Multi-Agent Based Beam Search for Real-Time Production Scheduling and Control

This book connects academic research with industrial practice, offering real-world solutions to production planning and scheduling problems. Introduces software which builds the MABBS method into a generic computation engine, and includes case studies.

The Multi-Agent Based Beam Search (MABBS) method systematically integrates four major requirements of manufacturing production - representation capability, solution quality, computation efficiency, and implementation difficulty - within a unified framework to deal with the many challenges of complex real-world production planning and scheduling problems.

Multi-agent Based Beam Search for Real-time Production Scheduling and Control introduces this method, together with its software implementation and industrial applications. This book connects academic research with industrial practice, and develops a practical solution to production planning and scheduling problems.

To simplify implementation, a reusable software platform is developed to build the MABBS method into a generic computation engine. This engine is integrated with a script language, called the Embedded Extensible Application Script Language (EXASL), to provide a flexible and straightforward approach to representing complex real-world problems.

Adopting an in-depth yet engaging and clear approach, and avoiding confusing or complicated mathematics and formulas, this book presents simple heuristics and a user-friendly software platform for system modelling. The supporting industrial case studies provide key information for students, lecturers, and industry practitioners alike.

Multi-agent Based Beam Search for Real-time Production Scheduling and Control offers insights into the complex nature of and a practical total solution to production planning and scheduling, and inspires further research and practice in this promising research area.

Describes production planning and scheduling in an in-depth, yet easy-to-understand, way Presents simple heuristics and a user-friendly software platform for system modeling Includes an industrial case study

Autorentext

Dr. Shu Gang Kang, with a Ph.D. from the University of Hong Kong, is a research associate at the Ohio State University. His major research interests include AI techniques and software engineering tools for production scheduling and industrial applications. Dr. Kang has been engaging in industrial research projects and working closely with the industry towards practical solutions to complex real-world engineering problems.

Dr. Shiu Hong Choi gained both his B.Sc. and Ph.D. degrees at the University of Birmingham in the UK. Prior to joining the University of Hong Kong, Dr. Choi had worked in computer industry as a CAD/CAM consultant. His research interests focus mainly on development of computer application systems for advanced product design and manufacturing. He has published a number of papers on virtual prototyping, RFID-based anti-counterfeiting, and production planning and scheduling.

Inhalt

1.Introduction.- 2.Literature Review.- 3.Research Methodology.- 4.The Multi-Stage Multi-Level Decision-Making Model.- 5.Knowledge-Directed opportunistic Search.- 6. The Multi-Agent Based Beam Search Method.- 7. Modeling Complex Decision-Making Problems with the MABBS Methods.- 8.The MABBS Engine.- 9.Industrial Application of the MABBS Method.- 10.Summary and Directions for Future Research.