Robust Work Planning and Development of a Decision Support System for Work Distribution on a Mixed-Model Automotive Assembly Line

Abstract

Line balancing is a very resource-intensive and time consuming process which is highly reliant on the experience and expertise of a few employees. Line balancing is made even more complex due to the high level of option content in premium automobiles. The current phase of this study involves hands-on training on the automotive assembly line, precedence relationship mapping of all the tasks involved on a pilot assembly line, identification of constraints, and development of a strategy to manage option content and constraints. The second phase will include the generation of an optimal line balance through optimization on expected station utilization. The current line balancing process relies significantly on the experience level of the utility workers and team leaders. Although initially labor intensive, the precedence mapping exercise and option coding strategy will facilitate the development of a decision support system to aid the human decision-maker in making data-driven decisions about work distribution.