Optimization of a Remanufacturing Production Planning System with the Help of Artificial Intelligence

Abstract

AbstractAlthough production planning in remanufacturing systems has attracted great interest from the research community, only a couple of real industrial applications can be perceived. Additionally, in real cases, companies are faced with manufacturing multiple products, which further complicates remanufacturing production planning (RPP). Therefore, there is a need to optimise RPP where manufacturers are involved in remanufacturing multiple products. Also optimized systems should consist of a number of uncertainties, such as the uncertain quality of the returned products.Because of these uncertainties the manufacturers have to use new parts or components - with both higher environmental impacts, as well as costs. In the present paper a line balancing scheduler of a remanufacturing system is presented - focusing on the disassembly, machining and reassembly of parts. The objective of the paper is the reduction of usage of the energy and cost intensive new parts with production scheduling using a genetic algorithm (GA). The achievements are illustrated and presented with a real industrial use case from a gas engine producer. A discrete event simulation (DES) is used for evaluation purposes and the results from the scheduler are compared with benchmarks of the current production planning of the gas engine manufacturer.