Improvements to Disassembly Lot Sizing With Task Control Through Reinforcement Learning

Abstract

This research presents a novel methodology to control disassembly tasks for cost-efficient component recovery from end-of-life products, fostering remanufacturing. Inventory management is an integral part of systems that assemble or disassemble products. Unlike assembly systems, disassembly operations pose a unique challenge, as they can lead to inventory accumulation and risk uncontrolled growth without careful management. Disassembly system inventory management is complex due to various factors, including non-uniform demand for disassembled components, uncertainty in demands for salvage components, the arrival of different end-of-life product variants, end-of-life product condition variation, and processing time variation. These complexities often lead to unexpected inventory fluctuations, resulting in high inventory costs, inventory shortages, and customer dissatisfaction due to uncertainty in component availability. These inventory fluctuations can be mitigated if a real-time decision-making system supports disassembly processes. This study explores an innovative approach to addressing these complexities and controlling disassembly tasks using Deep Reinforcement Learning (DRL). This approach offers a more effective alternative to traditional methods. Experiments on Quantum-dot LED (QLED), Organic LED (OLED), and Quantum Dot OLED (QD-OLED) TV disassembly systems demonstrate the effectiveness of the DRL approach. Compared to the Multiple Elman Neural Networks (MENN) method, the DRL model offers a 21% reduction in inventory accumulation and a 12% improvement in demand satisfaction for the disassembly setup in the study.