Download or read book Dynamic Scheduling Mechanism for Intelligent Workshop with Deep Reinforcement Learning Method Based on Multi Agent System Architecture written by Wenbin Gu and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the development and changes of industry and market demand, the personalized customization production mode with small batch and multiple batches has gradually become a new production mode. This makes production environment become more complex and dynamic. However, traditional production workshops cannot effectively adapt to this environment. Combing with new technologies, transforming traditional workshops into intelligent workshop to cope with new production mode become an urgent problem. Therefore, this paper proposes a multi-agent manufacturing system based on IoT for intelligent workshop. Meanwhile, this paper takes flexible job shop scheduling problem (FJSP) as a specific production scenario and establishes relevant mathematics model. To build the agent in intelligent workshop, this paper proposes a data-based with combination of virtual and physical agent (DB-VPA) which has information layer, software layer and physical layer. Then, based on the manufacturing system, this paper designs a dynamic scheduling mechanism for intelligent workshop. This method contains three aspects: (1) Modeling production process based on Markov decision process (MDP). (2) Designing communication mechanism for DB-VPAs. (3) Designing scheduling model combining with improve genetic programming and proximal policy optimization (IGP-PPO). Finally, relevant experiments are executed in a prototype experiment platform. The experiments indicate that the proposed method has superiority and generality in solving scheduling problem with dynamic events.

Download or read book Dynamic Scheduling in Large scale Manufacturing Processing Systems Using Multi agent Reinforcement Learning written by Shuhui Qu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Scheduling in manufacturing plays an essential role in building smart manufacturing from multiple points of view, including social, economic, and environmental. Optimal scheduling, or the allocation of jobs with different requirements for a manufacturing processing system to meet various objectives, has been discussed for several decades. However, advanced scheduling methods in modern processing systems have not significantly improved, nor have they been widely adopted by staff working on manufacturing production lines despite extensive research conducted into scheduling. Most traditional scheduling methods require statistical assumptions, which cannot support operations for a dynamic and stochastic modern processing system. In addition, most proposed scheduling methods are not sufficiently scalable for managing real-world, large-scale processing systems. To address these limitations, we focus on the dynamic scheduling approach, which involves scheduling real-time events in large-scale modern manufacturing systems, from a data-driven perspective. We implement reinforcement learning (RL) to learn adaptive, scalable, and optimal dynamic scheduling policies, since RL can learn the underlying processing system's patterns and adaptively make allocation decisions based on real-time job and server measurements. The direct application of existing RL methods on the scheduling problem in such large-scale processing systems is impractical and undesired due to the extremely high computational complexity of learning a good scheduling policy. This thesis presents a practical and systematic computational framework that integrates RL with existing expert knowledge at three levels: (1) System-level planning. The planning procedure characterizes the processing system by the nominal feasible region of the scheduling problem. (2) Algorithm-level design. The design of the algorithm in RL is carefully selected as the index-policy-based, multi-agent RL, significantly reducing control policy search complexity. (3) Learning-level demonstration. During the learning process of RL, the existing expert knowledge is used as a demonstration to increase search efficiency and stabilize the RL learning process. We conduct various experiments in both real factory scenarios and simulated environments to evaluate the performance of the framework on processing system scheduling problems. The effectiveness of the proposed index-policy-based, multi-agent reinforcement learning (MARL) method is evidenced by its performance over traditional dynamic scheduling methods, with a linear computational time complexity in regard to the number of machines and job classes.

Download or read book Artificial Intelligence in Reactive Scheduling written by R. Kerr and published by Springer. This book was released on 2016-01-09 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume encompasses state-of-the-art developments in AI-based reactive scheduling for real-time operation management in manufacturing shop floors. It is a collection of papers from the Second International Workshop of the IFIP Working Group 5.7 which brought together researchers from management information systems and knowledge engineering to expand the focus on applying new knowledge-based techniques.

Download or read book Generic Multi Agent Reinforcement Learning Approach for Flexible Job Shop Scheduling written by Schirin Bär and published by Springer Nature. This book was released on 2022-10-01 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: The production control of flexible manufacturing systems is a relevant component that must go along with the requirements of being flexible in terms of new product variants, new machine skills and reaction to unforeseen events during runtime. This work focuses on developing a reactive job-shop scheduling system for flexible and re-configurable manufacturing systems. Reinforcement Learning approaches are therefore investigated for the concept of multiple agents that control products including transportation and resource allocation.

Download or read book Intelligent Decision Making An AI Based Approach written by Gloria Phillips-Wren and published by Springer Science & Business Media. This book was released on 2008-03-04 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Decision Support Systems have the potential to transform human decision making by combining research in artificial intelligence, information technology, and systems engineering. The field of intelligent decision making is expanding rapidly due, in part, to advances in artificial intelligence and network-centric environments that can deliver the technology. Communication and coordination between dispersed systems can deliver just-in-time information, real-time processing, collaborative environments, and globally up-to-date information to a human decision maker. At the same time, artificial intelligence techniques have demonstrated that they have matured sufficiently to provide computational assistance to humans in practical applications. This book includes contributions from leading researchers in the field beginning with the foundations of human decision making and the complexity of the human cognitive system. Researchers contrast human and artificial intelligence, survey computational intelligence, present pragmatic systems, and discuss future trends. This book will be an invaluable resource to anyone interested in the current state of knowledge and key research gaps in the rapidly developing field of intelligent decision support.

Download or read book Multi Agent Based Beam Search for Real Time Production Scheduling and Control written by Shu Gang Kang and published by Springer Science & Business Media. This book was released on 2012-10-10 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Multi-Agent Based Beam Search (MABBS) method systematically integrates four major requirements of manufacturing production - representation capability, solution quality, computation efficiency, and implementation difficulty - within a unified framework to deal with the many challenges of complex real-world production planning and scheduling problems. Multi-agent Based Beam Search for Real-time Production Scheduling and Control introduces this method, together with its software implementation and industrial applications. This book connects academic research with industrial practice, and develops a practical solution to production planning and scheduling problems. To simplify implementation, a reusable software platform is developed to build the MABBS method into a generic computation engine. This engine is integrated with a script language, called the Embedded Extensible Application Script Language (EXASL), to provide a flexible and straightforward approach to representing complex real-world problems. Adopting an in-depth yet engaging and clear approach, and avoiding confusing or complicated mathematics and formulas, this book presents simple heuristics and a user-friendly software platform for system modelling. The supporting industrial case studies provide key information for students, lecturers, and industry practitioners alike. Multi-agent Based Beam Search for Real-time Production Scheduling and Control offers insights into the complex nature of and a practical total solution to production planning and scheduling, and inspires further research and practice in this promising research area.

Download or read book Dynamic Scheduling for Flexible Job Shop with Insufficient Transportation Resources Via Graph Neural Network and Deep Reinforcement Learning written by Min Zhang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The smart workshop is a powerful tool for manufacturing companies to reduce waste and improve production efficiency through real-time data analysis for self-organized production. Automated Guided Vehicles (AGVs) have been widely used for material handling in smart workshop due to their high degree of autonomy, flexibility and powerful end-to-end capability to cope with logistics tasks in production modes such as multiple species and small batch, and mass customization. However, the highly dynamic, complex and uncertain nature of the smart job shop environment makes production scheduling with limited transportation resources in mind a challenge. To this end, this paper addresses the dynamic flexible job shop scheduling problem with insufficient transportation resources (DFJSP-ITR), and learn high-quality priority dispatching rule (PDR) end-to-end to minimize makespan by the proposed deep reinforcement learning (DRL) method. To achieve integrated decision making for operation, machine and AGV, an architecture based on heterogeneous graph neural network and deep reinforcement learning is proposed. Considering the impact of different AGV distribution methods on the scheduling objective, this paper compares two different AGV distribution methods. Experiments show that the proposed method has superiority and good generalization ability compared with the current PDRs-based methods regardless of the AGV distribution strategy used.

Download or read book Chemical Production Scheduling written by Christos T. Maravelias and published by Cambridge University Press. This book was released on 2021-05-06 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand common scheduling as well as other advanced operational problems with this valuable reference from a recognized leader in the field. Beginning with basic principles and an overview of linear and mixed-integer programming, this unified treatment introduces the fundamental ideas underpinning most modeling approaches, and will allow you to easily develop your own models. With more than 150 figures, the basic concepts and ideas behind the development of different approaches are clearly illustrated. Addresses a wide range of problems arising in diverse industrial sectors, from oil and gas to fine chemicals, and from commodity chemicals to food manufacturing. A perfect resource for engineering and computer science students, researchers working in the area, and industrial practitioners.

Download or read book Service management and scheduling in cloud manufacturing written by Yongkui Liu and published by Walter de Gruyter GmbH & Co KG. This book was released on 2022-08-01 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book establishes the concept of cloud manufacturing and describes the technological system behind it. The authors discuss key technologies such as resources sensation and access, service-oriented architecture, cloud service management and evaluation, and interface visualization. With abundant case studies, the book is an essential reference for researchers and engineers in manufacturing and information management.

Download or read book Advances in Swarm Intelligence written by Ying Tan and published by Springer Nature. This book was released on 2021-07-07 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: his two-volume set LNCS 12689-12690 constitutes the refereed proceedings of the 12th International Conference on Advances in Swarm Intelligence, ICSI 2021, held in Qingdao, China, in July 2021. The 104 full papers presented in this volume were carefully reviewed and selected from 177 submissions. They cover topics such as: Swarm Intelligence and Nature-Inspired Computing; Swarm-based Computing Algorithms for Optimization; Particle Swarm Optimization; Ant Colony Optimization; Differential Evolution; Genetic Algorithm and Evolutionary Computation; Fireworks Algorithms; Brain Storm Optimization Algorithm; Bacterial Foraging Optimization Algorithm; DNA Computing Methods; Multi-Objective Optimization; Swarm Robotics and Multi-Agent System; UAV Cooperation and Control; Machine Learning; Data Mining; and Other Applications.

Download or read book A Cooperative Hierarchical Deep Reinforcement Learning Based Multi Agent Method for Distributed Job Shop Scheduling Problem with Random Job Arrivals written by Jiang-Ping Huang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed manufacturing has been an important trend in the industrial field, in which the production cost can be reduced through the cooperation among factories. In the real production, the random job arrivals are regular for the enterprises with daily delivered production tasks. In the paper, Distributed Job-shop Scheduling Problem (DJSP) with random job arrivals is studied. The distributed characteristics and the uncertain disturbance raise higher demands on the responsiveness and the self-adaptiveness of the scheduling method. To meet the scheduling requirements, a hierarchical Deep Reinforcement Learning (DRL) based multi-agent method Agentin is presented where the assigning agent (Agenta) and the sequencing agent (Agents) are respectively designed for job allocation and job sequencing, and they share the system information and extract the features they need independently. Agenta and Agents are both based on the specially-designed DQN framework, which has a variable threshold probability in the training stage, and it can balance the exploitation and exploration in the model training. For Agenta and Agents, two Markov Decision Process (MDP) formulations are established with elaborately-explored state features, rules-based action spaces and objective-oriented reward functions. Based on 1350 different production instances, the independent utility tests prove the effectiveness of the independent agents and the importance of the cooperation among the agents. The comparison test with the related algorithms validates the effectiveness of the integrated multi-agent method.

Download or read book Advances in Artificial Intelligence in Manufacturing written by Achim Wagner and published by Springer Nature. This book was released on with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Intelligent Scheduling written by M. Aarup and published by Springer Science & Business. This book was released on 1994 with total page 792 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scheduling complex processes, such as chemical manufacturing or space shuttle launches, is a focus of substantial effort throughout industry and government. In the past 20 years, the fields of operations research and operations management have tackled scheduling problems with considerable success. Recently, the artificial intelligence community has turned its attention to this class of problems, resulting in a fresh corpus of research and application that extends previous results. This book, comprising original contributions from experts in the field, highlights these new advances. These chapters present complete systems, stressing their unique characteristics, rather than presenting simple research results. Applications-oriented chapters are also included to inform researchers of state-of-the-art methodologies. Researchers and practitioners in industry and government will find this book valuable. It will also serve as an ideal text for a graduate course in knowledge-based scheduling.

Download or read book Population Based Approaches to the Resource Constrained and Discrete Continuous Scheduling written by Ewa Ratajczak-Ropel and published by Springer. This book was released on 2017-08-21 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses two of the most difficult and computationally intractable classes of problems: discrete resource constrained scheduling, and discrete-continuous scheduling. The first part of the book discusses problems belonging to the first class, while the second part deals with problems belonging to the second class. Both parts together offer valuable insights into the possibility of implementing modern techniques and tools with a view to obtaining high-quality solutions to practical and, at the same time, computationally difficult problems. It offers a valuable source of information for practitioners dealing with the real-world scheduling problems in industry, management and administration. The authors have been working on the respective problems for the last decade, gaining scientific recognition through publications and active participation in the international scientific conferences, and their results are obtained using population-based methods. Dr E. Ratajczk-Ropel explores multiple agent and A-Team concepts, while Dr A. Skakovski focuses on evolutionary algorithms with a particular focus on the population learning paradigm.

Download or read book Decentralized Holistic Production Scheduling with Multi agent Deep Reinforcement Learning written by Jens Popper and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Intelligent Scheduling Systems written by Donald E. Brown and published by Springer. This book was released on 1995 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scheduling is a resource allocation problem which exists in virtually every type of organization. Scheduling problems have produced roughly 40 years of research primarily within the OR community. This community has traditionally emphasized mathematical modeling techniques which seek exact solutions to well formulated optimization problems. While this approach produced important results, many contemporary scheduling problems are particularly difficult. Hence, over the last ten years operations researchers interested in scheduling have turned increasingly to more computer intensive and heuristic approaches. At roughly the same time, researchers in AI began to focus their methods on industrial and management science applications. The result of this confluence of fields has been a period of remarkable growth and excitement in scheduling research. Intelligent Scheduling Systems captures the results of a new wave of research at the forefront of scheduling research, of interest to researchers and practitioners alike. Presented are an array of the latest contemporary tools -- math modeling to tabu search to genetic algorithms -- that can assist in operational scheduling and solve difficult scheduling problems. The book presents the most recent research results from both operations research (OR) and artificial intelligence (AI) focusing their efforts on real scheduling problems.

Download or read book Automated Workflow Scheduling in Self Adaptive Clouds written by G. Kousalya and published by Springer. This book was released on 2017-05-25 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely text/reference presents a comprehensive review of the workflow scheduling algorithms and approaches that are rapidly becoming essential for a range of software applications, due to their ability to efficiently leverage diverse and distributed cloud resources. Particular emphasis is placed on how workflow-based automation in software-defined cloud centers and hybrid IT systems can significantly enhance resource utilization and optimize energy efficiency. Topics and features: describes dynamic workflow and task scheduling techniques that work across multiple (on-premise and off-premise) clouds; presents simulation-based case studies, and details of real-time test bed-based implementations; offers analyses and comparisons of a broad selection of static and dynamic workflow algorithms; examines the considerations for the main parameters in projects limited by budget and time constraints; covers workflow management systems, workflow modeling and simulation techniques, and machine learning approaches for predictive workflow analytics. This must-read work provides invaluable practical insights from three subject matter experts in the cloud paradigm, which will empower IT practitioners and industry professionals in their daily assignments. Researchers and students interested in next-generation software-defined cloud environments will also greatly benefit from the material in the book.