Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI). Woodhead Publishing Series in Textiles

  • ID: 2719831
  • Book
  • 256 Pages
  • Elsevier Science and Technology
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Practitioners in apparel manufacturing and retailing enterprises in the fashion industry, ranging from senior to front line management, constantly face complex and critical decisions. There has been growing interest in the use of artificial intelligence (AI) techniques to enhance this process, and a number of AI techniques have already been successfully applied to apparel production and retailing. Optimizing decision making in the apparel supply chain using artificial intelligence (AI): From production to retail provides detailed coverage of these techniques, outlining how they are used to assist decision makers in tackling key supply chain problems. Key decision points in the apparel supply chain and the fundamentals of artificial intelligence techniques are the focus of the opening chapters, before the book proceeds to discuss the use of neural networks, genetic algorithms, fuzzy set theory and extreme learning machines for intelligent sales forecasting and intelligent product cross-selling systems.

- Helps the reader gain an understanding of the key decision points in the apparel supply chain- Discusses the fundamentals of artificial intelligence techniques for apparel management techniques- Considers the use of neural networks in selecting the location of apparel manufacturing plants

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Woodhead Publishing Series in Textiles



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


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


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


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


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


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


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


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


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


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


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


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Wong, Calvin
W. K. Wong is full professor at The Hong Kong Polytechnic University, Hong Kong and is currently with the endowed professorship title as Cheng Yik Hung Professor in Fashion. His areas of research range from computer vision to artificial intelligence with applications in the textile and fashion industries. He has published over hundred research articles in high-impact artificial intelligence related journals and serves as editorial board member of several journals. He also provides consultancy services to fashion and textile companies in the industry.
Guo, Z. X.
Leung, S Y SS. Y. S. Leung is based at the Institute of Textiles and Clothing, The Hong Kong Polytechnic University, China.
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