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Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling

job-shop scheduling system for flexible and re-configurable manufacturing systems. Reinforcement Learning approaches are therefore;

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MultiAgent Coordination

multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an;

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Job-shop scheduling with limited buffer capacities

Job-shop scheduling with limited buffer capacities is een boek van Silvia Heitmann;

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MultiAgentBased Production Planning and Control

provides a complete overview of multi-agent based methods for today s competitive manufacturing environment, including the Job Shop Manufacturing;

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Metaheuristics for Production Scheduling

Job Shop Scheduling Problems, Imed Kacem. 3. A Hybrid GRASP-Differential Evolution Algorithm for Solving Flow Shop Scheduling Problems with;

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Job Shop Lean

+ years of learning, teaching, researching, and implementing Job Shop Lean since 1999, this book Describes the concepts, tools;

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Multi-Agent Machine Learning

of personality traits. Framework for understanding a variety of methods and approaches in multi-agent machine learning. Discusses methods;

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Lean for High-mix-low-volume Manufacturers

+ years of learning, teaching, researching, and implementing Job Shop Lean since 1999, this book Describes the concepts, tools;

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Principles of Sequencing and Scheduling

theory. This book offers an introduction and overview of sequencing and scheduling and covers such topics as single-machine and multi;

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Intelligent Scheduling of Robotic Flexible Assembly Cells

effective strategy for scheduling RFAC in a multi-product assembly environment, in which dynamic status and multi-objective optimization problems;

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Intelligent Scheduling of Robotic Flexible Assembly Cells

effective strategy for scheduling RFAC in a multi-product assembly environment, in which dynamic status and multi-objective optimization problems;

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Genetic Programming for Production Scheduling

This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The;

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Learn Unity ML-Agents - Fundamentals of Unity Machine Learning

cooperative and competitive multi-agent ecosystem. What you will learn Develop Reinforcement and Deep Reinforcement Learning for games. Understand;

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Complex Scheduling

applications also job-shop problems with flexible machines, transportation or limited buffers are discussed. Discrete optimization methods like linear;

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A Book of Open Shop Scheduling

This book provides an in-depth presentation of algorithms for and complexity of open shop scheduling. Open shops allow operations of a job;

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Job Scheduling Strategies for Parallel Processing

This volume contains the papers selected after a very careful refereeing process for presentation during the Workshop on Job Scheduling;

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Principles of Sequencing and Scheduling

: * Extensions of the basic model* Parallel-machine models* Flow shop scheduling* Scheduling groups of jobs* The job shop problem* Simulation models for;

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Handbook of Reinforcement Learning and Control

; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning;

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Sequencing & Scheduling with Inaccurate Data

major approaches for dealing with such problems: a stochastic approach, a fuzzy approach, a robust approach and a stability approach. Each;

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Human-Robot Interaction Control Using Reinforcemen t Learning

control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning;

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Hands-On Reinforcement Learning with Python

will learn Understand the basics of reinforcement learning methods, algorithms, and elements Train an agent to walk using OpenAI Gym and;

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Evolutionary Computation in Scheduling

role and impacts of ant colony optimization (ACO) in job shop scheduling problems, before presenting the application of the ACO algorithm;

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Mission-Critical Application Driven Intelligent Maritime Networks

reinforcement learning. To accommodate the computation demands of maritime communication services, the authors propose a multi-vessel offloading;

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Load-oriented Manufacturing Control

companies. It is available in numerous variants and is especially significant for the job-shop and series production. Load-Oriented Manufacturing;

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Holonic and Multi-Agent Systems for Manufacturing

This book constitutes the refereed proceedings of the 5th International Conference on Industrial Applications of Holonic and Multi-Agent;

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Abstraction in Reinforcement Learning

Reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic;

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Statistical Reinforcement Learning

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic;

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