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


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



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


No. of pages:
© 2013
Woodhead Publishing
Electronic ISBN:
Print ISBN:

About the authors

Calvin Wong

W. K. Wong is an Associate Professor at The Hong Kong Polytechnic University, Hong Kong.

S Y S Leung

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