A frequent challenge encountered by manufacturers of mechanical assemblies consists of the definition of quality criteria for the assembly lines of the subcomponents which are mounted into the final product. The rollout of Industry 4.0 standards paves the way for the usage of data-driven, intelligent approaches towards this goal. In this work, we investigate such a scenario originating in the daily operations of thyssenkrupp Presta AG, where new vibroacoustic quality specifications must be derived for the assembly line producing ball nut assemblies, based on the feedback offered by the vibroacoustic quality test of the steering gear, the final mechanical assembly they are mounted into. We first present a Mixed Integer Linear Programming (MILP) formulation for the problem and show that small instances of the corresponding available dataset can be solved to optimality. Upon ascertainment of the unsuitability of the MILP approach for industrial daily operations due to its long computation time, we propose a heuristic solving approach based on genetic algorithms and measure the performance gap between them in terms of achieved solution quality and computation time. Finally, we additionally propose a greedy heuristic designed to outperform the genetic algorithm in terms of computation time while still featuring a comparable solution quality. The practical relevance of the results is guaranteed, since the best solution reached by the genetic algorithm reduces the scrap costs with respect to the method currently employed by the company by 49.91%.
TR-AAUK-M-O-02-12-20, Alpen-Adria-Universität Klagenfurt, Mathematics, Optimization Group, February 2020