Multi-Agent Based Beam Search for Intelligent Production Planning and Scheduling PDF Download

Author: Shugang Kang
Publisher: Open Dissertation Press
ISBN: 9781361476208
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Languages : en
Pages :

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Book Description
This dissertation, "Multi-agent Based Beam Search for Intelligent Production Planning and Scheduling" by Shugang, Kang, 康書剛, 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 Multi-agent based beam search for intelligent production planning and scheduling submitted by KANG SHUGANG for the degree of Doctor of Philosophy at the University of Hong Kong in September 2007 To survive fierce global competition, manufacturing enterprises need to improve their responsiveness to market changes and the productivity of their manufacturing systems. Production planning and scheduling requirements have become more demanding than ever before. Due to the complexities of scheduling problems, the impact of scheduling research on real-world applications is limited, and much research work is needed to develop effective methods and tools that can be directly applied to the planning and scheduling functions of modern manufacturing systems. This study attempts to address this issue by incorporating the philosophies and techniques of multiple approaches into a unified framework for development of a powerful hybrid method, an open software platform, and subsequently an integrated system for production planning, scheduling, and execution. Hence, an effective total solution is developed for real-world applications. In the theoretical aspect, a Multi-Agent Based Beam Search (MABBS) method is developed by integrating AI (Multi-Agent System, Expert System) and OR (Beam Search) methods. Although the multi-agent system (MAS) is a promising technique for modeling complex dynamic systems, the solution quality of MAS-based method is usually questionable. The MABBS method can take advantage of the representation power of MAS, and enhances MAS with beam search method for improving solution quality. As such, it provides a powerful hybrid method to solve complex real-world problems. To simplify the use of the MABBS method, computer languages and an open software platform are developed. The Knowledge Markup Language (KML) is an XML based markup language to model intelligent agents; the Information Markup Language (IML) is an XML based markup language for rapid construction of graphical user interfaces (GUI) for user interaction; and the Embedded eXtensible Application Script Language (EXASL) is embedded into KML and IML to provide a simple and versatile programming language for both knowledge representation and user interaction. It also provides a flexible tool for configuration of complex systems. The software platform includes an inference engine, a smart client and a toolkit. The inference engine is responsible for the MABBS-based computation. It can exploit the parallel computation power of network-connected computers to reduce computation time. The smart client renders graphical user interfaces (GUI) with IML, which provides rich functionalities and enables remote access over the Internet. The toolkit provides an integrated development environment for both back-end knowledge representation and front-end GUI development. The languages and the software platform provide a reusable and flexible approach for the rapid construction of customized production planning and scheduling systems. To apply the MABBS method, the languages and the software platform to practical production planning and scheduling problems, an Integrated Production Planning, Scheduling, and Executing System (IPPSES) is developed. In this system, a multi-agent system is designed and the MABBS method is used for complex production planning and scheduling. A workflow-based execution system is developed