Balancing Labor Intensive Assembly Line Using Genetic Algorithm

In this era of product customization, the optimal usage of resources especially the available facilities and operators who are adding the value to the product is important. When the main production system needs to produce a large variety of products, the cost must be kept at minimum. Therefore the assembly line has to be planned in a much more flexible way. The present work deals with the optimization of manpower in the labor intensive assembly lines. This problem occurs especially in the final assembly of consumer durable products where production is still very labor-intensive and where the wage rates depend on the requirements and qualifications to fulfill the work. In many real world assembly lines, the work-piece is of large size and there are several workers operating on the same work-piece in each station. This type of lines is called multi-manned assembly line (MAL). In the previous studies on MALs it is assumed that task times are deterministic and independent of other factors. But in many real world MALs task time are affected by other factors; one of these factors is the number of operators in the station. In other words intense concentration of workers in a station increases the task processing times because of operators blocking each other or waiting times for facilities to be released by other operators in the station. The line efficiency of the assembly line is well affected by the improper allocation of human resources. The maximum number of workers to be allocated to each station should also be important. Here a multi objective optimization was done using Genetic Algorithm by considering labor constraints, cost and the line efficiency. The experimental result showed meaningful improvement in the Line Efficiency as compared to the existing system.