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

Released on by Shu Gang Kang
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  • Multi-Agent Based Beam Search for Real-Time Production Scheduling and Control Book Detail

  • Author : Shu Gang Kang
  • Release Date : 2012-10-10
  • Publisher : Springer Science & Business Media
  • Genre : Technology & Engineering
  • Pages : 136
  • ISBN 13 : 1447145763
  • File Size : 85,85 MB
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Multi-Agent Based Beam Search for Real-Time Production Scheduling and Control by Shu Gang Kang PDF Summary

Book Description: 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.

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