Changeability Focused Planning Method for Multi Model Assembly Systems in Automotive Industry

Series vehicle production is designed to produce effectively at a defined number of vehicles per period. Regarding market forecasts the overall market trend depicts an increasing demand for electrified vehicles within an uncertain propulsion concept vehicle mix. This demand cannot be predicted precisely because of volatile influencing factors such as governmental subsidies. Automotive companies are therefore confronted with the challenge of rapidly adapting their production systems accordingly. An approach to handle the variety of models within vehicle final assembly is to establish mixed model assembly lines. Since single model assembly lines are optimized for a specific production volume of one model, the subsequent integration of vehicles using alternative propulsion concepts into single model assembly lines stands as a great challenge in final assembly. Moreover, producing with optimal configured assembly systems after integrating an additional model is not ensured further on. To address this challenge, an approach for the greenfield planning of assembly lines using the concept of changeability is presented within this paper. The presented approach offers a new method to cover uncertainty regarding the future propulsion concept mix of assembly lines. This affects the initial setup of an assembly line concerning the line balancing and assembly equipment as possible subsequent changes to the assembly system increase costs. The target conflict is to minimize changes to the assembly system due to the integration of further propulsion concepts while ensuring cost efficient assembly. Hereto, the line balancing problem is solved for a fixed production volume ratio using a developed optimization algorithm. Thereafter, the production volume ratios are varied in order to identify an optimal solution for line balancing and assembly equipment. The uncertainty of volume ratios is considered in the integrated costs calculation module.