Novel approach for balancing manual automobile assembly based on genetic algorithm

As one of the key technologies in production process scheduling, line balancing has played a decisive role in improving productivity and increasing utilization efficiency. In this work, a novel approach for balancing manual automobile assembly lines is proposed within an improved genetic algorithm framework, where task sequence chromosomes are coded with satisfaction of precedence constraints and partitioned dynamically under the restricts of unidirectional stations on assembly line. Furthermore, the initial populations, chromosome selection schemes for next generation, crossover and mutation operators are reformed subsequently and hence the assembly balancing problem can be solved to the optimality or near optimality. The computational studies and comparisons have proven the validity and feasibility of the proposed approach in automobile assembly production lines.