A self-learning discrete salp swarm algorithm based on deep reinforcement learning for dynamic job shop scheduling problem

Gu, Yiming; Chen, Ming; Wang, Liang

doi:10.1007/s10489-023-04479-7

Cited by 7 publications

(2 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They proposed a hybrid DQN (HDQN) that includes double Q-learning, prioritized replay, and a soft target network update policy to minimize the maximum duration and total energy consumption. Gu et al (2023) [23] integrated DQN method into a scalp swarm algorithm (SWA) framework to dynamically tune the population parameters of SWA for JSP solving.…”

Section: Sarl For Schedulingmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Agent Reinforcement Learning for Extended Flexible Job Shop Scheduling

Peng,

Xiong,

Yang

et al. 2023

Machines

View full text Add to dashboard Cite

An extended flexible job scheduling problem is presented with characteristics of technology and path flexibility (dual flexibility), varied transportation time, and an uncertain environment. The scheduling can greatly increase efficiency and security in complex scenarios, e.g., distributed vehicle manufacturing, and multiple aircraft maintenance. However, optimizing the scheduling puts forward higher requirements on accuracy, real time, and generalization, while subject to the curse of dimension and usually incomplete information. Various coupling relations among operations, stations, and resources aggravate the problem. To deal with the above challenges, we propose a multi-agent reinforcement learning algorithm where the scheduling environment is modeled as a decentralized partially observable Markov decision process. Each job is regarded as an agent that decides the next triplet, i.e., operation, station, and employed resource. This paper is novel in addressing the flexible job shop scheduling problem with dual flexibility and varied transportation time in consideration and proposing a double Q-value mixing (DQMIX) optimization algorithm under a multi-agent reinforcement learning framework. The experiments of our case study show that the DQMIX algorithm outperforms existing multi-agent reinforcement learning algorithms in terms of solution accuracy, stability, and generalization. In addition, it achieves better solution quality for larger-scale cases than traditional intelligent optimization algorithms.

show abstract

Section: Sarl For Schedulingmentioning

confidence: 99%

“…Equation ( 22) makes the optimal network maximize the reward as far as possible, and Equation (23) ensures that the optimal network and the mixing network are optimal at the same time. The equations in (24) make two networks track each other when performing non-optimal action.…”

Section: Loss Functionmentioning

confidence: 99%