سال انتشار:
2020
عنوان انگلیسی مقاله:
Deep reinforcement learning for a color-batching resequencing problem
ترجمه فارسی عنوان مقاله:
یادگیری تقویتی عمیق برای یک مسئله سنجش مجدد دسته ای رنگ
منبع:
Sciencedirect - Elsevier - Journal of Manufacturing Systems, 56 (2020) 175-187. doi:10.1016/j.jmsy.2020.06.001
نویسنده:
Jinling Lenga, Chun Jina,*, Alexander Voglb, Huiyu Liuc
چکیده انگلیسی:
In automotive paint shops, changes of colors between consecutive production orders cause costs for cleaning the
painting robots. It is a significant task to re-sequence orders and group orders with identical color as a color
batch to minimize the color changeover costs. In this paper, a Color-batching Resequencing Problem (CRP) with
mix bank buffer systems is considered. We propose a Color-Histogram (CH) model to describe the CRP as a
Markov decision process and a Deep Q-Network (DQN) algorithm to solve the CRP integrated with the virtual car
resequencing technique. The CH model significantly reduces the number of possible actions of the DQN agent, so
that the DQN algorithm can be applied to the CRP at a practical scale. A DQN agent is trained in a deep
reinforcement learning environment to minimize the costs of color changeovers for the CRP. Two experiments
with different assumptions on the order attribute distributions and cost metrics were conducted and evaluated.
Experimental results show that the proposed approach outperformed conventional algorithms under both conditions.
The proposed agent can run in real time on a regular personal computer with a GPU. Hence, the proposed
approach can be readily applied in the production control of automotive paint shops to resolve orderresequencing
problems.
Keywords: Deep reinforcement learning | Color-Batching problem | Virtual car resequencing | Production control | Automotive industry
قیمت: رایگان
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