Emerging optimization techniques in production planning and control:
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| Format: | Book |
| Language: | English |
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London
Imperial College Press
2002
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXI, 632 S. graph. Darst. |
| ISBN: | 1860942660 |
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MARC
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| 650 | 4 | |a Mathematisches Modell | |
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| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Production management |x Mathematical models | |
| 650 | 4 | |a Process control |x Mathematical models | |
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Record in the Search Index
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| adam_text | Contents
Preface v
Acknowledgements ix
PART 1 Introduction 1
Chapter 1 Introduction to Adaptive Memory Programming
and Production Planning and Control 3
1.1. Production Planning Control within Integrated Manufacturing
Framework 3
1.1.1. Demand management 5
1.1.2. Production planning 6
1.1.3. Master production scheduling 6
1.1.4. Final assembly scheduling 7
1.1.5. Material requirement planning 7
1.1.6. Resource requirement planning and allocation 7
1.1.7. Rough-cut-capacity planning 8
1.1.8. Capacity requirement planning 8
1.1.9. Production control 8
1.1.10. New developments 9
1.2. Conventional Combinatorial Optimization Techniques 10
1.2.1. Optimization algorithms 12
1.2.2. Approximation algorithms or heuristics 12
1.3. Intelligent Optimization Fundamentals 14
1.3.1. Adaptive memory 16
1.3.2. Strategic exploration 17
1.3.3. Intensification 17
1.3.4. Diversification 17
xiv Contents
1.3.5. Neighbourhood 18
1.3.6. Move type 18
1.3.7. Solution from constructive methods 19
1.3.8. Generic scheme of an adaptive memory programming ... 19
1.4. Adaptive Memory Programming 20
1.4.1. Explicit memory versus implicit memory 21
1.4.2. Neural networks 22
1.4.3. Genetic algorithms 24
1.4.4. Tabu Search 24
1.4.5. Ant systems 25
1.5. Hybrid Systems 26
1.6. Summary 29
PART 2 Production Planning and Control Decisions 33
Chapter 2 Production Planning Systems 35
2.1. Introduction 35
2.2. Demand Forecasting 38
2.2.1. Simple moving average 38
2.2.2. Simple regression 41
2.2.3. Simple exponential smoothing 42
2.2.4. Seasonal exponential smoothing 45
2.3. Production Planning 47
2.3.1. Chase strategy 53
2.3.2. Level-production strategy 55
2.3.3. Flexible working hours strategy 57
2.4. Master Production Schedule 59
2.4.1. Calculating the Master Production Schedule (MPS) .... 60
2.5. Material Requirement Planning (MRP) 63
2.5.1. Material Resources Planning (MRP II) 72
2.6. Resource Requirement Planning and Allocation 75
2.7. Rough Cut Capacity Planning (RCCP) 82
2.8. Capacity Resources Planning (CRP) 83
2.9. Summary 85
Contents xv
Chapter 3 Production Control Systems 90
3.1. Introduction 90
3.2. Scheduling in Job Shop Production 92
3.2.1. Dispatching rules 94
3.2.2. Nondelay schedules 97
3.3. Scheduling in Batch Production 103
3.3.1. Batch sizing 104
3.3.2. Batch sequencing 105
3.3.3. Line of balance for batch production scheduling 106
3.4. Scheduling in Line Flow Production Ill
3.4.1. Curtailed-enumeration algorithm of NEH 113
3.4.2. Algorithms 115
3.4.3. Branch and bound technique 119
3.4.4. A graphical flow-shop manufacturing scheduling system:
FSMS 127
3.5. Scheduling in Assembly Line Production 133
3.5.1. Line balancing 133
3.6. Material Management 140
3.6.1. Purchasing 140
3.6.2. Process selection 141
3.6.3. Inventory control 145
3.6.4. Material handling and storage 145
3.6.5. Distribution and logistics 145
3.7. Inventory Control 147
3.7.1. Lot sizing for independent demand items 147
3.7.2. Economic order quantity 148
3.7.3. Economic Production Quantity (EPQ) 150
3.7.4. Economic Discounts Quantity (EQD) 152
3.8. Inventory Control Systems 154
3.8.1. Order point and stockout 155
3.8.2. Continuous review system 156
3.8.3. Periodic review system 157
3.8.4. Periodic Order Quantity (POQ) 159
3.9. Quality Control 161
3.9.1. Taguchi method for quality control 163
3.9.2. Process analysis and control tools 173
3.9.3. Statistical Process Control (SPC) tools 175
xvi Contents
3.9.4. Process control for variables 176
3.10. Summary 180
PART 3 Emerging Optimization Techniques 187
Chapter 4 Artificial Neural Networks 189
4.1. Background to Neural Networks 189
4.2. Learning in Supervised Neural Networks: Delta Rule 193
4.3. Backpropagation Neural Network (BPN) 197
4.4. Self-Organising Map (SOM) Neural Network 201
4.4.1. Neighbourhood 203
4.4.2. Size of Kohonen layer 204
4.4.3. Training coefficient 204
4.5. Adaptive Resonance Theory 204
4.5.1. Basic features of ART network 206
4.5.2. ART1 208
4.5.3. ART2 219
4.6. Hopfield Neural Network 227
4.7. Application of Neural Networks to Machine Tooling and
Production Sequencing in Manufacturing Cell Planning 229
4.7.1. Complete tooling changeover: a TSP approach 230
4.7.2. Partial tooling changeover 237
4.7.3. Assembly cell assignment and sequencing 247
4.7.4. Automated turret punch press location and hit
sequencing 252
4.7.5. Flow shop sequencing 255
4.7.6. Job shop sequencing 261
4.8. Summary 268
Chapter 5 Genetic Algorithms 273
5.1. Introduction 273
5.1.1. Genetic algorithms versus traditional optimization and
search techniques 274
5.2. Fundamentals of Genetic Algorithms 276
5.2.1. Selection 277
5.2.2. Partial bits exchange 278
Contents xvii
5.2.3. Random bit alteration 281
5.3. Manual Simulation of Genetic Algorithms 282
5.4. Aggregate Production Planning 286
5.4.1. Mathematical model for aggregate production planning . . 292
5.4.2. Aggregate production planning fundamentals 294
5.5. Genetic Algorithms Design Issues 297
5.5.1. Representation: genetic code 298
5.5.2. Initialization 299
5.5.3. Evaluation or score function values 299
5.5.4. Selection and selection 303
5.5.5. Partial bits exchange: mechanism to obtain new
solutions 304
5.5.6. Improving partial bits exchange operation 306
5.5.7. Random bit alteration: mechanism to obtain new
solutions 307
5.5.8. Replacement strategy 308
5.5.9. Convergence and divergence policies 308
5.5.10. The entropy measure 309
5.5.11. Diversification 309
5.5.12. Preventing loss of solutions 310
5.6. Genetic Algorithm Implementation 310
5.6.1. Data structures 310
5.6.2. The genetic algorithm procedures 311
5.7. Qualitative Innovations and Improvements 313
5.7.1. Parameter setting 314
5.7.2. Genetic algorithm operation 315
5.8. Computational Tests and Results 318
5.8.1. Comparing genetic algorithms with other methods 319
5.8.2. Comparing genetic algorithms with integer linear
programming 322
5.9. Summary 325
Chapter 6 Tabu Search 333
6.1. Background to Tabu Search 333
6.2. The Dilemma of Hill Climbing 336
6.3. Tabu Search Fundamentals 338
6.4. Short Term Memory in Tabu Search 339
xviii Contents
6.4.1. Recency-based memory 341
6.4.2. Tabu tenure 344
6.4.3. Aspiration criteria 346
6.4.4. Basic ideas for implementing recency-based memory .... 347
6.4.5. Candidate list strategies 349
6.5. Long Term Memory in Tabu Search 351
6.5.1. Frequency-based memory 351
6.5.2. Logical restructuring 354
6.5.3. Intensification strategies 358
6.5.4. Diversification strategies 359
6.5.5. Strategic oscillation 361
6.5.6. Basic ideas for implementing long term memory 363
6.6. The Theory of Constraints Product Mix Problem 364
6.6.1. Labour-based management accounting 364
6.6.2. Linear programming for the product mix problem 369
6.6.3. Theory of constraints heuristic for the product mix
problem 371
6.6.4. More on theory of constraints 374
6.6.5. Revised theory of constraints algorithm for the product
mix problem 381
6.7. Application of Tabu Search to the Product Mix Problem 388
6.7.1. Initial solution 388
6.7.2. Move 389
6.7.3. Neighbourhood size 390
6.7.4. Tabu list size 391
6.7.5. Aspiration criterion 391
6.7.6. Intensification 391
6.7.7. Diversification 391
6.7.8. Stopping criteria 392
6.8. Summary 396
Chapter 7 Ant Systems 401
7.1. The Ant System Paradigm 401
7.2. Ant Systems Fundamentals 404
7.2.1. Pheromone trail or memory 404
7.2.2. Solutions manipulation 405
7.2.3. Intensification and diversification 406
Contents xix
7.3. FANT: Fast Ant System 406
7.3.1. Memory implementation 407
7.3.2. Provisory solution generation 408
7.3.3. Improvement procedure 411
7.3.4. Memory updates 414
7.3.5. Intensification strategies 417
7.3.6. Diversification strategies 417
7.4. HAS: Hybrid Ant System 418
7.4.1. Memory implementation 420
7.4.2. Provisory solution generation 420
7.4.3. Improvement procedure 421
7.4.4. Memory update 421
7.4.5. Intensification 422
7.4.6. Diversification 422
7.5. The FANT Simulator 422
7.5.1. FANT data structures 423
7.5.2. FANT algorithms 424
7.6. HAS Simulator 428
7.7. Application of FANT to Flow Shop Scheduling: 1-Criterion .... 429
7.7.1. Flow shop scheduling 429
7.7.1.1. Graphical method for flow shop scheduling .... 433
7.7.1.2. Heuristic methods for flow shop scheduling .... 441
7.7.1.3. Performance measures for flow shop
scheduling 445
7.7.1.4. FANT simulator output for flow shop
scheduling 446
7.8. Application of FANT to Flow Shop Scheduling: Bi-Criteria .... 464
7.8.1. Mathematical formulation of the bi-criterion flow shop
problem 466
7.8.2. Daniels and Chambers heuristic for the bi-criteria
problem 467
7.8.3. Ant System-based Heuristic (ASH) for the bi-criteria
problem 468
7.9. Summary 475
xx Contents
Chapter 8 Simulated Annealing 487
8.1. Simulated Annealing Paradigm 487
8.2. Monte Carlo Model for Simulating Physical Annealing 490
8.3. Analogy Between Physical and Simulated Annealing 492
8.4. Cooling Schedule Classifications for Simulated Annealing
Schemes 495
8.4.1. Step-wise temperature reduction scheme 496
8.4.2. Continuous temperature reduction scheme 498
8.4.3. Non-monotonic temperature reduction scheme 499
8.5. Neighbourhood Search Techniques 500
8.5.1. Random search 500
8.5.2. Systematic search 501
8.6. Production Layout Strategies 501
8.6.1. Fixed-position layout 505
8.6.2. Process-based layout 506
8.6.3. Product-based layout 507
8.6.4. Group technology layout 507
8.7. Production Layout Planning 513
8.7.1. Determining part families 513
8.7.2. Cell formation 514
8.7.3. Rationalization of part families and workloads 533
8.7.4. Selecting the type of cell layout 533
8.7.5. Layout machines and auxiliary facilities in cell 536
8.8. Application of Simulated Annealing to Cell Formation 537
8.8.1. Problem representation 537
8.8.2. Transition mechanism 537
8.8.3. Cooling schedule 538
8.8.4. The simulated annealing algorithm 539
8.9. Summary 547
Chapter 9 Programming Techniques 553
9.1. Data Structure 554
9.2. Modular Design 557
9.3. Simple Tabu Search Run 573
9.4. Summary 584
Contents xxi
Appendix 587
A. Pascal Fundamentals 587
A.I. Putting Pascal fundamentals to use 602
A.2. Getting something from Pascal fundamentals 606
A.3. Summary 608
B. A Simple Tabu Search in Pascal 608
Author Index 625
Subject Index 629
i
|
| adam_txt |
Contents
Preface v
Acknowledgements ix
PART 1 Introduction 1
Chapter 1 Introduction to Adaptive Memory Programming
and Production Planning and Control 3
1.1. Production Planning Control within Integrated Manufacturing
Framework 3
1.1.1. Demand management 5
1.1.2. Production planning 6
1.1.3. Master production scheduling 6
1.1.4. Final assembly scheduling 7
1.1.5. Material requirement planning 7
1.1.6. Resource requirement planning and allocation 7
1.1.7. Rough-cut-capacity planning 8
1.1.8. Capacity requirement planning 8
1.1.9. Production control 8
1.1.10. New developments 9
1.2. Conventional Combinatorial Optimization Techniques 10
1.2.1. Optimization algorithms 12
1.2.2. Approximation algorithms or heuristics 12
1.3. Intelligent Optimization Fundamentals 14
1.3.1. Adaptive memory 16
1.3.2. Strategic exploration 17
1.3.3. Intensification 17
1.3.4. Diversification 17
xiv Contents
1.3.5. Neighbourhood 18
1.3.6. Move type 18
1.3.7. Solution from constructive methods 19
1.3.8. Generic scheme of an adaptive memory programming . 19
1.4. Adaptive Memory Programming 20
1.4.1. Explicit memory versus implicit memory 21
1.4.2. Neural networks 22
1.4.3. Genetic algorithms 24
1.4.4. Tabu Search 24
1.4.5. Ant systems 25
1.5. Hybrid Systems 26
1.6. Summary 29
PART 2 Production Planning and Control Decisions 33
Chapter 2 Production Planning Systems 35
2.1. Introduction 35
2.2. Demand Forecasting 38
2.2.1. Simple moving average 38
2.2.2. Simple regression 41
2.2.3. Simple exponential smoothing 42
2.2.4. Seasonal exponential smoothing 45
2.3. Production Planning 47
2.3.1. Chase strategy 53
2.3.2. Level-production strategy 55
2.3.3. Flexible working hours strategy 57
2.4. Master Production Schedule 59
2.4.1. Calculating the Master Production Schedule (MPS) . 60
2.5. Material Requirement Planning (MRP) 63
2.5.1. Material Resources Planning (MRP II) 72
2.6. Resource Requirement Planning and Allocation 75
2.7. Rough Cut Capacity Planning (RCCP) 82
2.8. Capacity Resources Planning (CRP) 83
2.9. Summary 85
Contents xv
Chapter 3 Production Control Systems 90
3.1. Introduction 90
3.2. Scheduling in Job Shop Production 92
3.2.1. Dispatching rules 94
3.2.2. Nondelay schedules 97
3.3. Scheduling in Batch Production 103
3.3.1. Batch sizing 104
3.3.2. Batch sequencing 105
3.3.3. Line of balance for batch production scheduling 106
3.4. Scheduling in Line Flow Production Ill
3.4.1. Curtailed-enumeration algorithm of NEH 113
3.4.2. Algorithms 115
3.4.3. Branch and bound technique 119
3.4.4. A graphical flow-shop manufacturing scheduling system:
FSMS 127
3.5. Scheduling in Assembly Line Production 133
3.5.1. Line balancing 133
3.6. Material Management 140
3.6.1. Purchasing 140
3.6.2. Process selection 141
3.6.3. Inventory control 145
3.6.4. Material handling and storage 145
3.6.5. Distribution and logistics 145
3.7. Inventory Control 147
3.7.1. Lot sizing for independent demand items 147
3.7.2. Economic order quantity 148
3.7.3. Economic Production Quantity (EPQ) 150
3.7.4. Economic Discounts Quantity (EQD) 152
3.8. Inventory Control Systems 154
3.8.1. Order point and stockout 155
3.8.2. Continuous review system 156
3.8.3. Periodic review system 157
3.8.4. Periodic Order Quantity (POQ) 159
3.9. Quality Control 161
3.9.1. Taguchi method for quality control 163
3.9.2. Process analysis and control tools 173
3.9.3. Statistical Process Control (SPC) tools 175
xvi Contents
3.9.4. Process control for variables 176
3.10. Summary 180
PART 3 Emerging Optimization Techniques 187
Chapter 4 Artificial Neural Networks 189
4.1. Background to Neural Networks 189
4.2. Learning in Supervised Neural Networks: Delta Rule 193
4.3. Backpropagation Neural Network (BPN) 197
4.4. Self-Organising Map (SOM) Neural Network 201
4.4.1. Neighbourhood 203
4.4.2. Size of Kohonen layer 204
4.4.3. Training coefficient 204
4.5. Adaptive Resonance Theory 204
4.5.1. Basic features of ART network 206
4.5.2. ART1 208
4.5.3. ART2 219
4.6. Hopfield Neural Network 227
4.7. Application of Neural Networks to Machine Tooling and
Production Sequencing in Manufacturing Cell Planning 229
4.7.1. Complete tooling changeover: a TSP approach 230
4.7.2. Partial tooling changeover 237
4.7.3. Assembly cell assignment and sequencing 247
4.7.4. Automated turret punch press location and hit
sequencing 252
4.7.5. Flow shop sequencing 255
4.7.6. Job shop sequencing 261
4.8. Summary 268
Chapter 5 Genetic Algorithms 273
5.1. Introduction 273
5.1.1. Genetic algorithms versus traditional optimization and
search techniques 274
5.2. Fundamentals of Genetic Algorithms 276
5.2.1. Selection 277
5.2.2. Partial bits exchange 278
Contents xvii
5.2.3. Random bit alteration 281
5.3. Manual Simulation of Genetic Algorithms 282
5.4. Aggregate Production Planning 286
5.4.1. Mathematical model for aggregate production planning . . 292
5.4.2. Aggregate production planning fundamentals 294
5.5. Genetic Algorithms Design Issues 297
5.5.1. Representation: genetic code 298
5.5.2. Initialization 299
5.5.3. Evaluation or score function values 299
5.5.4. Selection and selection 303
5.5.5. Partial bits exchange: mechanism to obtain new
solutions 304
5.5.6. Improving partial bits exchange operation 306
5.5.7. Random bit alteration: mechanism to obtain new
solutions 307
5.5.8. Replacement strategy 308
5.5.9. Convergence and divergence policies 308
5.5.10. The entropy measure 309
5.5.11. Diversification 309
5.5.12. Preventing loss of solutions 310
5.6. Genetic Algorithm Implementation 310
5.6.1. Data structures 310
5.6.2. The genetic algorithm procedures 311
5.7. Qualitative Innovations and Improvements 313
5.7.1. Parameter setting 314
5.7.2. Genetic algorithm operation 315
5.8. Computational Tests and Results 318
5.8.1. Comparing genetic algorithms with other methods 319
5.8.2. Comparing genetic algorithms with integer linear
programming 322
5.9. Summary 325
Chapter 6 Tabu Search 333
6.1. Background to Tabu Search 333
6.2. The Dilemma of Hill Climbing 336
6.3. Tabu Search Fundamentals 338
6.4. Short Term Memory in Tabu Search 339
xviii Contents
6.4.1. Recency-based memory 341
6.4.2. Tabu tenure 344
6.4.3. Aspiration criteria 346
6.4.4. Basic ideas for implementing recency-based memory . 347
6.4.5. Candidate list strategies 349
6.5. Long Term Memory in Tabu Search 351
6.5.1. Frequency-based memory 351
6.5.2. Logical restructuring 354
6.5.3. Intensification strategies 358
6.5.4. Diversification strategies 359
6.5.5. Strategic oscillation 361
6.5.6. Basic ideas for implementing long term memory 363
6.6. The Theory of Constraints Product Mix Problem 364
6.6.1. Labour-based management accounting 364
6.6.2. Linear programming for the product mix problem 369
6.6.3. Theory of constraints heuristic for the product mix
problem 371
6.6.4. More on theory of constraints 374
6.6.5. Revised theory of constraints algorithm for the product
mix problem 381
6.7. Application of Tabu Search to the Product Mix Problem 388
6.7.1. Initial solution 388
6.7.2. Move 389
6.7.3. Neighbourhood size 390
6.7.4. Tabu list size 391
6.7.5. Aspiration criterion 391
6.7.6. Intensification 391
6.7.7. Diversification 391
6.7.8. Stopping criteria 392
6.8. Summary 396
Chapter 7 Ant Systems 401
7.1. The Ant System Paradigm 401
7.2. Ant Systems Fundamentals 404
7.2.1. Pheromone trail or memory 404
7.2.2. Solutions manipulation 405
7.2.3. Intensification and diversification 406
Contents xix
7.3. FANT: Fast Ant System 406
7.3.1. Memory implementation 407
7.3.2. Provisory solution generation 408
7.3.3. Improvement procedure 411
7.3.4. Memory updates 414
7.3.5. Intensification strategies 417
7.3.6. Diversification strategies 417
7.4. HAS: Hybrid Ant System 418
7.4.1. Memory implementation 420
7.4.2. Provisory solution generation 420
7.4.3. Improvement procedure 421
7.4.4. Memory update 421
7.4.5. Intensification 422
7.4.6. Diversification 422
7.5. The FANT Simulator 422
7.5.1. FANT data structures 423
7.5.2. FANT algorithms 424
7.6. HAS Simulator 428
7.7. Application of FANT to Flow Shop Scheduling: 1-Criterion . 429
7.7.1. Flow shop scheduling 429
7.7.1.1. Graphical method for flow shop scheduling . 433
7.7.1.2. Heuristic methods for flow shop scheduling . 441
7.7.1.3. Performance measures for flow shop
scheduling 445
7.7.1.4. FANT simulator output for flow shop
scheduling 446
7.8. Application of FANT to Flow Shop Scheduling: Bi-Criteria . 464
7.8.1. Mathematical formulation of the bi-criterion flow shop
problem 466
7.8.2. Daniels and Chambers heuristic for the bi-criteria
problem 467
7.8.3. Ant System-based Heuristic (ASH) for the bi-criteria
problem 468
7.9. Summary 475
xx Contents
Chapter 8 Simulated Annealing 487
8.1. Simulated Annealing Paradigm 487
8.2. Monte Carlo Model for Simulating Physical Annealing 490
8.3. Analogy Between Physical and Simulated Annealing 492
8.4. Cooling Schedule Classifications for Simulated Annealing
Schemes 495
8.4.1. Step-wise temperature reduction scheme 496
8.4.2. Continuous temperature reduction scheme 498
8.4.3. Non-monotonic temperature reduction scheme 499
8.5. Neighbourhood Search Techniques 500
8.5.1. Random search 500
8.5.2. Systematic search 501
8.6. Production Layout Strategies 501
8.6.1. Fixed-position layout 505
8.6.2. Process-based layout 506
8.6.3. Product-based layout 507
8.6.4. Group technology layout 507
8.7. Production Layout Planning 513
8.7.1. Determining part families 513
8.7.2. Cell formation 514
8.7.3. Rationalization of part families and workloads 533
8.7.4. Selecting the type of cell layout 533
8.7.5. Layout machines and auxiliary facilities in cell 536
8.8. Application of Simulated Annealing to Cell Formation 537
8.8.1. Problem representation 537
8.8.2. Transition mechanism 537
8.8.3. Cooling schedule 538
8.8.4. The simulated annealing algorithm 539
8.9. Summary 547
Chapter 9 Programming Techniques 553
9.1. Data Structure 554
9.2. Modular Design 557
9.3. Simple Tabu Search Run 573
9.4. Summary 584
Contents xxi
Appendix 587
A. Pascal Fundamentals 587
A.I. Putting Pascal fundamentals to use 602
A.2. Getting something from Pascal fundamentals 606
A.3. Summary 608
B. A Simple Tabu Search in Pascal 608
Author Index 625
Subject Index 629
i |
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| dewey-ones | 658 - General management |
| dewey-raw | 658.5/01/5118 22 |
| dewey-search | 658.5/01/5118 22 |
| dewey-sort | 3658.5 11 45118 222 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV023527270 |
| illustrated | Illustrated |
| index_date | 2024-07-02T22:34:12Z |
| indexdate | 2024-07-09T21:23:57Z |
| institution | BVB |
| isbn | 1860942660 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016847477 |
| oclc_num | 248403193 |
| open_access_boolean | |
| owner | DE-521 DE-634 |
| owner_facet | DE-521 DE-634 |
| physical | XXI, 632 S. graph. Darst. |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | Imperial College Press |
| record_format | marc |
| spelling | Onwubolu, Godfrey C. Verfasser aut Emerging optimization techniques in production planning and control Godfrey C. Onwubolu London Imperial College Press 2002 XXI, 632 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Mathematisches Modell Mathematical optimization Industrial applications Algorithms Production management Mathematical models Process control Mathematical models Produktionsplanung (DE-588)4047360-0 gnd rswk-swf Produktionskontrolle (DE-588)4175800-6 gnd rswk-swf Optimierung (DE-588)4043664-0 gnd rswk-swf Produktionskontrolle (DE-588)4175800-6 s Optimierung (DE-588)4043664-0 s Produktionsplanung (DE-588)4047360-0 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016847477&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Onwubolu, Godfrey C. Emerging optimization techniques in production planning and control Mathematisches Modell Mathematical optimization Industrial applications Algorithms Production management Mathematical models Process control Mathematical models Produktionsplanung (DE-588)4047360-0 gnd Produktionskontrolle (DE-588)4175800-6 gnd Optimierung (DE-588)4043664-0 gnd |
| subject_GND | (DE-588)4047360-0 (DE-588)4175800-6 (DE-588)4043664-0 |
| title | Emerging optimization techniques in production planning and control |
| title_auth | Emerging optimization techniques in production planning and control |
| title_exact_search | Emerging optimization techniques in production planning and control |
| title_exact_search_txtP | Emerging optimization techniques in production planning and control |
| title_full | Emerging optimization techniques in production planning and control Godfrey C. Onwubolu |
| title_fullStr | Emerging optimization techniques in production planning and control Godfrey C. Onwubolu |
| title_full_unstemmed | Emerging optimization techniques in production planning and control Godfrey C. Onwubolu |
| title_short | Emerging optimization techniques in production planning and control |
| title_sort | emerging optimization techniques in production planning and control |
| topic | Mathematisches Modell Mathematical optimization Industrial applications Algorithms Production management Mathematical models Process control Mathematical models Produktionsplanung (DE-588)4047360-0 gnd Produktionskontrolle (DE-588)4175800-6 gnd Optimierung (DE-588)4043664-0 gnd |
| topic_facet | Mathematisches Modell Mathematical optimization Industrial applications Algorithms Production management Mathematical models Process control Mathematical models Produktionsplanung Produktionskontrolle Optimierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016847477&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT onwubolugodfreyc emergingoptimizationtechniquesinproductionplanningandcontrol |