Autonomous decision-making method of transportation process for flexible job shop scheduling problem based on reinforcement learning

Yan, Jian Hua; Liu, Zhifeng; Zhang, Tao; Zhang, Yueze

doi:10.1109/mlise54096.2021.00049

Cited by 10 publications

(2 citation statements)

References 13 publications

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“…Aside from this application, RL was used to solve the routing problem in a bidirectional transport network for the purpose of avoiding deadlocks and obtaining collision-free trajectories [ 26 ]. The deep Q network (DQN) was used in [ 27 ] to learn a transportation strategy with breakpoint continuation and hierarchical feedback, which can calculate and further modify a transportation schedule in a short time to accommodate dynamic factors. That aside, the authors of [ 28 ] tried to teach a neural network to allocate transportation duties to AGVs and design routes for them in accordance with the rewards computed by the network.…”

Section: Literature Reviewmentioning

confidence: 99%

Toward Energy-Efficient Routing of Multiple AGVs with Multi-Agent Reinforcement Learning

Xian-feng

Deng

Shi

et al. 2023

Sensors

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This paper presents a multi-agent reinforcement learning (MARL) algorithm to address the scheduling and routing problems of multiple automated guided vehicles (AGVs), with the goal of minimizing overall energy consumption. The proposed algorithm is developed based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, with modifications made to the action and state space to fit the setting of AGV activities. While previous studies overlooked the energy efficiency of AGVs, this paper develops a well-designed reward function that helps to optimize the overall energy consumption required to fulfill all tasks. Moreover, we incorporate the e-greedy exploration strategy into the proposed algorithm to balance exploration and exploitation during training, which helps it converge faster and achieve better performance. The proposed MARL algorithm is equipped with carefully selected parameters that aid in avoiding obstacles, speeding up path planning, and achieving minimal energy consumption. To demonstrate the effectiveness of the proposed algorithm, three types of numerical experiments including the ϵ-greedy MADDPG, MADDPG, and Q-Learning methods were conducted. The results show that the proposed algorithm can effectively solve the multi-AGV task assignment and path planning problems, and the energy consumption results show that the planned routes can effectively improve energy efficiency.

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Section: Literature Reviewmentioning

confidence: 99%

Toward Energy-Efficient Routing of Multiple AGVs with Multi-Agent Reinforcement Learning

Xian-feng

Deng

Shi

et al. 2023

Sensors

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“…The agent learns via experience and aims to maximize the rewards it receives in certain scenarios. The primary goal of RL is to optimize the cumulative reward obtained by an agent through the evaluation and selection of actions within a dynamic environment [14][15][16][17][18][19][20]. The most current development in artificial intelligence technology has allowed successful application Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”

Section: Introductionmentioning

confidence: 99%

A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective

Modrak,

Ranjitharamasamy,

Balamurugan

et al. 2024

Preprint

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In this study, a comprehensive review on production scheduling based on reinforcement learning (RL) techniques using bibliometric analysis has been carried out. The aim of this work is, among other things, to point out the growing interest in this domain and to outline the influence of RL as a type of machine learning on production scheduling. To achieve this, the paper explores production scheduling using RL by investigating the descriptive metadata of all pertinent publications contained in the Web of Science database. The study focuses on a wide spectrum of publications spanning the years between 1998 and 2023. The findings and recommendations of this study can serve as new insights for future research endeavors in the realm of production scheduling using RL techniques.

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Enabling the Evaluation of Production Scheduling Algorithms in Complex Production Environments Using Individually Deployable Scheduling Services

Groth

Dippel

Schümann

2023

Lecture Notes in Computer Science

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Autonomous decision-making method of transportation process for flexible job shop scheduling problem based on reinforcement learning

Cited by 10 publications

References 13 publications

Toward Energy-Efficient Routing of Multiple AGVs with Multi-Agent Reinforcement Learning

Toward Energy-Efficient Routing of Multiple AGVs with Multi-Agent Reinforcement Learning

A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective

Enabling the Evaluation of Production Scheduling Algorithms in Complex Production Environments Using Individually Deployable Scheduling Services

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