Agent Based Manufacturing And Control Systems

Traditional manufacturing systems rely upon centralized, hierarchical systems that are not responsive enough to the increasing demand for mass customization. Decentralized, or heterarchical, management systems using autonomous agents promise to nullify the limitations of previous solutions. Agent-Based Manufacturing and Control Systems: New

Low-volume high-variety products like personalized cars or customized engines will be the key issues for manufacturing in the 21st century. The necessary control technology is based on the concept of holons, which are the units of production and behave as autonomous and cooperative agents, providing flexibility, adaptability, agility, and dynamic reconfigurability. This book presents the latest research results in agent-based manufacturing as carried out by researchers in academia and industry within the international "Holonic Manufacturing Systems" project. As this project was driven by industry, the results presented here are of vital interest not just to researchers in agent technologies or distributed artificial intelligence, but also to engineers and professionals in industry who have to respond to rapid changes and new demands in production.

At the crossroads of artificial intelligence, manufacturing engineering, operational research and industrial engineering and management, multi-agent based production planning and control is an intelligent and industrially crucial technology with increasing importance. This book provides a complete overview of multi-agent based methods for today’s competitive manufacturing environment, including the Job Shop Manufacturing and Re-entrant Manufacturing processes. In addition to the basic control and scheduling systems, the author also highlights advance research in numerical optimization methods and wireless sensor networks and their impact on intelligent production planning and control system operation. Enables students, researchers and engineers to understand the fundamentals and theories of multi-agent based production planning and control Written by an author with more than 20 years’ experience in studying and formulating a complete theoretical system in production planning technologies Fully illustrated throughout, the methods for production planning, scheduling and controlling are presented using experiments, numerical simulations and theoretical analysis Comprehensive and concise, Multi-Agent Based Production Planning and Control is aimed at the practicing engineer and graduate student in industrial engineering, operational research, and mechanical engineering. It is also a handy guide for advanced students in artificial intelligence and computer engineering.

Presents a methodology developed by DaimlerChrysler. Illustrates the methodology through detailed case studies.

Agent Technology, or Agent-Based Approaches, is a new paradigm for developing software applications. It has been hailed as 'the next significant breakthrough in software development', and 'the new revolution in software' after object technology or object-oriented programming. In this context, an agent is a computer system which is capable of acting autonomously in its environment in order to meet its design objectives. So in the area of concurrent design and manufacturing, a manufacturing resource, namely a machine or an operator, may cooperate and negotiate with other agents for task assignment; and an existing engineering software can be integrated with a distributed integrated engineering design and manufacturing system. Hence in agent-based systems, there is no centralized system control structure, and no pre-defined agenda for the system execution, as exist in traditional systems. This book systematically describes the principles, key issues, and applications of agent technology in relation to concurrent engineering design and manufacturing. It introduces the methodology, standards, frameworks, tools, and languages of agent-based approaches and presents a general procedure for building agent-based concurrent engineering design and manufacturing systems. Both professional and university researchers and postgraduates should find this an invaluable presentation of the corresponding theories and methods, with some practical examples for developing multi-agent systems in the domain.

Industrial Agents explains how multi-agent systems improve collaborative networks to offer dynamic service changes, customization, improved quality and reliability, and flexible infrastructure. Learn how these platforms can offer distributed intelligent management and control functions with communication, cooperation and synchronization capabilities, and also provide for the behavior specifications of the smart components of the system. The book offers not only an introduction to industrial agents, but also clarifies and positions the vision, on-going efforts, example applications, assessment and roadmap applicable to multiple industries. This edited work is guided and co-authored by leaders of the IEEE Technical Committee on Industrial Agents who represent both academic and industry perspectives and share the latest research along with their hands-on experiences prototyping and deploying industrial agents in industrial scenarios. Learn how new scientific approaches and technologies aggregate resources such next generation intelligent systems, manual workplaces and information and material flow system Gain insight from experts presenting the latest academic and industry research on multi-agent systems Explore multiple case studies and example applications showing industrial agents in a variety of scenarios Understand implementations across the enterprise, from low-level control systems to autonomous and collaborative management units

Issues such as logistics, the coordination of different teams, and automatic control of machinery become more difficult when dealing with large, complex projects. Yet all these activities have common elements and can be represented by mathematics. Linking theory to practice, Industrial Control Systems: Mathematical and Statistical Models and Techni

A comprehensive and hands-on introduction to the core concepts, methods, and applications of agent-based modeling, including detailed NetLogo examples. The advent of widespread fast computing has enabled us to work on more complex problems and to build and analyze more complex models. This book provides an introduction to one of the primary methodologies for research in this new field of knowledge. Agent-based modeling (ABM) offers a new way of doing science: by conducting computer-based experiments. ABM is applicable to complex systems embedded in natural, social, and engineered contexts, across domains that range from engineering to ecology. An Introduction to Agent-Based Modeling offers a comprehensive description of the core concepts, methods, and applications of ABM. Its hands-on approach—with hundreds of examples and exercises using NetLogo—enables readers to begin constructing models immediately, regardless of experience or discipline. The book first describes the nature and rationale of agent-based modeling, then presents the methodology for designing and building ABMs, and finally discusses how to utilize ABMs to answer complex questions. Features in each chapter include step-by-step guides to developing models in the main text; text boxes with additional information and concepts; end-of-chapter explorations; and references and lists of relevant reading. There is also an accompanying website with all the models and code.

This dissertation, "Design and Analysis of Agent-based FMS Control Systems" by Chi-kit, Fan, 樊智傑, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled Design and analysis of agent-based FMS control systems submitted by Fan Chi Kit for the degree of Doctor of Philosophy at The University of Hong Kong in March 2005 A flexible manufacturing system (FMS) is a complex manufacturing system, which demands a robust control software for its scheduling, planning and control functions. This study considers the applications of multi-agent systems (MAS) in FMS control systems. Its aim was to establish a new hybrid-based approach in the design and analysis of agent-based FMS control systems. The proposed hybrid approach is based on the UML (Unified Modelling Language) and CPN (Coloured Petri Net) modeling technologies. The UML modeling methodology was adopted in the design and specification of the MAS-based FMS control systems and the individual agent. A mapping mechanism was developed to convert the UML views into CPN models. The CPN models can then be used to represent the dynamic behavior and interactions of agents in the MAS. In the corresponding CPN model, resource allocation by agents is represented by the color tokens. Three different flexible assembly cell (FAC) configurations were used as examples of manufacturing systems. According to a simple FAC configuration, a generic agent-based FAC control system was established. Using this generic agent-based FAC control system as the basis, the hybrid UML-CPN modeling approach was used to establish the MAS models for the two FAC configurations. To evaluate system flexibility and the effectiveness of different scheduling approaches, two different pallet allocation rules, the first-come first-served (FCFS) rule and the tabu search- based algorithm, were applied to each of the three FAC configurations. Simulation and analysis of the CPN models were then carried out to evaluate the dynamic behavior and interactions of the agent-based FAC control systems. Token activities of each simulation run were recorded and used for analysis. A data mining algorithm, the Apriori algorithm, was then used to mine the agent activity data to obtain useful information for evaluating the performances of the different agent-based FAC control systems in the case studies. Based on the evaluation results, the UML components of the agent-based system model can be modified in accordance with the dynamic models. The proposed hybrid UML-CPN models are able to cope with the flexibility requirements of complex flexible manufacturing environments. The examples demonstrated that these hybrids were to support the robust design and flexibility requirements of agent-based control systems corresponding to changes or disturbances in flexible manufacturing systems. The design methodology and analytical framework based on the hybrid UML-CPN models were found to be adaptable and flexible, and were capable of coping with sophisticated planning and scheduling problems in complex automated manufacturing environments under different system configurations. DOI: 10.5353/th_b3158032 Subjects: Intelligent agents (Computer software) Production control - Data processing Computer integrated manufacturing systems Manufacturing processes - Automation

Collaborative design has attracted much attention in the research community in recent years. With increasingly decentralized manufacturing systems and processes, more collaborative approaches and systems are needed to support distributed manufacturing operations. "Collaborative Design and Planning for Digital Manufacturing" presents a focused collection of quality chapters on the state-of-the-art research efforts in the area of collaborative design and planning, as well as their practical applications towards digital manufacturing. "Collaborative Design and Planning for Digital Manufacturing" provides both a broad-based review of the key areas of research in digital manufacturing, and an in-depth treatment of particular methodologies and systems, from collaborative design to distributed planning, monitoring and control. Recent development and innovations in this area provide a pool of focused research efforts, relevant to a wide readership from academic researchers to practicing engineers.