Generic multi-agent reinforcement learning approach for flexible job-shop scheduling / / Schirin Bär

• Autore: Bär Schirin
• Pubblicazione: Wiesbaden : , : Springer Vieweg, , [2022]; ©2022
• Descrizione fisica: 1 online resource (163 pages)
• Disciplina: 670.285
• Soggetto topico: Flexible manufacturing systems; Reinforcement learning; Aprenentatge per reforç (Intel·ligència artificial); Sistemes multiagent; Sistemes de producció flexibles
• Soggetto genere / forma: Llibres electrònics
• Nota di contenuto: Intro -- Danksagung -- Abstract -- Zusammenfassung -- Contents -- Abbreviations -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 Research Goals -- 1.2 Methodology -- 1.3 Structure of the Thesis -- 2 Requirements for Production Scheduling in Flexible Manufacturing -- 2.1 Foundations of Flexible Job-Shop Scheduling Problems -- 2.2 Requirement Analysis of Flexible Scheduling Solutions -- 2.2.1 Influences on Warehouse Control Systems -- 2.2.2 Influences on Manufacturing Control Systems -- 2.2.3 Derived and Ranked Requirements -- 2.3 State of the Art: Approaches to Solve Job-Shop Scheduling Problems -- 2.3.1 Conventional Scheduling Solutions -- 2.3.2 Reinforcement Learning Scheduling Solutions -- 2.4 Identification of the Research Gap -- 2.5 Contribution of this Work: Extended Flexible Job-Shop Scheduling Problem -- 3 Reinforcement Learning as an Approach for Flexible Scheduling -- 3.1 Understanding the Foundations: Formalization as a Markov Decision Process -- 3.1.1 Agent-Environment Interaction -- 3.1.2 Policies and Value Functions -- 3.1.3 Challenges Arising in Reinforcement Learning -- 3.2 Deep Q-Learning -- 3.2.1 Temporal Difference Learning and Q-Learning -- 3.2.2 Deep Q-Network -- 3.2.3 Loss Optimization -- 3.3 State of the Art: Cooperating Agents to Solve Complex Problems -- 3.3.1 Multi-Agent Learning Methods -- 3.3.2 Learning in Cooperative Multi-Agent RL Setups -- 3.4 Summary -- 4 Concept for Multi-Resources Flexible Job-Shop Scheduling -- 4.1 Concept for Agent-based Scheduling in FMS -- 4.1.1 Overall Concept -- 4.1.2 Job Specification -- 4.1.3 Petri Net Simulation -- 4.2 Formalization as a Markov Decision Process -- 4.2.1 Action Designs -- 4.2.2 State Designs -- 4.2.3 Reward Design -- 4.3 Considered Flexible Manufacturing System -- 4.4 Evaluation of the Technical Functionalities -- 4.5 Summary. ; 5 Multi-Agent Approach for Reactive Scheduling in Flexible Manufacturing -- 5.1 Training Set-up -- 5.2 Specification of the Reward Design -- 5.3 Evaluation of Suitable Training Strategies -- 5.3.1 Evaluation of MARL Algorithms -- 5.3.2 Selection of MARL Learning Methods -- 5.3.3 Evaluation of Parameter Sharing and Centralized Learning -- 5.4 Training Approach to Present Situations -- 5.5 Summary -- 6 Empirical Evaluation of the Requirements -- 6.1 Generalization to Various Products and Machines -- 6.2 Achieving the Global Objective -- 6.2.1 Comparison of Dense and Sparse Global Rewards -- 6.2.2 Cooperative Behavior -- 6.3 Benchmarking against Heuristic Search Algorithms -- 6.3.1 Evaluation for Unknown and Known Situations -- 6.3.2 Evaluation of Real-time Decision-Making -- 6.4 Consolidated Requirements Evaluation -- 6.5 Summary -- 7 Integration into a Flexible Manufacturing System -- 7.1 Acceptance Criteria for the Integration Concept -- 7.2 Integration Concept of MARL Scheduling Solution -- 7.2.1 Integration in the MES -- 7.2.2 Information Exchange -- 7.3 Design Cycle -- 7.3.1 Functioning Scheduling -- 7.3.2 Efficient Production Flow -- 7.3.3 Handling of Unforeseen Events -- 7.3.4 Handling of New Machine Skills -- 7.3.5 Handling of New Machines -- 7.4 Summary -- 8 Critical Discussion and Outlook -- 9 Summary -- 1 Bibliography.
• Titolo autorizzato: Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling
• ISBN: 9783658391799; 9783658391782
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996495171403316
• Lo trovi qui: Univ. di Salerno