Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI)

Woodhead Publishing Series in Textiles

Preface

Acknowledgements

Chapter 1: Understanding key decision points in the apparel supply chain

Abstract:

1.1 Introduction

1.2 Selection of plant locations

1.3 Production scheduling and assembly line balancing control

1.4 Cutting room

1.5 Retailing

Chapter 2: Fundamentals of artificial intelligence techniques for apparel management applications

Abstract:

2.1 Artificial intelligence (AI) techniques: a brief overview

2.2 Rule-based expert systems

2.3 Evolutionary optimization techniques

2.4 Feedforward neural networks (FNNs)

2.5 Fuzzy logic

2.6 Conclusions

Chapter 3: Selecting the location of apparel manufacturing plants using neural networks

Abstract:

3.1 Introduction

3.2 Classification methods using artificial neural networks

3.3 Classifying decision models for the location of clothing plants

3.4 Classification using unsupervised artificial neural networks (ANN)

3.5 Classification using supervised ANN

3.6 Conclusion

3.7 Acknowledgements

3.9 Appendix: performance of back propagation (BP) and learning vector quantization (LVQ) with a different number of hidden neurons

Chapter 4: Optimizing apparel production order planning scheduling using genetic algorithms

Abstract:

4.1 Introduction

4.2 Problem formulation

4.3 Dealing with uncertain completion and start times

4.4 Genetic algorithms for order scheduling

4.5 Experimental results and discussion

4.6 Conclusions

4.7 Acknowledgement

Chapter 5: Optimizing cut order planning in apparel production using evolutionary strategies

Abstract:

5.1 Introduction

5.2 Formulation of the cut order planning (COP) decision-making model

5.3 Genetic COP optimization

5.4 An example of a genetic optimization model for COP

5.5 Conclusions

5.6 Acknowledgement

5.8 Appendix: comparison between industrial practice and proposed COP decision-making model

Chapter 6: Optimizing marker planning in apparel production using evolutionary strategies and neural networks

Abstract:

6.1 Introduction

6.2 Packing method for optimized marker packing

6.3 Evolutionary strategy (ES) for optimizing marker planning

6.4 Experiments to evaluate performance

6.5 Conclusion

Chapter 7: Optimizing fabric spreading and cutting schedules in apparel production using genetic algorithms and fuzzy set theory

Abstract:

7.1 Introduction

7.2 Problem formulation in fabric-cutting operations

7.3 Genetic optimization of fabric scheduling

7.4 Case studies using real production data

7.5 Conclusions

7.6 Acknowledgement

7.8 Appendix: nomenclature

Chapter 8: Optimizing apparel production systems using genetic algorithms

Abstract:

8.1 Introduction

8.2 Problem formulation in sewing operations

8.3 Genetic optimization of production line balancing

8.4 Experimental results

8.5 Conclusions

8.6 Acknowledgement

8.8 Appendix: nomenclature

Chapter 9: Intelligent sales forecasting for fashion retailing using harmony search algorithms and extreme learning machines

Abstract:

9.1 Introduction

9.2 Hybrid intelligent model for medium-term fashion sales forecasting

9.3 Evaluating model performance with real sales data

9.4 Experimental results and analysis

9.5 Assessing forecasting performance

9.6 Conclusions

6.7 Acknowledgement

Chapter 10: Intelligent product cross-selling system in fashion retailing using radio frequency identification (RFID) technology, fuzzy logic and rule-based expert system

Abstract:

10.1 Introduction

10.2 Radio frequency identification (RFID)-enabled smart dressing system (SDS)

10.3 Intelligent product cross-selling system (IPCS)

10.4 Implementation of the RFID-enabled SDS and IPCS

10.5 Evaluation of the RFID-enabled SDS

10.6 Assessing the use of RFID technology in fashion retailing

10.7 Conclusions

10.8 Acknowledgement

Index