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Institute: search.filters.UBSInstitut.Institut für Industrielle Fertigung und FabrikbetriebAuthor: search.filters.author.Albus, Marcel

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    Towards scalability for resource reconfiguration in robotic assembly line balancing problems using a modified genetic algorithm
    (2024) Albus, Marcel; Hornek, Timothée; Kraus, Werner; Huber, Marco F.
    Assembly lines are still one of the most used manufacturing systems in modern-day production. Most research affects the building of new lines and, less frequently, the reconfiguration of existing lines. However, the first is insufficient to meet the reconfigurable production paradigm required by volatile market demands. Consequent reconfiguration of resources by production requests affects companies’ competitiveness. This paper introduces a problem-specific genetic algorithm for optimizing the reconfiguration of a Robotic Assembly Line Balancing Problem with Task Types, including additional company constraints. First, we present the greenfield and brownfield optimization objectives, then a mathematical problem formulation and the composition of the genetic algorithm. We evaluate our model against an Integer Programming baseline on a reconfiguration dataset with multiple equipment alternatives. The results demonstrate the capabilities of the genetic algorithm for the greenfield case and showcase the possibilities in the brownfield case. With a scalability improvement through computation time decrease of up to ∼2.75 ×, reduced number of equipment and workstations, but worse objective values, the genetic algorithm holds the potential for reconfiguring assembly lines. However, the genetic algorithm has to be further optimized for the reconfiguration to leverage its full potential.
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    Optimization approach for robotic assembly line balancing in constrained greenfield and reconfiguration scenarios
    (Stuttgart : Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA, 2025) Albus, Marcel; Huber, Marco (Univ.-Prof. Dr.-Ing. habil.)
    This research explores the potential of automated optimization techniques in reducing costs for the Robotic Assembly Line Balancing Problem (RALBP) in greenfield and reconfiguration scenarios. The introduction analyses the shifts towards flexibility and mass customization and identifies the manufacturing requirements on the Assembly Line Balancing (ALB). Various problem formulations are examined and reviewed on real-world requirements, leading to the definition of the RALBP with Task Types (RALBP-TT). The manufacturing requirements of mass personalization highlight the necessity for reconfiguration solutions, and an investigation of existing models reveals the need for improvements. Solution techniques are compared based on their ALB suitability and manufacturing requirements, highlighting the exact methods as best suited. As a result, an exact Integer Programming (IP) model for greenfield and reconfiguration scenarios and a Genetic Algorithm (GA) model for validation are presented. The models include additional constraints, a multi-cost approach, and a resource database. The performance of the IP and GA models are validated and compared using a new benchmarking dataset, and the discussion shows that the IP model can leverage existing equipment for cost reduction but with compromises. In comparison, the GA drastically reduces computation time but yields higher total costs due to its sequential nature and compromises in equipment and workstation usage. Future research directions could investigate flexible cycle time constraints for the IP model or improve the sequential GA steps. Furthermore, a multiobjective approach to addressing sustainability goals, or the objective of equipment reduction, is worth investigating. Real-world validation and refinement of optimization models based on customers’ feedback are suggested for further investigation.