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 Multi agent Reinforcement Learning Approaches for Distributed Job Shop Scheduling Problems written by Thomas Gabel and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Optimization and Learning written by Bernabé Dorronsoro and published by Springer Nature. This book was released on 2020-02-15 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the Third International Conference on Optimization and Learning, OLA 2020, held in Cádiz, Spain, in February 2020. The 23 full papers were carefully reviewed and selected from 55 submissions. The papers presented in the volume focus on the future challenges of optimization and learning methods, identifying and exploiting their synergies,and analyzing their applications in different fields, such as health, industry 4.0, games, logistics, etc.
Download or read book Progress in Artificial Intelligence and Pattern Recognition written by Yanio Hernández Heredia and published by Springer. This book was released on 2018-09-21 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2018, held in Havana, Cuba, in September 2018. The 42 full papers presented were carefully reviewed and selected from 101 submissions. The papers promote and disseminate ongoing research on mathematical methods and computing techniques for artificial intelligence and pattern recognition, in particular in bioinformatics, cognitive and humanoid vision, computer vision, image analysis and intelligent data analysis, as well as their application in a number of diverse areas such as industry, health, robotics, data mining, opinion mining and sentiment analysis, telecommunications, document analysis, and natural language processing and recognition.
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 Reinforcement Learning written by Abdelhamid Mellouk and published by IntechOpen. This book was released on 2011-01-14 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic.
Download or read book Multi agent Workload Control and Flexible Job Shop Scheduling written by Zuobao Wu and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Both new rules are nonparametric and easy to be implemented in practice. A job release mechanism is applied to reduce job flowtimes (up to 20.3%) and work-in-process inventory (up to 33.1%), without worsening earliness and tardiness, and lead time performances. Flexible job shop scheduling problems are an important extension of the classical job shop scheduling problems and present additional complexity. A multi-agent scheduling method with job earliness and tardiness objectives in a flexible job shop environment is proposed. A new job routing and sequencing mechanism is developed. In this mechanism, different criteria for two kinds of jobs are proposed to route these jobs. Two sequencing algorithms based on existing methods are developed to deal with these two kinds of jobs. The proposed methodology is implemented in a flexible job shop environment. The computational results indicate that the proposed methodology is extremely fast.
Download or read book Optimization and Learning written by and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the Third International Conference on Optimization and Learning, OLA 2020, held in Cádiz, Spain, in February 2020. The 23 full papers were carefully reviewed and selected from 55 submissions. The papers presented in the volume focus on the future challenges of optimization and learning methods, identifying and exploiting their synergies, and analyzing their applications in different fields, such as health, industry 4.0, games, logistics, etc. --
Download or read book Learning in Cooperative Multi Agent Systems written by Thomas Gabel and published by Sudwestdeutscher Verlag Fur Hochschulschriften AG. This book was released on 2009-09 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a distributed system, a number of individually acting agents coexist. In order to achieve a common goal, coordinated cooperation between the agents is crucial. Many real-world applications are well-suited to be formulated in terms of spatially or functionally distributed entities. Job-shop scheduling represents one such application. Multi-agent reinforcement learning (RL) methods allow for automatically acquiring cooperative policies based solely on a specification of the desired joint behavior of the whole system. However, the decentralization of the control and observation of the system among independent agents has a significant impact on problem complexity. The author Thomas Gabel addresses the intricacy of learning and acting in multi-agent systems by two complementary approaches. He identifies a subclass of general decentralized decision-making problems that features provably reduced complexity. Moreover, he presents various novel model-free multi-agent RL algorithms that are capable of quickly obtaining approximate solutions in the vicinity of the optimum. All algorithms proposed are evaluated in the scope of various established scheduling benchmark problems.
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 Introduction to Scheduling written by Yves Robert and published by CRC Press. This book was released on 2009-11-18 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Full of practical examples, Introduction to Scheduling presents the basic concepts and methods, fundamental results, and recent developments of scheduling theory. With contributions from highly respected experts, it provides self-contained, easy-to-follow, yet rigorous presentations of the material.The book first classifies scheduling problems and
Download or read book Multiagent Scheduling written by Alessandro Agnetis and published by Springer Science & Business Media. This book was released on 2014-01-31 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scheduling theory has received a growing interest since its origins in the second half of the 20th century. Developed initially for the study of scheduling problems with a single objective, the theory has been recently extended to problems involving multiple criteria. However, this extension has still left a gap between the classical multi-criteria approaches and some real-life problems in which not all jobs contribute to the evaluation of each criterion. In this book, we close this gap by presenting and developing multi-agent scheduling models in which subsets of jobs sharing the same resources are evaluated by different criteria. Several scenarios are introduced, depending on the definition and the intersection structure of the job subsets. Complexity results, approximation schemes, heuristics and exact algorithms are discussed for single-machine and parallel-machine scheduling environments. Definitions and algorithms are illustrated with the help of examples and figures.
Download or read book Intelligent Quality Systems written by Duc T. Pham and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the tenn quality does not have a precise and universally accepted definition, its meaning is generally well understood: quality is what makes the difference between success and failure in a competitive world. Given the importance of quality, there is a need for effective quality systems to ensure that the highest quality is achieved within given constraints on human, material or financial resources. This book discusses Intelligent Quality Systems, that is quality systems employing techniques from the field of Artificial Intelligence (AI). The book focuses on two popular AI techniques, expert or knowledge-based systems and neural networks. Expert systems encapsulate human expertise for solving difficult problems. Neural networks have the ability to learn problem solving from examples. The aim of the book is to illustrate applications of these techniques to the design and operation of effective quality systems. The book comprises 8 chapters. Chapter 1 provides an introduction to quality control and a general discussion of possible AI-based quality systems. Chapter 2 gives technical information on the key AI techniques of expert systems and neural networks. The use of these techniques, singly and in a combined hybrid fonn, to realise intelligent Statistical Process Control (SPC) systems for quality improvement is the subject of Chapters 3-5. Chapter 6 covers experimental design and the Taguchi method which is an effective technique for designing quality into a product or process. The application of expert systems and neural networks to facilitate experimental design is described in this chapter.
Download or read book Constraint Based Scheduling written by Philippe Baptiste and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Constraint Programming is a problem-solving paradigm that establishes a clear distinction between two pivotal aspects of a problem: (1) a precise definition of the constraints that define the problem to be solved and (2) the algorithms and heuristics enabling the selection of decisions to solve the problem. It is because of these capabilities that Constraint Programming is increasingly being employed as a problem-solving tool to solve scheduling problems. Hence the development of Constraint-Based Scheduling as a field of study. The aim of this book is to provide an overview of the most widely used Constraint-Based Scheduling techniques. Following the principles of Constraint Programming, the book consists of three distinct parts: The first chapter introduces the basic principles of Constraint Programming and provides a model of the constraints that are the most often encountered in scheduling problems. Chapters 2, 3, 4, and 5 are focused on the propagation of resource constraints, which usually are responsible for the "hardness" of the scheduling problem. Chapters 6, 7, and 8 are dedicated to the resolution of several scheduling problems. These examples illustrate the use and the practical efficiency of the constraint propagation methods of the previous chapters. They also show that besides constraint propagation, the exploration of the search space must be carefully designed, taking into account specific properties of the considered problem (e.g., dominance relations, symmetries, possible use of decomposition rules). Chapter 9 mentions various extensions of the model and presents promising research directions.
Download or read book Reinforcement Learning for Job shop Scheduling written by Wei Zhang and published by . This book was released on 1996 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation studies applying reinforcement learning algorithms to discover good domain-specific heuristics automatically for job-shop scheduling. It focuses on the NASA space shuttle payload processing problem. The problem involves scheduling a set of tasks to satisfy a set of temporal and resource constraints while also seeking to minimize the total length (makespan) of the schedule. The approach described in the dissertation employs a repair-based scheduling problem space that starts with a critical-path schedule and incrementally repairs constraint violations with the goal of finding a short conflict-free schedule. The temporal difference (TD) learning algorithm TD([lambda]) is applied to train a neural network to learn a heuristic evaluation function for choosing repair actions over schedules. This learned evaluation function is used by a one-step lookahead search procedure to nd solutions to new scheduling problems. Several important issues that affect the success and the efficiency of learning have been identified and deeply studied. These issues include schedule representation, network architectures, and learning strategies. A number of modifications to the TD([lambda]) algorithm are developed to improve learning performance. Learning is investigated based on both hand-engineered features and raw features. For learning from raw features, a time-delay neural network architecture is developed to extract features from irregular-length schedules. The learning approach is evaluated on synthetic problems and on problems from a NASA space shuttle payload processing task. The evaluation function is learned on small problems and then applied to solve larger problems. Both learning-based schedulers (using hand-engineered features and raw features respectively) perform better than the best existing algorithm for this task--Zweben's iterative repair method. It is important to understand why TD learning works in this application. Several performance measures are employed to investigate learning behavior. We verified that TD learning works properly in capturing the evaluation function. It is concluded that TD learning along with a set of good features and a proper neural network is the key to this success. The success shows that reinforcement learning methods have the potential for quickly finding high-quality solutions to other combinatorial optimization problems.
Download or read book Handbook on Scheduling written by Jacek Błażewicz and published by Springer Science & Business Media. This book was released on 2007-06-12 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a theoretical and application-oriented analysis of deterministic scheduling problems in advanced planning and computer systems. The text examines scheduling problems across a range of parameters: job priority, release times, due dates, processing times, precedence constraints, resource usage and more, focusing on such topics as computer systems and supply chain management. Discussion includes single and parallel processors, flexible shops and manufacturing systems, and resource-constrained project scheduling. Many applications from industry and service operations management and case studies are described. The handbook will be useful to a broad audience, from researchers to practitioners, graduate and advanced undergraduate students.
Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
