Applications of artificial intelligence in the apparel industry: a review

Guo, Zitao; Wong, Wai Keung; Leung, S.Y.S.; Li, Min

doi:10.1177/0040517511411968

Cited by 106 publications

(54 citation statements)

References 101 publications

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“…In order to automatically generate apparel designs, Guo et al (2011) have dedicated to develop artificial intelligence based CAD systems via neural networks, genetic algorithm, fuzzy logic, immune system and expert system. Apart from this, some personalised style design systems (Mok et al, 2013, Kim and Cho, 2000, Zeng et al, 2013 allow users to create their own style.…”

Section: Apparel Design Support Systemmentioning

confidence: 99%

Apparel recommendation system evolution: an empirical review

Guan

Qin

Ling

et al. 2016

IJCST

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PurposeWith the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers' economic models to help drive online sales. Initially, this paper undertakes an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this article reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps, and eventually propose new research solutions to benefit apparel retailing market. Design/methodology/approachThis study utilizes empirical research drawing on 130 academic publications indexed from online Databases. We introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications, and keywords. FindingsThis study classified high-tech integrated apparel systems into 3D CAD systems, personalized design systems, and recommendation systems. Our research interest is focused on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms were tested in system modelling, existing research lacks of concerns in terms of apparel and users profiles research. Thus, from the review, we have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system. Originality/valueBased on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective.

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Section: Apparel Design Support Systemmentioning

confidence: 99%

Apparel recommendation system evolution: an empirical review

Guan

Qin

Ling

et al. 2016

IJCST

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“…In the area of production decision-making, a great number of papers have been published and there are also comprehensive survey papers (Dolgui and Prodhon 2007;Wang et al 2009;Wazed, Ahmed, and Nukman 2010), which involve a large number of research areas, including aggregate planning (Sakalli, Baykoc, and Birgoren 2010;Fahimnia, Farahani, and Sarkis 2013), shop scheduling (Engin, Ceran, and Yilmaz 2011;Jolai, Rabiee, and Asefi 2012) and assembly line balancing (Hu et al 2010;Guo et al 2012), etc. However, little attention has been paid on the production decisionmaking problems in the manufacturing environment with the consideration of multiple production plants located in different regions.…”

Section: Order Allocation and Decision-making In Apparel Manufacturingmentioning

confidence: 99%

A cloud-based intelligent decision-making system for order tracking and allocation in apparel manufacturing

Guo

Wong

Guo

2013

International Journal of Production Research

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This paper addressed the order tracking and allocation problem in an apparel manufacturing environment with multiple plants. A cloud-based intelligent decision-making system was developed to tackle this problem, which combined radio frequency identification and cloud computing technologies to capture real-time production records and make remote production order tracking, and employed computational intelligence techniques to generate effective order allocation solutions to appropriate plants. To evaluate the effectiveness of the proposed system, the system was implemented in an apparel manufacturing company with multiple plants, which reported distinct reductions in production costs and increases in production efficiency. This paper also investigated learning phenomenon in production and its effects on production efficiency and decision-making performance.

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“…Since then a large number of sales forecasting papers have been published, which involves a wide variety of applications in real-world industries, such as print circuit board industry [3] and apparel industry [4].…”

Section: A Sales Forecastingmentioning

confidence: 99%

Intelligent multivariate sales forecasting using wrapper approach and neural networks

Guo

Wong

2012

IEEE 10th International Conference on Industrial Informatics

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This research investigated a retail sales forecasting problem based on early sales. An effective multivariate intelligent decision-making (MID) model is developed to handle this problem by integrating a data preparation and preprocessing module, a harmony search-wrapper-based variable selection (HWVS) module and a multivariate intelligent forecaster (MIF) module. The HWVS module selects out the optimal input variable subset from given candidate inputs as the inputs of MIF. The MIF is proposed to model the relationship between the selected input variables and the sales volumes of retail products, and then employed to forecast the sales volumes of retail products. Experiments were conducted to evaluate the effectiveness of the proposed model. Results show that it is statistically significant that the proposed MID model can provide superior forecasts to ELM-based model and generalized linear model.

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Applications of artificial intelligence in the apparel industry: a review

Cited by 106 publications

References 101 publications

Apparel recommendation system evolution: an empirical review

Apparel recommendation system evolution: an empirical review

A cloud-based intelligent decision-making system for order tracking and allocation in apparel manufacturing

Intelligent multivariate sales forecasting using wrapper approach and neural networks

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