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Autore: |
Bär Schirin
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Titolo: |
Generic multi-agent reinforcement learning approach for flexible job-shop scheduling / / Schirin Bär
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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.: | 9910616202703321 |
Lo trovi qui: | Univ. Federico II |
Opac: | Controlla la disponibilità qui |