Assisted production system planning by means of complex robotic assembly line balancing

Abstract

Today, manufacturers and suppliers are challenged to deliver customized products at the lowest possible cost and in increasingly shorter time frames, due to the increasing number of variants. Achieving this demands efficient production system planning. However, current planning in the manufacturing industry is heavily reliant on manual processes and individual expertise. Prior research tackles this issue by aiming to develop a comprehensive approach for assisted, model-based rough planning of production systems. This article focuses the optimization of variant-specific production systems. The basis for this is a process precedence graph that restricts the optimization of the assignment of process steps to stations. In the mathematical modeling of the Assembly Line Balancing Problem (ALBP), this work addresses complex constraints, including the selection of station equipment, the utilization of multiple robots per station and a non-discrete assignment of tasks. The approach developed is applied to the example of a Tier 1 automotive supplier, where the multi-criteria solution of the ALBP allows an evaluation of the planning result. To this end, this work compares the algorithmically generated solution both qualitatively and quantitatively with an example of manual expert planning. Thereby it demonstrates the broad, industrial applicability of the approach. Consequently, this research contributes to enhancing efficiency in production system planning, leading to sustainable reductions in both costs and time.