Multi Agent Based Beam Search For Real Time Production Scheduling And Control
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Multi-Agent Based Beam Search for Real-Time Production Scheduling and Control
Author: Shu Gang Kang
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-10-11
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.
Industrial Applications of Holonic and Multi-Agent Systems
This book constitutes the refereed proceedings of the 7th International Conference on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2015, held in Valencia, Spain, in September 2015. The 19 revised full papers presented together with one invited talk were carefully reviewed and selected from 27 submissions. The papers are organized in the following topical sections: surveys, conceptual design and validation, digital factories and manufacturing control systems, ARUM: adaptive production management, and smart grids, complex networks and big data.
Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling
The production control of flexible manufacturing systems is a relevant component that must go along with the requirements of being flexible in terms of new product variants, new machine skills and reaction to unforeseen events during runtime. This work focuses on developing a reactive job-shop scheduling system for flexible and re-configurable manufacturing systems. Reinforcement Learning approaches are therefore investigated for the concept of multiple agents that control products including transportation and resource allocation.