调度(生产过程)
数学优化
计算机科学
物料搬运
自动引导车
作业车间调度
生产线
工程类
工业工程
数学
人工智能
嵌入式系统
布线(电子设计自动化)
机械工程
作者
Binghai Zhou,Zhaoxu He
标识
DOI:10.1080/00207543.2021.2017056
摘要
Since global warming and the needs for sustainable production models, this paper focuses on a Just-in-Time (JIT)-based sustainable material handling scheduling problem (JSMHSP) with time window and capacity constraints for mixed-model assembly lines in the automobile industry. A novel Hybrid-load Automated Guided Vehicle (H-AGV) is proposed to fulfil material handling tasks between supermarkets and assembly lines. The motivation is to minimise the total line-side inventory and the total energy consumption, which corresponds to JIT and environmental objectives. Due to the NP-hard nature of the proposed scheduling problem, a Deep Q network and Non-dominated sorting-based Hyper-Heuristic (DN-HH) algorithm is presented to solve the bi-objective scheduling problem, which benefits from the synergy of the Deep Q Network (DQN) and Hyper-Heuristic (HH). In the DQN, the states and rewards are designed according to the characteristics of the scheduling problem. To improve the performance of DQN, the experience pool (EP) and the target network are presented to improve the convergence speed. Computational results reveal that the proposed DN-HH algorithm outperforms the other two compared algorithms in both solution quality and convergence speed and the performance of the H-AGV is better than that of the other two types of AGVs.
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