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 |  موضوع: كتاب Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management السبت 30 ديسمبر 2023, 11:01 am | |
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Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management
Lyes Benyoucef , Bernard Grabot
Editors
و المحتوى كما يلي :
Contents
1 Intelligent Manufacturing Systems
E. Oztemel .1
1.1 Introduction .1
1.2 Traditional Manufacturing Systems 3
1.3 Changes in Manufacturing Systems: a Historical Perspective 6
1.4 Artificial Intelligence and Intelligent Manufacturing Systems .13
1.4.1 Technologies of Artificial Intelligence 13
1.4.2 Intelligent Manufacturing Systems 22
1.5 Properties of Intelligent Manufacturing Systems 25
1.6 Architecture of Intelligent Manufacturing Systems 27
1.7 Holonic Manufacturing Systems .33
1.8 Applications of Intelligent Manufacturing Systems 37
1.9 Conclusions .39
References 39
2 Agent-based System for Knowledge Acquisition and Management
Within a Networked Enterprise
A.J. Soroka 43
2.1 Agent-based and Related Systems 43
2.1.1 Origins of Agent Research 44
2.1.2 Definition of an Agent .46
2.1.3 Agent Architectures .47
2.1.4 Agent Types and Applications 50
2.1.5 Machine Learning for Generation of Knowledge Bases .53
2.2 Product Fault Knowledge Acquisition and Management .57
2.2.1 Automating Knowledge Base management .57
2.2.2 Analysis of Knowledge Base Management Process 58
2.3 Agent System for Knowledge Acquisition and Management .63
2.3.1 User Agent .65
2.3.2 Server Agent 70xvi Contents
2.3.3 Testing . 79
2.4 Conclusions 81
References . 81
3 Multi-agent Simulation-based Decision Support System
and Application in Networked Manufacturing Enterprises
H. Ding, W. Wang, M. Qiu and J. Dong . 87
3.1 Introduction 87
3.2 Literature Review . 88
3.2.1 Simulation Methods 89
3.2.2 Multi-agent Simulation . 89
3.2.3 Tools and Applications 90
3.3. Problem Description and Approach . 91
3.3.1 Platform Architecture 92
3.3.2 Multi-agent Supply Chain Simulation Model . 93
3.4 Modeling and Analysis . 94
3.4.1 Baseline Simulation Model . 95
3.4.2 Scenario Analysis 95
3.4.3 Inventory Control Policy . 96
3.4.4 Forecast Accuracy . 97
3.4.5 Build-to-plan vs. Build-to-order 99
3.4.6 Procurement Policy . 100
3.4.7 Truck Utilization . 101
3.5 Conclusions and Perspectives . 103
References . 104
4 A Collaborative Decision-making Approach for Supply Chain Based
on a Multi-agent System
Y. Ouzrout, A. Bouras, E.-H. Nfaoui and O. El Beqqali . 107
4.1 Introduction 108
4.2 Distributed Simulation and Supply Chain Management . 110
4.2.1 Decision-making and Multi-agent Approaches . 111
4.2.2 Multi-agent Systems Simulation . 111
4.3 The Supply Chain Modeling . 112
4.3.1 Supply Chain Modeling Methodology 113
4.3.2 The Safety Inventory Case Study 114
4.4 The Multi-agent Architecture . 116
4.4.1 The Modeling Principle . 116
4.4.2 Architecture . 117
4.4.3 Negotiation Protocols 120
4.5 Industrial Case Study 122
4.6 Conclusion and Perspectives 125
References . 126Contents xvii
5 Web-services-based e-Collaborative Framework to Provide Production
Control with Effective Outsourcing
A. Keshari, M. K. Tiwari and R. Teti .129
5.1 Introduction .130
5.2 Literature Review .131
5.3 Design of Web-services-based e-Collaborative Framework .133
5.3.1 The Proposed Framework 133
5.3.2 Design of System Components of Model 134
5.3.3 The Mathematical Model 139
5.3.4 Overall Objective Function .142
5.4 Protocols of Functional Agents .143
5.4.1 Machine Agent Protocol 143
5.4.2 Production Controlling Agent Protocol .144
5.4.3 Task Management Agent (TMA) Protocol 145
5.4.4 Hierarchy of Steps in Web-services-based e-Collaborative System145
5.5 Results and Discussion .145
5.5.1 Sample-sort Simulated Annealing Algorithm .148
5.5.2 Experimental Analysis 148
5.6 Conclusions .157
References 158
6 Isoarchic and Multi-criteria Control of Supply Chain Network
F. Ounnar and P. Pujo 161
6.1 Introduction .161
6.2 Supply Chain Management Limits .163
6.3 Control of a Dynamic Logistic Network: Isoarchic and Multi-criteria
Control .164
6.3.1 Description of the Interacting Entities .165
6.3.2 Definition of Self-organized Control .166
6.3.3 Support Structure: Autonomous Control Entity 171
6.4 Distributed Simulation of a Dynamic Logistic Network 174
6.4.1 High-level Architecture Components 174
6.4.2 Integration of the DEVS-ACE Models in High-level Architecture
Environment 175
6.5 Experiments via Simulation 175
6.6 Conclusion and Future Work 178
References 179
7 Supply Chain Management Under Uncertainties:
Lot-sizing and Scheduling Rules
A. Dolgui, F. Grimaud and K. Shchamialiova 181
7.1 Introduction .181
7.2 Short Presentation of Lot-sizing and Scheduling Problems 184
7.2.1 Basic Models and Extensions 187xviii Contents
7.2.2 Lot-sizing and Scheduling Under Uncertainties 191
7.2.3 Optimization Techniques 196
7.3 A Case Study 197
7.3.1 Description of the Case Study . 197
7.3.2 Representation of Uncertainties 200
7.3.3 Objective Function 202
7.3.4 Decomposition for Optimization . 203
7.3.5 Numerical Example . 210
7.3.6 Numerical Results . 214
7.4 Conclusions 216
References . 217
8 Meta-heuristics for Real-time Routing Selection in Flexible
Manufacturing Systems
M. Souier, A. Hassam and Z. Sari 221
8.1 Introduction 222
8.2 Literature Review . 223
8.3 Job-shop 225
8.3.1 Job-shop Problem 225
8.3.2 Simulation of a Flexible Manufacturing System Environment . 226
8.4 Dissimilarity Maximization Method and Modified DMM Rules . 228
8.4.1 Dissimilarity Maximization Method for Real-time Routing
Selection 228
8.4.2 Modified Dissimilarity Maximization Method for Real-time
Routing Selection 229
8.5 Meta-heuristics for Job-shop Routing . 230
8.5.1 Ant Colony Optimization 230
8.5.2 Simulated Annealing . 231
8.5.3 Particle Swarms Optimization . 232
8.5.4 Genetic Algorithms . 234
8.5.5 Taboo Search . 235
8.5.6 Electromagnetism-like Method . 236
8.6 Performance Evaluation of Routing Selection Methods . 237
8.6.1 System Simulation Without Presence of Breakdown 237
8.6.2 System Simulation With Presence of Breakdown . 241
8.7 Conclusions 246
References . 247
9 Meta-heuristic Approaches for Multi-objective Simulation-based
Optimization in Supply Chain Inventory Management
D. Sánchez, L. Amodeo and C. Prins 249
9.1 Introduction 249
9.2 Literature Review . 251
9.3 Problem Formulation 253Contents xix
9.4 Implementation of Selected Evolutionary Algorithms 256
9.4.1 Non-dominated Sorting Genetic Algorithm II .256
9.4.2 Strength Pareto Evolutionary Algorithm II 258
9.4.3 Strength Pareto Evolutionary Algorithm IIb 259
9.4.4 Multi-objective Particle Swarm Optimization .260
9.5 Computational Experiments and Analysis 262
9.5.1 Evaluation Criteria 262
9.5.2 Parameters Used 263
9.5.3 Experimental Results .263
9.6 Conclusion 267
References 268
10 Diverse Risk/Cost Balancing Strategies for Flexible Tool
Management in a Supply Network
D. D’Addona and R. Teti 271
10.1 Introduction .271
10.2 Multi-agent Tool Management System 272
10.3 Flexible Tool Management Strategies 275
10.3.1 Current Inventory-Level Decision Making (INVADAPT) .277
10.3.2 Fixed-Horizon Inventory-Level Decision Making (I-FUTURE) 279
10.3.3 Variable-Horizon Inventory-Level Decision Making
(INVADAPT_NS) .280
10.4 Tool Inventory Management Simulations 281
10.4.1 Simulation Through INVADAPT .287
10.4.2 Simulation Through I-FUTURE .294
10.4.3 Simulation Through INVADAPT_NS 301
10.5 Test Case Applications .309
10.6 Conclusions and Future Work 312
References 312
11 Intelligent Integrated Maintenance Policies for Manufacturing Systems
N. Rezg and S. Dellagi 315
11.1 Introduction .315
11.2 Optimal Maintenance Policy Considering the Influence of the Production
Plan on the Deterioration of the Manufacturing System 318
11.2.1 Problem Statement 318
11.2.2 Problem Formulation .319
11.2.3 Influence of Manufacturing System Deterioration on the Optimal
Production Plan .324
11.2.4 Optimization of the Maintenance Policy .326
11.3 Intelligent Periodic Preventive Maintenance Policy in Finite Horizon
with an Adaptive Failure Law 330
11.3.1 Problem Description 330xx Contents
11.3.2 Model Formulation and Intelligent Determination of the Optimal
Solution . 332
11.3.3 Numerical Example . 335
11.4 Conclusions 338
References . 339
12 Enhancing the Effectiveness of Multi-pass Scheduling Through
Optimization via Simulation
T. Yoo, H.-B. Cho and E. Yücesan 341
12.1 Introduction 342
12.2 Multi-pass Scheduling Using Nested Partitions and Optimal Computing
Budget Allocation . 346
12.2.1 The Proposed Multi-pass Scheduling Framework . 346
12.2.2 The Outer Loop: Nested Partitions 346
12.2.3 The Inner Loop: Optimal Computing Budget Allocation . 348
12.3 Implementation of Nested Partitions and Optimal Computing Budget
Allocation 349
12.3.1 Nested Partitions: Partitioning Strategy 349
12.3.2 Nested Partitions: Sampling Strategy 351
12.3.3 Nested Partitions: Backtracking Strategy 351
12.3.4 Optimal Computing Budget Allocation: Ranking and Selection
Strategy . 352
12.3.5 Performance of Nested Partitions and Optimal Computing Budget
Allocation 353
12.4 Experimental Design and Analysis . 354
12.4.1 Experimental Assumptions 354
12.4.2 Experimental Design . 356
12.4.3 Experimental Results for the Probability of Correct Selection 357
12.5 Conclusions 363
References . 364
13 Intelligent Techniques for Safety Stock Optimization in Networked
Manufacturing Systems
B. Desmet, E.-H. Aghezzaf and H. Vanmaele . 367
13.1 Introduction 367
13.2 Multi-echelon Inventory Control in Networked Manufacturing
Systems 369
13.3 Literature Review . 374
13.4 System Safety Stock Optimization in n-echelon Distribution Systems 377
13.4.1 Introduction of a Two-echelon Distribution System . 378
13.4.2 Characterization of the Back-order Service Time in the Central
Warehouse . 380
13.4.3 Characterization of the Actual Retailer Replenishment
Lead Time . 382Contents xxi
13.4.4 System Safety Stock Optimization in a Two-echelon Distribution
System .384
13.5 System Safety Stock Optimization in n-echelon Assembly Systems 386
13.5.1 Introduction of a Two-echelon Assembly System .386
13.5.2 Characterization of the Backorder Service Time for a Subset of
Components .389
13.5.3 Characterization of the Incoming Service Time to the Assembly .390
13.5.4 Characterization of the Actual Assembly Lead Time .391
13.5.5 System Safety Stock Optimization in a two-echelon Assembly
System .392
13.5.6 Distribution System as Special Case of the Assembly System 394
13.6 System Safety Stock Optimization in Generic Networks .395
13.6.1 Introduction of a Spanning Tree System .395
13.6.2 Characterization of Actual Replenishment Lead Times in a
Spanning Tree System .402
13.6.3 System Safety Stock Optimization for a Spanning Tree System .415
13.6.4 Extension to Generic Systems .416
13.7 Conclusions .418
References 419
14 Real-world Service Interaction with Enterprise Systems in Dynamic
Manufacturing Environments
S. Karnouskos, D. Savio, P. Spiess, D. Guinard, V. Trifa and O. Baecker .423
14.1 Motivation 424
14.2 Real-world Awareness .426
14.2.1 Device Integration Protocols 426
14.2.2 Device-to-Business Coupling .428
14.2.3 Integrating Heterogeneous Devices 429
14.3 Enterprise Integration 430
14.4 Integrating Manufacturing Equipment with the SOCRADES Integration
Architecture .432
14.5 Towards Dynamic Adaptation .435
14.5.1 Simulation 437
14.5.2 Self-healing Mechanisms .438
14.5.3 Self-optimizing Mechanisms 439
14.6 Concept Validation in Prototypes 440
14.6.1 Machine Monitoring, Dynamic Decision and Order Adaptation 441
14.6.2 The Future Shop Floor: Mashup of Heterogeneous Service-orientedarchitecture Devices and Services .444
14.6.3 Dynamic Supply Chain Management Adaptation 446
14.6.4 Taming Protocol Heterogeneity for Enterprise Services 449
14.6.5 Energy Monitoring and Control via Representational
State Transfer 451
14.7 Discussion and Future Directions 454xxii Contents
14.8 Conclusions and Future Work . 455
References . 455
15 Factory of the Future: A Service-oriented System of Modular, Dynamic
Reconfigurable and Collaborative Systems
A.-W. Colombo, S. Karnouskos and J.-M. Mendes . 459
15.1 Introduction 460
15.2 The Emergence of Cooperating Objects . 461
15.3 The Cross-layer Service-oriented-architecture-driven Shop Floor . 463
15.4 Dynamic Reconfiguration of a Service-oriented-architecture-based
Collaborative Shop Floor . 465
15.4.1 Methodology . 466
15.4.2 Example . 468
15.5 Analysis Behind the Engineering Methods and Tools 469
15.5.1 Applying Functional Analysis to Validate Service Composition
Paths in High-level Petri-net-based Orchestration Models . 469
15.5.2 Example . 472
15.6 A Service-oriented-architecture-based Collaborative Production
Management and Control System Engineering Application 474
15.7 Conclusions and Future Work 479
References . 480
16 A Service-oriented Shop Floor to Support Collaboration in
Manufacturing Networks
J. Barata, L. Ribeiro and A.-W. Colombo . 483
16.1 Introduction 483
16.2 Agility in Manufacturing 485
16.3 Collaborative Networks 487
16.4 Service-oriented-architecture as a Method to Support Agility and
Collaboration . 488
16.5 Architecture 493
16.5.1 The Role of Componentization . 493
16.5.2 Service Exposure, Composition and Aggregation . 495
16.5.3 The Role of Orchestration in Control, Monitoring and Diagnosis 496
16.6 An Implementation . 498
16.6.1 Manufacturing Device Service Implementation 498
16.6.2 Coalition Leader Service Implementation . 498
16.6.3 Application Example: a Collaborative Pick-and-place Operation. 499
16.7 Conclusions 501
References . 501
Index.……………………………………………………………………………505
Index
A
Agent-based system, 41, 43, 126
Ant colony optimization, 221
Artificial intelligence, 1, 11, 13, 14, 21, 22,
43−45, 81, 83−85, 126, 131, 484
Assembly system, 196, 218−220, 367,
374−377, 386, 388, 389,
393−395, 403, 418, 419, 477,
479, 502
Availability, 114, 132, 139, 141, 145, 148−
152, 156, 157, 219, 222, 284,
285, 297, 315, 316, 318, 335−
339, 433, 490
B
Budget allocation, 341, 345, 354, 364
Business processes, 52, 58, 63, 83, 423,
425, 426, 430, 431, 439
C
Capacitated lot-sizing problem, 190
Collaboration, 90, 107, 108, 113, 114, 116,
129, 130, 133, 136, 272, 459−
463, 466, 469, 475, 477, 479,
483, 484, 488, 494, 501
Collaborative system, 459, 463, 479
D
Decision-making, 2, 13, 58, 73, 75, 88, 90,
91, 104, 107, 110−112,
119−121, 125, 133, 161, 162,
164−166, 169, 171, 182, 221,
223, 247, 341, 342, 356, 363
Device integration, 427, 455, 464
Device profile for web services (DPWS),
425, 427, 428, 432, 448, 451,
454, 478, 484, 490, 501, 502
Distributed simulation, 89, 126, 161, 162,
174
Distributed system, 126
Distribution system, 90, 367, 368, 375,
377−380, 385, 386, 392, 394,
395, 418−420
Dynamic manufacturing, 426
Dynamic reconfigurable system, 459
E
e-collaboration, 129, 133
Economic lot scheduling problem, 189
Economic order quantity, 187
Economic production quantity, 188
Electromagnetic meta-heuristic, 221
Energy monitoring, 451
Enterprise integration, 158, 423, 430
Enterprise resource planning (ERP), 108,
426, 464, 466, 477, 478
Enterprise system, 424−426, 429, 436,
444, 449, 454, 456, 459, 463,
466, 477, 479, 481
Evolutionary algorithm, 222, 249, 250,
252, 256, 264, 269
Expert system, 15, 16, 37, 44, 58, 73, 342,
343506 Index
F
Failure rate, 315, 318, 319, 323−325, 327,
339
Flexible manufacturing system, 1, 132,
159, 221, 222, 226, 248, 364,
365, 485
Flow-shop, 182, 183, 185−187, 191, 198,
217, 218, 340
Fuzzy logic, 14, 19, 20, 38, 365
G
Generic networked manufacturing system,
367
Genetic algorithm, 14, 18, 19, 37, 39, ,
111, 125, 158, 181, 184, 197,
205, 207, 216, 217, 219, 220,
223, 225, 234, 246, 252, 253,
256, 268, 269, 342−344, 365
Graph, 344, 370, 372, 392, 395, 417, 441,
453
H
Holonic, 1, 33, 36, 40, 161, 162, 164, 165,
172, 178, 180
I
Intelligent agent, 45, 46, 83−85, 120, 271,
272, 274, 312,
Internet of things, 423, 424, 428, 440, 456,
462
Inventory control, 92, 94, 96, 97, 218, 277,
312, 367, 368, 370, 371, 373,
374, 419
Isoarchic control model, 161
J
Job-shop, 183, 185−187, 223−226, 246,
247
K
Knowledge acquisition, 53
Knowledge management, 61, 127, 465
L
Lead time, 191−194, 201, 217−219
Lot-sizing, 181−184, 187, 191, 195, 196,
197, 217, 218, 220, 373, 374,
419, 420
M
Machine learning, 43, 57, 61, 81−83, 85
Maintenance, 315−319, 322, 324, 326,
329− 332, 334−340
Manufacturing network, 91, 434, 483, 484,
488, 489, 492
Manufacturing system, 1−3, 6−8, 10, 11,
13, 14, 22−27, 29, 33, 36−41,
44, 165, 180, 182, 183, 191,
251, 316, 318, 319, 338−340,
344, 485, 502
Material requirement planning, 192
Meta-heuristic, 221−226, 230, 236, 237,
239−243, 245−247, 249
Middleware, 424, 426, 432, 433, 446, 502
Multi-agent, 45, 52, 83, 84, 87−91, 103,
105, 107, 111, 116, 126, 129,
130, 132, 133, 158, 179, 271,
272, 273, 313, 315, 491, 501
Multi-criteria, 161, 162, 164, 166,
168−170, 269
Multi-objective optimization, 249
Multi-pass scheduling, 341−343, 345−349,
351, 354, 356−358, 363, 364
N
Nested partitions, 345, 365
Networked enterprise, 43, 177
Networked manufacturing enterprise, 87
Networked manufacturing system, 367,
368, 370
Neural network, 75, 342, 344, 365, 367
O
OPC-UA, 425, 450
Open-shop, 185, 187
Optimization, 126, 148, 162, 164, 166,
168, 172, 179, 180, 181, 189,
196, 200, 204, 205, 215, 217,
218, 219, 221, 222, 224, 231−Index 507
233, 247−254, 256, 262, 268,
269, 316, 318, 326, 329, 339,
341, 345, 364, 365, 367, 368,
372, 374−377, 385, 386, 393,
405, 415, 417−420, 422, 423,
434, 436, 439, 446, 454, 493
Outsourcing, 129, 130−132, 134, 138, 142,
145, 148, 156−158, 419
P
Parallel machines, 185
Planning horizon, 184, 185, 187, 189, 190,
197−199, 374
Production control, 158, 315, 316, 465,
476, 479
Production system, 184, 185, 193, 216,
220, 222−224, 227, 229, 237,
246, 316, 420, 459, 463, 465,
470, 474, 478, 480, 481, 486,
501
R
Random yield, 183, 194, 195, 201, 217,
219
Real-time routing, 221, 228, 229
Real-time scheduling, 222−224, 341, 342,
365
Reliability, 169, 318, 319, 332, 338, 339,
433, 455
Renewal process, 193, 201, 218
Representational state transfer (REST),
425, 427, 450, 451, 453, 456,
457
Radio frequency identification (RFID),
432, 444, 446, 456
S
Safety stock, 367, 375, 418, 419
SAP, 423, 424, 432, 433, 439, 441−443,
445, 446, 448, 449, 459, 461,
478
Scheduling, 51, 83, 91, 94, 105, 112, 127,
129, 130−132, 138, 139, 145,
148, 157−159, 166, 179, 181,
184, 221−226, 246−248, 339,
341−345, 348, 349, 356,
363−365, 423, 446
Sequencing, 132, 158, 181−184, 197, 216−
218, 220, 223, 225, 344, 365
Service interaction, 423
Service-oriented architecture, 424, 428,
444, 459, 461, 463−465,
470−473, 477, 479−481, 489,
501
Service-oriented device, 503
Service-oriented ecosystem, 490
Service-oriented infrastructure, 484
Service-oriented modelling, 491
Service-oriented shop floor, 483
Service-oriented paradigm, 456
Simulated annealing, 148, 157−159, 223,
225, 231, 246, 247, 252, 342,
344, 364
Simulation, 87−97, 99, 101−105, 110−112,
126, 127, 149−151, 161, 174−
177, 179−181, 184, 214, 217,
226, 246−254, 263, 268, 271,
272, 281, 283, 286−288, 295,
299, 301, 302, 307, 312, 314,
339, 341, 342, 344−349,
352−357, 359, 363−365, 369,
374, 376, 383−386, 391, 393,
394, 402, 415−418, 421, 423,
426, 435−438, 446, 455, 456
Single machine, 183, 185, 197, 201, 219
SOCRADES, 424, 430, 455, 463, 471,
473, 475, 477−480, 490, 503
Supply chain, 88−95, 99, 100, 104, 105,
107−115, 117, 119, 122, 125−
127, 132, 158, 161−163,
179−182, 218, 226, 250−254,
256, 260, 268, 269, 312, 313,
372, 374, 375, 398, 400−415,
418−420, 422, 423, 426, 434,
480, 485−488, 501, 502
Supply network, 271−273, 275, 313
Swarm optimization, 256
T
Taboo, 221, 222, 235
Tool management, 223, 271, 272, 275,
312, 313508 Index
U
Uncertainty, 107, 111, 114, 130, 181, 191−
196, 218−220, 370, 373, 420,
485
W
Wagner–Whitin, 189
Web-oriented querying interface, 429
Web-service, 129, 130, 133, 462, 475, 478, 481
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