Yaoyao Ping, Yongkui Liu, Lin Zhang, Lihui Wang, Xun Xu
{"title":"不同任务到达模式下基于深度强化学习的云制造多任务调度","authors":"Yaoyao Ping, Yongkui Liu, Lin Zhang, Lihui Wang, Xun Xu","doi":"10.1115/1.4062217","DOIUrl":null,"url":null,"abstract":"\n Cloud manufacturing is a manufacturing model that aims to provide on-demand resources and services to consumers over the Internet. Scheduling is one of the core techniques for cloud manufacturing to achieve the aim. Multi-task scheduling with dynamical task arrivals is an important research issue in the area of cloud manufacturing scheduling. Many traditional algorithms such as the genetic algorithm (GA) and ant colony optimization algorithm (ACO) have been used to solve the issue, which, however, are either incapable of or perform poorly in tackling the problem. Deep reinforcement learning (DRL) that combines artificial neural networks with reinforcement learning provides an effective technique in this regard. In view of this, we employ a typical deep reinforcement learning algorithm – Deep Q-network (DQN) – and proposed a DQN-based multi-task scheduling approach for cloud manufacturing. Three different task arrival modes – arriving at the same time, arriving in random batches, and arriving one by one sequentially – are considered. Four baseline approaches including random scheduling, round-robin scheduling, earliest scheduling, and minimum execution time scheduling are investigated. A comparison of results indicates that the DQN-based scheduling approach is able to effectively address the multi-task scheduling problem in cloud manufacturing and performs best among all approaches.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep Reinforcement Learning-Based Multi-Task Scheduling in Cloud Manufacturing under Different Task Arrival Modes\",\"authors\":\"Yaoyao Ping, Yongkui Liu, Lin Zhang, Lihui Wang, Xun Xu\",\"doi\":\"10.1115/1.4062217\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Cloud manufacturing is a manufacturing model that aims to provide on-demand resources and services to consumers over the Internet. Scheduling is one of the core techniques for cloud manufacturing to achieve the aim. Multi-task scheduling with dynamical task arrivals is an important research issue in the area of cloud manufacturing scheduling. Many traditional algorithms such as the genetic algorithm (GA) and ant colony optimization algorithm (ACO) have been used to solve the issue, which, however, are either incapable of or perform poorly in tackling the problem. Deep reinforcement learning (DRL) that combines artificial neural networks with reinforcement learning provides an effective technique in this regard. In view of this, we employ a typical deep reinforcement learning algorithm – Deep Q-network (DQN) – and proposed a DQN-based multi-task scheduling approach for cloud manufacturing. Three different task arrival modes – arriving at the same time, arriving in random batches, and arriving one by one sequentially – are considered. Four baseline approaches including random scheduling, round-robin scheduling, earliest scheduling, and minimum execution time scheduling are investigated. A comparison of results indicates that the DQN-based scheduling approach is able to effectively address the multi-task scheduling problem in cloud manufacturing and performs best among all approaches.\",\"PeriodicalId\":16299,\"journal\":{\"name\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4062217\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering-transactions of The Asme","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4062217","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
Deep Reinforcement Learning-Based Multi-Task Scheduling in Cloud Manufacturing under Different Task Arrival Modes
Cloud manufacturing is a manufacturing model that aims to provide on-demand resources and services to consumers over the Internet. Scheduling is one of the core techniques for cloud manufacturing to achieve the aim. Multi-task scheduling with dynamical task arrivals is an important research issue in the area of cloud manufacturing scheduling. Many traditional algorithms such as the genetic algorithm (GA) and ant colony optimization algorithm (ACO) have been used to solve the issue, which, however, are either incapable of or perform poorly in tackling the problem. Deep reinforcement learning (DRL) that combines artificial neural networks with reinforcement learning provides an effective technique in this regard. In view of this, we employ a typical deep reinforcement learning algorithm – Deep Q-network (DQN) – and proposed a DQN-based multi-task scheduling approach for cloud manufacturing. Three different task arrival modes – arriving at the same time, arriving in random batches, and arriving one by one sequentially – are considered. Four baseline approaches including random scheduling, round-robin scheduling, earliest scheduling, and minimum execution time scheduling are investigated. A comparison of results indicates that the DQN-based scheduling approach is able to effectively address the multi-task scheduling problem in cloud manufacturing and performs best among all approaches.
期刊介绍:
Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining