Assembly Line Balancing Optimization Using Genetic Algorithm Method Compared to Heuristic Method

This study uses Genetic Algorithm and heuristic method to balance the task of a simple assembly. Nowadays, Industry 4.0 highlights features like the Internet of Things, Services, and Cyber-Physical Systems (CPS). In this context, assembly line balancing is crucial to enhance efficiency. A study was conducted using Genetic Algorithms and heuristics method such as Shortest Processing Time, Largest Candidate Rule, and Ranked Positional Weight. The results showed that the Genetic Algorithm method outperformed the heuristics in production simulation and smoothness. Despite its effectiveness, the Genetic Algorithm method showed minor drawbacks in line efficiency and idle time. Parameters like iteration numbers, mutation rates, and population size influenced the solutions generated. However, the Genetic Algorithm offered multiple optimal solutions and can be considered for improving line performance.