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Multi-Agent Based Beam Search for Real-Time Production Scheduling and Control: Method, Software and Industrial Application 2013 ed. [Hardback]

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  • Formāts: Hardback, 134 pages, height x width: 235x155 mm, weight: 3435 g, X, 134 p. With online files/update., 1 Hardback
  • Izdošanas datums: 11-Oct-2012
  • Izdevniecība: Springer London Ltd
  • ISBN-10: 1447145755
  • ISBN-13: 9781447145752
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Multi-Agent Based Beam Search for Real-Time Production Scheduling and Control: Method, Software and Industrial Application 2013 ed.Palielināt
  • Formāts: Hardback, 134 pages, height x width: 235x155 mm, weight: 3435 g, X, 134 p. With online files/update., 1 Hardback
  • Izdošanas datums: 11-Oct-2012
  • Izdevniecība: Springer London Ltd
  • ISBN-10: 1447145755
  • ISBN-13: 9781447145752
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781447145752.html
Keywords:

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.



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.
1 Introduction
1(6)
1.1 Production Scheduling Problems
1(3)
1.1.1 Overview
1(1)
1.1.2 Concepts
1(2)
1.1.3 Theoretical Problems and Real-World Problems
3(1)
1.2 Research Challenges
4(3)
1.2.1 NP-Hard Property of Scheduling Problems
4(1)
1.2.2 Complex Constraints and Objectives
5(1)
1.2.3 Changes
5(1)
1.2.4 Integration of Planning and Scheduling
6(1)
References
6(1)
2 Literature Review
7(10)
2.1 Scheduling Methods
7(5)
2.1.1 Overview
7(1)
2.1.2 Optimization Algorithms
7(1)
2.1.3 Dispatching Rules
8(1)
2.1.4 AI-Based Scheduling
9(1)
2.1.5 Simulation-Based Scheduling
10(1)
2.1.6 Multi-Agent Based Scheduling
11(1)
2.1.7 Integration of the Scheduling Methods
12(1)
2.2 Scheduling Systems
12(5)
References
13(4)
3 Research Methodology
17(6)
3.1 What Obstructs Real-World Application of Scheduling Research?
17(2)
3.1.1 The Issues of the Traditional Research Methodology
17(1)
3.1.2 Disconnection Between Academic Research and Industrial Practice
18(1)
3.2 The Four Major Requirements for a Practical Scheduling Method
19(1)
3.3 The Methodology of this Study
20(3)
References
21(2)
4 The Multi-Stage Multi-Level Decision-Making Model
23(8)
4.1 Decision-Making Problems
23(1)
4.2 Decomposition of Decision-Making Problems
24(1)
4.3 Parallel Decomposition
25(1)
4.4 Sequential Decomposition
25(1)
4.5 The MSMLDM Model
26(5)
References
30(1)
5 Knowledge-Directed Opportunistic Search
31(20)
5.1 Exploring the Solution-Path Space
31(9)
5.2 Strategies for Exploring a Combinatorially Explosive Solution Space
40(1)
5.3 The Beam Search Method
40(1)
5.4 The Knowledge-Directed Opportunistic Search Method
41(7)
5.4.1 Intelligent Allocation of Computation Resources
42(5)
5.4.2 Knowledge-Directed Search
47(1)
5.5 Comparison of KDOS with Other Search Methods
48(3)
References
49(2)
6 The Multi-Agent Based Beam Search Method
51(74)
6.1 Agent-Based Modeling for Complex Manufacturing Systems
51(1)
6.2 The Multi-Agent Based Beam Search Method
52(9)
6.2.1 Introduction
52(2)
6.2.2 Working Mechanism of Simulation
54(2)
6.2.3 Integration of Agent-Based Simulation with the KDOS Method
56(1)
6.2.4 Working Mechanism of the MABBS Method
56(5)
6.3 ANN-Based Knowledge Representation for Agent-Based Decision-Making
61(7)
6.3.1 Problem Analysis
61(3)
6.3.2 Artificial Neural Network
64(1)
6.3.3 Application of ANN to Decision-Making Problems
65(1)
6.3.4 Integration of ANN with the MABBS Method
66(2)
6.4 Summary of the MABBS Method
68(55)
References
69(51)
9.2.5 Simulation, Optimization, and Learning
120(1)
9.2.6 Dealing with Uncertainties
121(1)
9.2.7 Presenting the Result
122(1)
9.3 Experience and Lessons
123(2)
10 Summary and Directions for Future Research
125(4)
10.1 Summary of this Study
125(1)
10.2 Directions for the Future Research and Development
126(3)
Authors Biography 129(2)
Index 131
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.