Reinforcement learning for disassembly sequence planning optimization

Abstract

The disassembly process is one of the most expensive phases in the product life cycle for both maintenance and the End of Life dismantling process. Industry must optimize the disassembly sequence to ensure time-cost-efficiency. This paper presents a new approach based on the Reinforcement Learning algorithm to optimize Disassembly Sequence Planning. This research work focuses on two types of dismantling: partial and full disassembly. By introducing a fitness function within the Reinforcement Learning algorithm, it is aimed at implementing optimized Disassembly Sequence Planning for five disassembly parameters or goals: (1) minimizing disassembly tool changes, (2) minimizing disassembly direction changes, (3) optimizing dismantling time including preparation and processing time, (4) prioritizing the dismantling of the smallest parts, and (5) facilitating access to wear parts. The proposed approach is applied to a demonstrative example. Finally, a comparison with other approaches from the literature is provided to demonstrate the efficiency of the new approach.