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

135 Pages·2013·2.31 MB·English

by  Shu Gang Kang

Multi-Agent Based Beam Search for Real-Time
Production Scheduling and Control
Shu Gang Kang Shiu Hong Choi
•
Multi-Agent Based
Beam Search for
Real-Time Production
Scheduling and Control
Method, Software and
Industrial Application
123
ShuGangKang Shiu HongChoi
The OhioStateUniversity Department of Industrialand
Columbus Manufacturing Systems Engineering
USA The Universityof HongKong
Hong Kong
Hong KongSAR
ISBN 978-1-4471-4575-2 ISBN 978-1-4471-4576-9 (eBook)
DOI 10.1007/978-1-4471-4576-9
SpringerLondonHeidelbergNewYorkDordrecht
LibraryofCongressControlNumber:2012947851
(cid:2)Springer-VerlagLondon2013
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Synopsis
Production scheduling is a long-standing research area of tremendous practical
value. The demand for it in industry is huge and acute. Regretfully, most sched-
uling research results are seldom applied to production practice and commercial
scheduling software systems seem not yet mature.
This has motivated the authors to develop a practical solution that can be
applied to complex real-world problems. This book presents our research work
aimed to narrow the gap between research and practice in production scheduling.
Production scheduling involves complex decision making to optimize certain
performance measures in order to accomplish production tasks timely and cost-
effectively. There aremanyresearchchallengesinproductionscheduling,such as
the NP-hard property, complex constraints and preferences, changes, and inte-
gration of planning and scheduling. Usually, these challenges are tackled sepa-
rately in theoretical investigations. Although such research efforts may provide
insightsintocertainaspectsoftheissue,theyoftendonotleadtototalsolutionsto
complex real-world problems. In order to create a practical production planning
and scheduling solution, it is necessary to systematically consider the major
challenges within a unified framework and work toward real-world applications.
Thisbooktacklesproductionschedulingwithanewapproach.Weidentifyfour
major requirements, namely modeling capability, solution quality, computation
efficiency, and implementation difficulty, to develop a practical solution to pro-
duction scheduling problems.
Based on this vision, a generic multi-stage multi-level decision-making
(MSMLDM) model is developed to formulate complex production planning and
scheduling problems, while the philosophy and techniques of the multi-agent
system (MAS) are employed to represent complex dynamic manufacturing sys-
tems. An enhanced beam search (BS) method, called the knowledge directed
opportunistic search (KDOS), is developed to strike a balance between solution
quality and computation efficiency. These techniques are seamlessly integrated
into a unified framework and thus lead to a promising method called the multi-
agent based beam search (MABBS). To simplify the software implementation of
v
vi Synopsis
the MABBS method, a reusable software platform is developed. The MABBS
method is built intoa generic computation engine, while a script language, called
embedded extensible application script language (EXASL), is developed to pro-
vide a flexible and straightforward approach to representing complex real-world
problems with the MABBS method. We are now working with a large manufac-
turing company in North America to apply our research result to industrial prac-
tices to evaluate its effectiveness. Some preliminary results are reported in this
book.
This book includes four parts. The first part provides a research background.
The second part elaborates the MABBS method, and the third part describes the
software implementation of the MABBS method. The fourth part presents an
industrialapplication oftheresearchresult.Thisbookis intendedfor researchers,
college students, and industrial practitioners who may wish to get an insight into
the complex nature of and practical solutions to production scheduling problems.
We hope it would help to promote further research and practice in this promising
research area.
Contents
1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Production Scheduling Problems . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.2 Concepts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.3 Theoretical Problems and Real-World Problems . . . . . 3
1.2 Research Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.1 NP-Hard Property of Scheduling Problems . . . . . . . . . 4
1.2.2 Complex Constraints and Objectives. . . . . . . . . . . . . . 5
1.2.3 Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.4 Integration of Planning and Scheduling. . . . . . . . . . . . 6
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Literature Review. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1 Scheduling Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.2 Optimization Algorithms. . . . . . . . . . . . . . . . . . . . . . 7
2.1.3 Dispatching Rules . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.4 AI-Based Scheduling . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.5 Simulation-Based Scheduling. . . . . . . . . . . . . . . . . . . 10
2.1.6 Multi-Agent Based Scheduling. . . . . . . . . . . . . . . . . . 11
2.1.7 Integration of the Scheduling Methods . . . . . . . . . . . . 12
2.2 Scheduling Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1 What Obstructs Real-World Application
of Scheduling Research?. . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1.1 The Issues of the Traditional
Research Methodology . . . . . . . . . . . . . . . . . . . . . . . 17
vii
viii Contents
3.1.2 Disconnection Between Academic Research
and Industrial Practice . . . . . . . . . . . . . . . . . . . . . . . 18
3.2 The Four Major Requirements for a Practical
Scheduling Method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3 The Methodology of this Study . . . . . . . . . . . . . . . . . . . . . . 20
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4 The Multi-Stage Multi-Level Decision-Making Model . . . . . . . . . 23
4.1 Decision-Making Problems . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2 Decomposition of Decision-Making Problems . . . . . . . . . . . . 24
4.3 Parallel Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.4 Sequential Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.5 The MSMLDM Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
5 Knowledge-Directed Opportunistic Search. . . . . . . . . . . . . . . . . . 31
5.1 Exploring the Solution-Path Space . . . . . . . . . . . . . . . . . . . . 31
5.2 Strategies for Exploring a Combinatorially
Explosive Solution Space . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.3 The Beam Search Method. . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.4 The Knowledge-Directed Opportunistic Search Method. . . . . . 41
5.4.1 Intelligent Allocation of Computation Resources . . . . . 42
5.4.2 Knowledge-Directed Search. . . . . . . . . . . . . . . . . . . . 47
5.5 Comparison of KDOS with Other Search Methods. . . . . . . . . 48
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
6 The Multi-Agent Based Beam Search Method. . . . . . . . . . . . . . . 51
6.1 Agent-Based Modeling for Complex
Manufacturing Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
6.2 The Multi-Agent Based Beam Search Method. . . . . . . . . . . . 52
6.2.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
6.2.2 Working Mechanism of Simulation . . . . . . . . . . . . . . 54
6.2.3 Integration of Agent-Based Simulation
with the KDOS Method . . . . . . . . . . . . . . . . . . . . . . 56
6.2.4 Working Mechanism of the MABBS Method . . . . . . . 56
6.3 ANN-Based Knowledge Representation for Agent-Based
Decision-Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6.3.1 Problem Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6.3.2 Artificial Neural Network . . . . . . . . . . . . . . . . . . . . . 64
6.3.3 Application of ANN to Decision-Making Problems . . . 65
6.3.4 Integration of ANN with the MABBS Method. . . . . . . 66
6.4 Summary of the MABBS Method. . . . . . . . . . . . . . . . . . . . . 68
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Contents ix
7 Modeling Complex Decision-Making Problems
with the MABBS Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
7.1 Modeling Dynamic Systems with Intelligent Agents. . . . . . . . 71
7.2 Representation of Agent Decision-Making Logic . . . . . . . . . . 74
7.3 Overview of the EXASL Language . . . . . . . . . . . . . . . . . . . 74
7.4 Getting Started with the EXASL Language. . . . . . . . . . . . . . 75
7.4.1 A Simple EXASL Example. . . . . . . . . . . . . . . . . . . . 75
7.4.2 EXASL Functions . . . . . . . . . . . . . . . . . . . . . . . . . . 76
7.4.3 EXASL Flow Control. . . . . . . . . . . . . . . . . . . . . . . . 76
7.4.4 EXASL Callback Statements. . . . . . . . . . . . . . . . . . . 78
7.4.5 EXASL Data Management . . . . . . . . . . . . . . . . . . . . 80
7.4.6 User Interface Support . . . . . . . . . . . . . . . . . . . . . . . 82
7.4.7 Organization of EXASL Programs. . . . . . . . . . . . . . . 83
7.4.8 EXASL Development Environment . . . . . . . . . . . . . . 83
7.4.9 Summary of the EXASL Language . . . . . . . . . . . . . . 83
7.5 Modeling MABBS Problems with the EXASL Language . . . . 84
7.5.1 Agent Logic Module. . . . . . . . . . . . . . . . . . . . . . . . . 84
7.5.2 EXASL API Functions for MABBS. . . . . . . . . . . . . . 85
7.5.3 Representing MABBS Problems with EXASL. . . . . . . 88
7.6 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
8 The MABBS Engine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
8.1 Data Management of the MABBS Engine. . . . . . . . . . . . . . . 91
8.2 The Working Mechanism of the MABBS Engine. . . . . . . . . . 97
8.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
8.2.2 The Initializing Phase. . . . . . . . . . . . . . . . . . . . . . . . 98
8.2.3 The Running Phase . . . . . . . . . . . . . . . . . . . . . . . . . 98
8.2.4 The Filtering Phase . . . . . . . . . . . . . . . . . . . . . . . . . 102
8.2.5 The Get-Result Phase. . . . . . . . . . . . . . . . . . . . . . . . 103
8.2.6 The Implementation of the EXASL API Functions. . . . 104
8.3 ANN-Based Knowledge Acquisition and Knowledge
Guided Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
8.4 Parallel Computation to Improve Performance. . . . . . . . . . . . 107
8.5 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
9 Industrial Application of the MABBS Method. . . . . . . . . . . . . . . 111
9.1 The Requirements for Real-Time Scheduling and
Control of Manufacturing Systems . . . . . . . . . . . . . . . . . . . . 112
9.2 MABBS-Based Simulation and Optimization for
Real-Time Scheduling and Control. . . . . . . . . . . . . . . . . . . . 114
9.2.1 Problem Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . 114
9.2.2 Interfacing with Real-Time Data . . . . . . . . . . . . . . . . 116
9.2.3 System Architecture. . . . . . . . . . . . . . . . . . . . . . . . . 117
9.2.4 The Agent Model for Manufacturing System. . . . . . . . 117
x Contents
9.2.5 Simulation, Optimization, and Learning . . . . . . . . . . . 120
9.2.6 Dealing with Uncertainties . . . . . . . . . . . . . . . . . . . . 121
9.2.7 Presenting the Result . . . . . . . . . . . . . . . . . . . . . . . . 122
9.3 Experience and Lessons . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
10 Summary and Directions for Future Research. . . . . . . . . . . . . . . 125
10.1 Summary of this Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
10.2 Directions for the Future Research and Development . . . . . . . 126
Authors Biography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
Chapter 1
Introduction
1.1 Production Scheduling Problems
1.1.1 Overview
Production scheduling is a complex decision-making procedure to allocate
resources to tasks over time, with the goal to optimize various performance
measures under various constraints. Thus, the production tasks can be accom-
plished timely and cost-effectively.
Becauseofitsimportancetothemanufacturingindustry,productionscheduling
has been attracting researchers from various domains to work toward effective
solutionsfordecades(Pinedo2002).However,duetothecomplexityofreal-world
productionschedulingproblems,academicresearchresultsareseldomcarriedout
intoindustrialapplication (Jacobs andWeston 2007).Much moreeffortisneeded
tofill the wide gapbetweenacademicresearch andindustrialpractice inthis area
(Herrmann 2005).
This chapter briefly describes production scheduling problems and highlights
some of the major research challenges. The purpose is to outline the authors’
overall perception on production scheduling. It will by no means be comprehen-
sive and in-depth. For a systematic study of scheduling problems, interested
readers may refer to text books on production scheduling (Pinedo 2002).
1.1.2 Concepts
To help to describe production scheduling problems clearly, we introduce the
following basic concepts:
1. Job;
2. Operation;
S.G.KangandS.H.Choi,Multi-AgentBasedBeamSearchforReal-Time 1
ProductionSchedulingandControl,DOI:10.1007/978-1-4471-4576-9_1,
(cid:2)Springer-VerlagLondon2013