상세정보

Preface. Part 1: General Issues. 1. Artificial Intelligence in Industrial Decision Making, Control and Automation: an Introduction; S.G. Tzafestas, H. Verbruggen. 2. Conceptual integration of Qualitative and Quantitative Process Models; E.A. Woods. 3. Timing Problems and their Handling at System Integration; L. Motus. 4. Analysis for Correct Reasoning in Interactive Man Robot Systems: Disjunctive Syllogism with Modus ponens and Modus tollens; E.C. Koenig. Part 2: Intelligent Systems. 5. Applied Intelligent Control Systems: R. Shoureshi, M. Wheeler, L. Brackney. 6. Intelligent Simulation in Designing Complex Dynamic Control Systems; F. Zhao. 7. Multiresolutional Architectures for Autonomous Systems with Incomplete and Indequate Knowledge Representation; A. Meysel. 8. Distributed Intelligent Systems in Cellular Robotics; T. Fuikuda, T. Ueyama, K. Sekiyama. 9. Distributed Artificial Intelligence in Manufacturing Control; S. Albayrak, H. Krallmann. Part 3: Neural Networks in Modelling, Control and Scheduling. 10. Artificial Neural Networks for Modelling; A.J. Krijgsman, H.B. Verbruggen, P.M. Bruijn. 11. Neural Networks in Robot Control; S.G. Tzafestas. 12. Control Strategy of Robotic Manipulator Based on Flexible Neural Network Structure; M. Teshnehlab, K. Watanabe. 13. Neuro-Fuzzy Approaches to Anticipatory Control; L.H. Tsoukalas, A. Ikonomopoulos, R.E. Uhrig. 14. New Approaches to Large-Scale Scheduling Problems: Constraint Directed Programming and Neural Networks; Y. Kobayashi, N. Nonaka. Part 4: Systems Diagnostics. 15. Knowledge-Based Fault Diagnosis of Technological Systems; H.B. Verbruggen, S.G. Tzafestas, E. Zanni. 16. Model-Based Diagnosis: State Transition Events and Constraint Equations; K.-E. Arzen, A. Wallen, T.F. Petti. 17. Diagnosis with Explicit Models of Goals and Functions; J.E. Larsson. Part 5: Industrial Robotic, Manufacturing and Organizational Systems. 18. Multi-Sensor Integration for Mobile Robot Navigation; A. Traca de Almeida, H. Araujo, J. Dias, U. Nunes. 19. Incremental Design of a Flexible Robotic Assembly Cell Using Reactive Robots; E.s. Tzafestas, S.G. Tzafestas. 20. On the Comparison of AI and DAI Based Planning Techniques for Automated Manufacturing Systems; A.I. Kokkinaki, K.P. Valavanis. 21. Knowledge-Based Supervision of Flexible Manufacturing Systems; A.K.A. Toguyeni, E. Craye, J.-C. Gentina. 22. A Survey of Knowledge-Based Industrial Scheduling; K.S. Hindi, M.g. Singh. 23. Reactive Batch Scheduling; V.J. Terpstra, H.B. Verbruggen. 24. Applying Groupware Technologies to Support Management in Organizations; A. Michailidis, P.-I. Gouma, R. Rada. Index.

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CONTENTS
Preface = xxv
Contributors = xxvii
PART ⅠGENERAL ISSUES
 CHAPTER 1 ARTIFICIAL INTELLIGENCE IN INDUSTRIAL DECISION MAKING, CONTROL AND AUTOMATION : AN INTRODUCTION / S. Tzafestas and H. Verbruggen
  1. Introduction = 1
  2. Decision Making, Control and Automation = 2
   2.1. Decision Making Theory = 2
   2.2. Control and Automation = 4
  3. Artificial Intelligence Methodologies = 6
   3.1. Reasoning under uncertainty = 7
   3.2. Qualitative reasoning = 14
   3.3. Neural nets reasoning = 16
  4. Artificial Intelligence in Decision Making = 19
  5. Artificial Intelligence in Control and Supervision = 22
  6. Artificial Intelligence in Engineering Fault Diagnosis = 24
  7. Artificial Intelligence in Robotic and Manufacturing Systems = 26
  8. Conclusions = 30
  References = 31
 CHAPTER 2 CONCEPTUAL INTEGRATION OF QUALITATIVE AND QUALITATIVE PROCESS MODELS / E. A. Woods
  1. Introduction = 41
  2. Qualitative Reasoning = 42
   2.1. Common Concepts = 43
   2.2. Qualitative Mathematics = 44
   2.3. The notion of state = 45
   2.4. Describing Behaviour = 45
   2.5. Components of qualitative reasoning = 45
   2.6. Towards more quantitative models = 47
  3. Formal Concepts and Relations in the HPT = 48
   3.1. Quantities = 48
   3.2. Physical Objects, process equipment, materials and substances = 48
   3.3. The input file = 49
   3.4. Activity conditions = 49
   3.5. Numerical functions and influences = 50
   3.6. Logical relations and rules = 52
  4. Defining Views and Phenomena = 52
   4.1. Individuals and individual conditions = 52
   4.2. Quality conditions and preconditions = 54
   4.3. Relations = 56
   4.4. Dynamic influences = 56
   4.5. Instantiating a definition = 57
   4.6. Activity levels = 57
  5. Deriving and Reasoning with an HPT Model = 59
   5.1. Extending the topological model = 59
   5.2. Deriving the phenomenological model = 60
   5.3. Activity and state space models = 61
  6. Discussion and Conclusion = 63
  References = 64
 CHAPTER 3 TIMING PROBLEMS AND THEIR HANDLING SYSTEM INTEGRATION / L. Motus
  1. Introdiction = 67
  2. Essential Features of Control Systems = 68
   2.1. Essential (forced) concurrency = 70
   2.2. Truly asynchronous mode of execution of interacting processes = 70
   2.3. Time - selective interprocess communication = 71
  3. Concerning Time - Correct Functioning of Systems = 71
   3.1. Performance - bound properties = 72
   3.2. Timewise correctness of events and data = 72
   3.3. Time correctness of interprocess communication = 73
  4. A Mathematical Model for Quantitative Timing Analysis (Q - Model) = 73
   4.1. Paradigms used = 74
   4.2. The Q - model = 74
  5. The Q - Model Based Analytical Study of System Properties = 76
   5.1. Separate elements of a specification = 76
   5.2. Pairs of interacting processes = 77
   5.3. Group of interacting processes = 78
  6. An example of the Q - Model Application = 79
  7. Conclusions = 85
  References = 85
 CHAPTER 4 ANALYSIS FOR CORRECT REASONING IN INTERACTIVE MAN ROBOT SYSTEMS : DISJUNCTIVE SYLLOGISM WITH MODUS PONES AND MODUS TOLLENS / E. C. Koenig
  1. Introduction = 89
  2. Valid Command Arguments = 90
  3. Correct Reasoning : Disjunctive Syllogism = 91
   3.1. Plausible composite command arguments = 92
   3.2. Plausible composite commands = 92
  4. Conclusions = 96
  References = 96
PART 2 INTELLIGENT SYSTEMS
 CHAPTER 5 APPLIED INTELLIGENT CONTROL SYSTEMS / R. Shoureshi ; M. Wheeler ; L. Brackney
  1. Introduction = 101
  2. A Proposed Structure for Intelligent Control Systems (ICS) = 102
  3. Intelligent Automatic Generation Control (IAGC) = 105
  4. Intelligent Comfort Control System = 110
  5. Control System Development = 111
  6. Experimental Results = 116
  7. Conclusion = 116
  References = 119
 CHAPTER 6 INTELLIGENT SIMULATION IN DESIGNING COMPLEX DYNAMIC CONTROL SYSTEMS / F. Zhao
  1. Introduction = 127
  2. The Control Engineer's Workbench = 128
  3. Automatic Control Synthesis in Phase Space = 128
   3.1. Overview of the phase space navigator = 129
   3.2. Intelligent navigation in phase space = 129
   3.3. Planning control paths with flow pips = 130
  4. The Phase Space Navigator = 131
   4.1. Reference trajectory generation = 131
   4.2. Reference trajectory tracking = 133
   4.3. The autonomous control synthesis algorithms = 135
   4.4. Discussion of the synthesis algorithms = 137
  5. An illustration : Stabilizing a Buckling Column = 139
   5.1. The column model = 140
   5.2. Extracting and representing qualitative phase - space structure of the buckling column = 141
   5.3. Synthesizing control laws for stabilizing the column = 143
   5.4. The phase - space modeling makes the global navigation possible = 148
  6. An application : Maglev Controller Design = 148
   6.1. The maglev model = 148
   6.2. Phase - space control trajectory design = 150
  7. Discussion = 155
  8. Conclusions = 155
  References = 156
 CHAPTER 7 MULTIRESOLUTIONAL ARCHITECTURES FOR AUTONOMOUS SYSTEMS WITH INCOMPLETE AND INADEQUATE KNOWLEDGE REPRESENTATION / A. Meystel
  1. Introduction = 159
  2. Architectures for Intelligent Control Systems : Terminology, Issues, and a Conceptual Framework = 161
   2.1. Definitions = 161
   2.2. Issues and problems = 165
   2.3. Conceptual framework for intelligent systems architecture = 170
  3. Overview of the General Results = 171
  4. Evolution of the Multiresolutional Control Architecture (MCA) : Its Active and Reactive Components = 173
   4.1. General structure of the controller = 173
   4.2. Multiresolutional control architecture (MCA) = 175
  5. Nested Control Strategy : Generation of a Nested Hierarchy for MCA = 177
   5.1. GFACS triplet : Generation of intelligent behavior = 177
   5.2. Off - line decision making procedures of planning - control on MCA = 178
   5.3. Generalised controller = 180
   5.4. Universe of the trajectory generator : Second level = 181
   5.5. Representation of the planning / control problem in MCA = 183
   5.6. Search as the general control strategy for MCA = 185
  6. Elements of the Theory of Nested Mulitiresolutional Control for MCA = 187
   6.1. Commutative diagram for a nested multiresolutional controller = 187
   6.2. Tessellated knowledge bases = 187
   6.3. Generalization = 188
   6.4. Attention and consecutive refinement = 189
   6.5. Accuracy and resolution of representation = 190
   6.6. Complexity and tessellation : ε - entropy = 194
  7. MCA in Autonomous Control System = 195
   7.1. The multiresolutional generalization of system models = 195
   7.2. Perception stratified by resolution = 196
   7.3. Maps of the world stratified by resolution = 197
  8. Development of Algorithms for MCA = 198
   8.1. Extensions of the Bellman's optimality principle = 198
   8.2. Nested Multiresolutional search in the state space = 198
  9. Complexity of Knowledge Representation and Manipulation = 201
   9.1. Multiresolutional consecutive refinement : Search in the state space = 201
   9.2. Multiresolutional consecutive refinement : Multiresolutional search of a trajectory in the state space = 203
   9.3. Evaluation and minimization of the complexity of the MCA = 205
  10. Case Studies = 208
   10.1. A pilot for an autonomous robot (two levels of resolution) = 208
   10.2. PILOT with two agents for control (a case of behavioral duality) = 211
  11. Conclusion = 219
  References = 220
 CHAPTER 8 DISTRIBUTED INTELLIGENT SYSTEMS IN CELLULAR ROBOTICS / R. Fukuda, T. Ueyama ; K. Sekiyama
  1. Introduction = 225
  2. Concept of Cellular Robotic System = 226
  3. Prototypes of CEBOT = 227
   3.1. Prototype CEBOT Mark Ⅳ = 229
   3.2. Cellular Manipulator = 231
  4. Distributed Genetic Algorithm = 234
   4.1. Distributed Decision Making = 234
   4.2. Structure configuration problem = 235
   4.3. Application of genetic algorithm = 236
   4.4. Distributed genetic algorithm = 239
   4.5. Simulation results = 241
  5. Conclusions = 245
  References = 245
 CHAPTER 9 DISTRIBUTED ARTIFICIAL INTELLIGENCE IN MANUFACTURING CONTROL / S. Albayrak ; H. Krallmann
  1. Introduction = 247
  2. Tasks of Manufacturing Control = 248
  3. The State - of - the - Art of the DAI Technique in Manufacturing Control = 252
   3.1. ISIS / OPIS = 252
   3.2. SOJA / SONIA = 254
   3.3. YAMS = 255
  4. Distributed Artificial Intelligence = 259
   4.1. Cooperative problem solving = 261
   4.2. Phases of cooperating problem solving = 261
   4.3. Blackboard metaphor, model and frameworks = 264
   4.4. History of the blackboard model = 274
   4.5. Advantages of DAI = 276
  5. VerFlex - BB System : Approach and Implementation = 277
   5.1. Distributed approach to the solution of the task order execution = 277
   5.2. Why was the blackboard model used? = 281
   5.3. The VerFlex - BB system = 281
   References = 292
PART 3 NEURAL NETWORKS IN MODELLING, CONTROL AND SCHEDULING
 CHAPTER 10 ARTIFICIAL NEURAL NETWORKS FOR MODELLING / A. J. Krijgsman ; H. B. Verbruggen ; P. M. Bruijin
  1. Introduction = 297
  2. Description of artificial neurons = 298
  3. Artificial neural networks (ANN) = 299
  4. Nonlinear models and ANN = 300
  5. Networks = 302
   5.1. Multilayered static neural networks = 302
   5.2. Radial basis function networks = 303
   5.3. Cerebellum model articulation controller (CMAC) = 304
  6. Identification of Dynamic Systems Using ANN = 306
   6.1. Identification problem definition = 306
   6.2. Model description for identification = 308
  7. Hybrid Modelling = 308
   Orthogonal least - squares algorithm = 309
  8. Model Validation = 313
  9. Experiments and Results Using Neural Identification = 314
  10. Conclusions = 323
  References = 323
 CHAPTER 11 NEURAL NETWORKS IN ROBOT CONTROL / S. G. Tzafestas
  1. Introduction = 327
  2. Neurocontrol Architectures = 328
   2.1. General issues = 328
   2.2. Unsupervised NN control architectures = 329
   2.3. DIMA Ⅱ. Neurocontroller for linear systems = 331
   2.4. Adaptive learning neurocontrol for CARMA systems = 336
  3. Robot Neurocontrol = 339
   3.1. A look at robotics = 339
   3.2. Neural nets in robotics : General review = 341
   3.3. Robot control using hierarchical NNs = 343
   3.4. Minimum torque - change robot neurocontrol = 346
   3.5. Improved iterative learning robot neurocontroller = 349
  4. Numerical Examples = 352
   4.1. Example 1 : DIMA Ⅱ controller for linear systems = 352
   4.2. Example 2 : Neurocontroller for CARMA systems = 354
   4.3. Example 3 : Supervised neurocontrol of a broom - balancing system = 357
   4.4. Example 4 : Feedback - error learning robot neurocontrol = 361
   4.5. Example 5 : Iterative robot nuerocontrol = 366
   4.6. Example 6 : Unsupervised robot - neurocontroller using hierarchical NN = 372
  5. Conclusions and Discussion = 375
  6. Appendix : A Bridt Look at Neural Networks = 376
   6.1. Single - layer perceptron (SLP) = 377
   6.2. Multi - layer perceptron (MLP) = 378
   6.3. Hopfield network = 381
   References = 384
 CHAPTER 12 CONTROL STRATEGY OF ROBOTIC MANIPULATOR BASED ON FLEXIBLE NEURAL NETWORKS STRUCTURE / M. Teshnehalb ; K. Watanabe
  1. Introduction = 389
  2. The Representation of Bipolar Unit Function = 390
  3. Learning Architecture = 391
   3.1. The learning of connection weights = 392
   3.2. The learning of sigmoid unit function parameters = 393
  4. Neural Network - Bases Adaptive Controller = 394
   4.1. The feedback - error learning rule = 396
   4.2. Adaptation of neural network controller = 397
  5. Simulation Example = 402
  6. Conclusion = 402
  References = 402
 CHAPTER 13 NEURO - FUZZY APPROACHES TO ANTICIPATORY CONTROL / L. H. Tsoukalas ; A. Ikonomopoulos ; R. E. Uhrig
  1. Introduction = 405
  2. Issues of Formalism Anticipatory Systems = 407
  3. Issues of Measurement and Prediction = 412
  4. Conclusions = 417
  References = 418
 CHAPTER 14 NEW APPROACHES TO LAGER - SCALE SCHEDULING PROBLEMS : CONSTRAINT DIRECTED PROGRAMMING AND NEURAL NETWORKS / Y. Kobayashi ; H. Nonaka
  1. Introduction = 421
  2. Method = 422
   2.1. Problem and method description = 422
   2.2. Knowledge - based method for lower - level problems = 424
   2.3. Knowledge - based scheduling method for upper - level problems = 431
   2.4. Neural networks for upper - level problems = 432
  3. Application Examples = 439
   3.1. Scheduling systems = 439
   3.2. Problem = 439
   3.3. Results = 439
  4. Conclusions = 444
  References = 445
PART 4 SYSTEM DIAGNOSTICS
 CHAPTER 15 KNOWLEDGE - BASED FAULT DIAGNOSIS OF TECHNOLOGICAL SYSTEMS / H. Verbruggen ; S. Tzafestas ; E. Zanni
  1. Introduction = 449
  2. Knowledge Representation and Acquisition for Fault Diagnosis = 451
   2.1. Knowledge representation = 451
   2.2. Knowledge acquisition = 454
  3. First - and Second - Generation Diagnostic Expert Systems = 456
   3.1. General issues = 456
   3.2. First - generation expert systems = 456
   3.3. Deep reasoning = 457
   3.4. Qualitative reasoning = 458
   3.5. Second - generation expert systems = 462
  4. A General Look at the FD Methodologies and Second - Generation ES Architectures = 462
   4.1. General issues = 462
   4.2. Diagnostic modelling = 463
   4.3. Second - generation FD expert system architectures = 464
  5. A Survey of Digital Systems Diagnostic Tools = 467
   5.1. The D - algorithm = 467
   5.2. Davis' diagnostic methodology = 468
   5.3. Integrated diagnostic model (IDM) = 470
   5.4. The diagnostic assistance reference tool (DART) = 472
   5.5. The intelligent diagnostic tool (IDT) = 474
   5.6. The Lockheed expert system (LES) = 476
   5.7. Other systems = 476
  6. A General Methodology for the Development of FD Tools in the Digital Circuits Domain = 477
   6.1. Description of the structure = 478
   6.2. Description of the behaviour = 479
   6.3. The diagnostic mechanism = 480
   6.4. The constraint suspension technique = 482
   6.5. Advantages of the deviation detection and constraint suspension technique = 485
  7. A General Methodology for the Development of FD Tools in the Process Engineering Domain = 486
  8. Implementation of a Digital Circuits Diagnostic Expert System (DICIDEX) = 489
   8.1. Introduction = 489
   8.2. Dicidex description = 490
   8.3. Examples of system - user dialogues = 496
  9. Conclusions = 501
  References = 502
 CHAPTER 16 MODEL - BASED DIAGNOSIS : STATE TRANSITION EVENTS AND CONSTRAINT EQUATIONS / K. - E. Arzen ; A. Wallen ; T. F. Petti
  1. Introduction = 507
  2. Diagnostic Model Processer Method (DMP) = 509
  3. Model Integrated Diagnosis Analysis System (MIDAS) = 512
   3.1. MIDAS models = 512
   3.2. MIDAS diagnosis = 515
  4. Steritherm Diagnosis = 518
   4.1. DMP Steritherm diagnosis = 518
   4.2. MIDAS Steritherm diagnosis = 519
  5. Comparisons = 520
  6. Conclusions = 522
  References = 523
 CHAPTER 17 DIAGNOSIS WITH EXPLICIT MODELS OF GOALS AND FUNCTIONS / J. E. Larsson
  1. Introduction = 525
  2. Basic Idaes in Multilevel Flow Modeling (MFM) = 526
  3. An Example of a Flow Model = 526
  4. Three Diagnostic Methods = 528
   4.1. Measurement validation = 529
   4.2. Alarm analysis = 530
   4.2. Fault Diagnosis = 531
  5. Implementation = 531
  6. Complex Systems = 532
  7. Conclusions = 532
  References = 533
PART 5 INDUSTRIAL ROBOTIC, MANUFACTURING AND ORGANIZATIONAL SYSTEMS
 CHAPTER 18 MULTI - SENSOR INTEGRATION FOR MOBILE ROBOT NAVIGATION / A. Traca de Almeida ; H. Araujo ; J. Dias ; U. Nunes
  1. Introduction = 537
  2. Sensor - Based Navigation = 537
  3. Sensor System = 538
  4. Sensor Integration for Localization : Some Methodologies = 540
   4.1. Data integration - Intrinsic sensor level = 542
   4.2. Data integration - Extrinsic sensor level = 544
  5. Experimental Setup = 547
   5.1. Sensors' descriptions = 547
  6. Conclusions = 553
  References = 553
 CHAPTER 19 INCREMENTAL DESIGN OF A FLEXIBLE ROBOTIC ASSEMBLY CELL USING REACTIVE ROBOTS / E. S. Tzafestas ; S. G. Tzafestas
  1. Introduction = 555
  2. Description of the Assembly Cell = 556
  3. Basic Architecture of the Robot = 559
  4. Case 1 : The minimal Assembly Cell = 561
  5. Case 2 : Extending the Robots Architecture = 562
  6. Case 3 : Using More than one Assembly Robots = 563
  7. Case 4 : Combining Cases 2 and 3 - Interacting Factors = 565
  8. Case 5 : The Adaptive Robot - Commitment to Product = 567
  9. Conclusions and Further Work = 569
  References = 570
 CHAPTER 20 ON THE COMPARISON OF AI AND DAI BASED PLANNING TECHNIQUES FOR AUTOMATED MANUFACTURING SYSTEMS / A. I. Kokkinaki ; K. P. Valavanis
  1. Introduction = 573
  2. Traditional Artificial Intelligence Planning Systems = 575
   2.1. Theorem proving based planning systems = 577
   2.2. Blackboard - based architectures = 579
   2.3. Assembly planning and assembly sequences representations = 582
  3. Distributed Artificial Intelligence Planning Systems = 593
   3.1. Coordination in multi - agent planning = 594
   3.2. Theories of belief = 595
   3.3. Synchronization of multi - agents = 595
  4. Distributed Planning Systems = 596
   4.1. Route planning using distributed techniques = 596
   4.2. Distributed NOAH = 600
  5. Distributed Planning Synchronization examples = 601
   5.1. CSP influenced synchronization method = 601
   5.2. Partial plan synchronization = 605
   5.3. Logic based plan synchronization = 606
  6. Application of Learning to Planning = 608
  7. Conclusions = 610
  References = 612
 CHAPTER 21 KNOWLEDGE - BASED SUPERVISION OF FLEXIBLE MANUFACTURING SYSTEMS / A. K. A. Toguyeni ; E. Craye ; J. - C. Gentina
  1. Supervision and AI - Techniques = 631
  2. Piloting Functions = 632
   2.1. Introduction = 632
   2.2. Problems met from design to implementation = 633
   2.3. The knowledge - based system = 634
   2.4. Conclusion = 637
  3. Manager of Working Modes = 637
   3.1. Introduction = 637
   3.2. Representation and modelling of the process = 638
   3.3. The manager framework = 642
   3.4. Conclusion = 648
  4. A Model - Based Diagnostic System for On - Line Monitoring = 650
   4.1. Introduction = 650
   4.2. The modeling method = 650
   4.3. The causal temporal signature or CTS = 651
   4.4. The multi - agent framework of diagnostic system = 655
   4.5. Conclusion = 660
  5. General Conclusion = 660
  References = 661
 CHAPTER 22 A SURVEY OF KNOWLEDGE - BASED INDUSTRIAL SCHEDULING / K. S. Hindi ; M. G. Singh
  1. Introduction = 663
  2. Knowledge Acquisition = 664
  3. Knowledge Representation = 665
   3.1. Logic - based systems = 665
   3.2. Rule - based systems = 666
   3.3. Frame - based systems = 667
   3.4. Multi knowledge representation systems = 668
  4. Temporal Issues = 669
  5. Control Mechanisms = 670
   5.1. Forward reasoning systems = 670
   5.2. Constraint - directed and opportunistic systems = 671
   5.3. Mixed control systems = 673
  6. Knowledge Based Scheduling Systems (KBSS) = 674
   6.1. The primary scheduler (PS) = 675
   6.2. The heuristic scheduler (HS) = 676
   6.2 The backtracking scheduler (BS) = 677
  7. Reactive and Real - Time Scheduling = 678
  8. Conclusions = 679
  References = 680
 CHAPTER 23 REACTIVE BATCH SCHEDULING / V. J. Terpstra ; H. B. Verbruggen
  1. Introduction = 688
   1.1. Project = 688
   1.2. Scheduling = 688
   1.3. Example case = 689
   1.4. Definitions = 690
  2. Scheduling strategy = 691
   2.1. Modelling = 692
   2.2. Modularity = 692
   2.3. Prediction and cycles = 693
   2.4. Reactive behaviour = 693
   2.5. Robustness = 694
  3. Modelling = 694
   3.1. The equipment model = 695
   3.2. The master recipe = 697
   3.3. Master schedule = 698
   3.4. The degrees of freedom of the scheduler = 699
  4. Planner = 699
  5. Integer scheduler = 700
  6. Non - integer scheduler = 704
   6.1. Ganeration of NLP model = 704
   6.2. Dedicated NLP solver = 707
  7. Reactiveness = 708
   7.1. Horizons = 708
   7.2. Sample Rate = 709
   7.3. Three Control Loops in Scheduler = 709
   7.4. Error Signal = 710
   7.5. Timing = 711
   7.6. Progressive Reasoning = 713
   7.7. Anticipatory Schedules = 714
   7.8. Parallelism = 716
  8. Robustness analysis = 716
  9. Implementation and Results = 719
  10. Conclusions = 720
  References = 720
 CHAPTER 24 APPLYING GROUPWARE TECHNOLOGIES TO SUPPORT MANAGEMENT IN ORGANIZATIONS / A. Michailidis, P. - I. Gouma ; R. Rada
  1. Introduction = 723
  2. Groupware = 723
   2.1. Groups and computer - supported cooperative work = 724
   2.2. Groupware taxonomy = 724
   2.3. Review of groupware systems = 728
  3. Management = 729
   3.1. Organizations = 730
   3.2. Managing organizations = 733
   3.3. IT Systems for management - support in organizations = 735
   3.4. Comparing R&D department with organizations = 737
  4. Case Study = 738
   4.1. Modelling the organizational structure = 739
   4.2. The activity model environment (AME) model = 739
   4.3. The modified version of AME = 740
  5. Implementation - The MUCH System = 745
  6. Conclusion = 747
  References = 748
INDEX = 757