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

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

From Production to Retail

1st Edition - January 24, 2013

Write a review

  • Authors: W K Wong, Z X Guo, S Y S Leung
  • Hardcover ISBN: 9780857097798
  • eBook ISBN: 9780857097842

Purchase options

Purchase options
Available
DRM-free (PDF, EPub, Mobi)
Sales tax will be calculated at check-out

Institutional Subscription

Free Global Shipping
No minimum order

Description

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.

Key Features

  • 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

Readership

R&D managers in production and design and researchers, instructors, post-graduate and under-graduate students studying fashion and intelligent textiles

Table of Contents

  • 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

Product details

  • No. of pages: 256
  • Language: English
  • Copyright: © Woodhead Publishing 2013
  • Published: January 24, 2013
  • Imprint: Woodhead Publishing
  • Hardcover ISBN: 9780857097798
  • eBook ISBN: 9780857097842

About the Authors

W K Wong

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.

Affiliations and Expertise

Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

Z X Guo

Affiliations and Expertise

Queen Mary University, UK

S Y S Leung

S. Y. S. Leung is based at the Institute of Textiles and Clothing, The Hong Kong Polytechnic University, China.

Affiliations and Expertise

Hong Kong Polytechnic University, China

Ratings and Reviews

Write a review

There are currently no reviews for "Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI)"