A novel hybrid-load AGV for JIT-based sustainable material handling scheduling with time window in mixed-model assembly line

ABSTRACT

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.

KEYWORDS:

• Mixed-model assembly lines
• material handling
• just-in-time
• energy consumption
• Deep Q network
• hyper-heuristic

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [Grant Number 71471135].

Notes on contributors

Binghai Zhou

Binghai Zhou was born in 1965, in Zhejiang Province, China. He received his Master degree in Industrial Engineering from Shanghai Jiaotong University in 1989 and Doctor degree in 1992, respectively. He is currently a professor in the Mechanical Engineering school of Tongji University, Shanghai, China. His research interests cover scheduling and simulation of discrete systems (manufacturing/logistics Systems), preventive maintenance modelling of equipment and artificial intelligent algorithm.

Zhaoxu He

Zhaoxu He was born in 1997, in Hebei Province, China. He received his Bachelor degree in Industrial Engineering from Tongji University, Shanghai, China. Currently, He is a senior student. His current research interests include evolutionary algorithm, manufacturing system modelling and simulation.