Author:
Kegyes Tamás,Süle Zoltán,Abonyi János
Abstract
AbstractAs the environmental aspects become increasingly important, the disassembly problems have become the researcher’s focus. Multiple criteria do not enable finding a general optimization method for the topic, but some heuristics and classical formulations provide effective solutions. By highlighting that disassembly problems are not the straight inverses of assembly problems and the conditions are not standard, disassembly optimization solutions require human control and supervision. Considering that Reinforcement learning (RL) methods can successfully solve complex optimization problems, we developed an RL-based solution for a fully formalized disassembly problem. There were known successful implementations of RL-based optimizers. But we integrated a novel heuristic to target a dynamically pre-filtered action space for the RL agent (dlOptRL algorithm) and hence significantly raise the efficiency of the learning path. Our algorithm belongs to the Heuristically Accelerated Reinforcement Learning (HARL) method class. We demonstrated its applicability in two use cases, but our approach can also be easily adapted for other problem types. Our article gives a detailed overview of disassembly problems and their formulation, the general RL framework and especially Q-learning techniques, and a perfect example of extending RL learning with a built-in heuristic.
Funder
Innovációs és Technológiai Minisztérium
University of Pannonia
Publisher
Springer Science and Business Media LLC
Reference50 articles.
1. Beamon BM (1999) Measuring supply chain performance. Int J Oper Prod Manag 19(3):275–292
2. Bianchi RA, Ribeiro CH, Costa AHR (2012) Heuristically accelerated reinforcement learning: theoretical and experimental results. In: ECAI, pp 169–174
3. Bianchi RAC, Celiberto JLA, Santos PE, Matsuura JP, Lopez De Mantaras R (2015) Transferring knowledge as heuristics in reinforcement learning: a case-based approach. Artif Intell 226:102–121
4. Camacho-Otero J, Boks C, Pettersen IN (2018) Consumption in the circular economy: a literature review. Sustainability (Switzerland) 10(8):2758
5. Chand M, Ravi C (2023) A state-of-the-art literature survey on artificial intelligence techniques for disassembly sequence planning. CIRP J Manuf Sci Technol 41:292–310
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献