied to the customer. It emphasizes efficiency, quality, and adaptability through ideas such as just-in-time production, continuous improvement (Kaizen), and value stream optimization. However, the increased complexity of modern production systems, driven by global rivalry, product variety, and rapid technology innovation, has shown the limitations of classic lean tools in achieving maximum operational efficiency. In this context, computational intelligence techniques, particularly Genetic Algorithms (GAs), have emerged as powerful tools for addressing optimization problems that are nonlinear, multi-objective, and constrained by dynamic production settings. This paper investigates the integration of Genetic Algorithms with lean manufacturing principles as a strategic approach to enhance production efficiency. Genetic Algorithms, inspired by the evolutionary process of natural selection, utilize populations of potential solutions and iterative improvement mechanisms to explore large solution spaces effectively. When embedded within lean frameworks, GAs can be employed to optimize production scheduling, resource allocation, process sequencing, and facility layout while preserving lean objectives such as waste minimization, flow efficiency, and cost reduction. The study systematically reviews relevant literature to establish the theoretical and practical synergy between GAs and lean manufacturing. It highlights case applications in areas such as job-shop scheduling, demand-driven production, and inventory optimization under just-in-time constraints. Furthermore, it discusses how GA-driven decision-support systems can enhance lean tools like value stream mapping and continuous improvement initiatives by introducing adaptive, data-driven feedback loops. Despite the promising potential, challenges such as algorithmic complexity, computational cost, and the need for real-time data integration persist. The paper also identifies limitations in empirical validations and the need for hybrid models that combine GAs with machine learning and simulation-based optimization to address uncertainty and variability.

Keywords

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