Multi-Agent Based Beam Search for Real-Time Production Scheduling and Control [electronic resource] : Method, Software and Industrial Application / by Shu Gang Kang, Shiu Hong Choi.

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.

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.