Search Books

Multi-Agent Based Beam Search for Real-Time Production Scheduling and Control: Method, Software and Industrial Application

Author Shu Gang Kang, Shiu Hong Choi
Publisher Springer
📄 Viewing lite version Full site ›
🌎 Shop on Amazon — choose country
118.62 159.00 USD
🛒 Buy New on Amazon 🇺🇸 🏷 Buy Used — $130.20

✓ Usually ships in 24 hours

Share:
Book Details
PublisherSpringer
ISBN / ASIN1447161653
ISBN-139781447161653
AvailabilityUsually ships in 24 hours
Sales Rank99,999,999
MarketplaceUnited States 🇺🇸

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.