A Resource Sharing Mechanism for Sustainable Production in the Garment Industry

Ma, Ke; Wang, Lichuan; Chen, Yan

doi:10.3390/su10010052

Cited by 7 publications

(3 citation statements)

References 23 publications

(25 reference statements)

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“…The needs of consumers in the market have changed with the rise in material and cultural standards of living, from the traditional economical to the enjoyable. As a result, traditional mass production methods are no longer able to meet consumers' needs for fashion and individuality [ 9 ]. Today, in the pursuit of individuality and fashion, people have new requirements for bespoke garment designs that inspire confidence and satisfaction.…”

Section: Introductionmentioning

confidence: 99%

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Yan

2022

Computational Intelligence and Neuroscience

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With the rapid economic development and rising consumption levels in recent years, people are becoming more and more demanding in terms of style and fashion of clothes. As a result, customer demand for personalised clothing is increasing and the need to respond quickly to consumer demands is also becoming a competitive issue for clothing companies. The automation and intelligence of the garment design and production process is an important part of the implementation of intelligent manufacturing in the garment industry and a necessary way to transform and upgrade the garment industry. Successful clothing styles always have a distinct style identity. The style of the garment can not only conveys the designer's vision but also express the emotional needs of the consumer. In contrast, traditional garment design involves only designers and a single style. With so many styles available, the user has only been able to combine them repeatedly and has not been able to create an innovative design. In addition, apparel design and product development is still a highly empirical task. To be specific, most apparel companies can only respond to a rapidly changing market by increasing the number of designers. However, this blind expansion of staff inevitably leads to increased production costs. As a result, how to effectively develop garment products without relying on the empirical knowledge of garment designers is one of the important issues in achieving intelligent manufacturing in garment enterprises. With the rapid development of computer and network technologies, artificial intelligence, machine learning, and expert systems are widely used in various industries. Nevertheless, the application of these advanced technologies in the field of garment design is still not deep enough. This is mainly due to the uncertainty and imprecision of garment design knowledge. Also, with the rapid development of the fashion industry and the arrival of the trend of personalisation, people's demand for clothing has gradually shifted from mass appeal in terms of comfort and aesthetics to personalisation in terms of self-polishing and temperament. The personalisation of clothing encompasses a wide range of preferences in terms of style and fit. The bottom-up design process and the relatively independent setup of functional modules in traditional clothing technology have prevented the different design levels from being interlinked. This does not reflect the composition of the garment elements in the process of forming features and makes it difficult to grasp the overall design state of the garment. Therefore, in order to address these above issues, this paper proposes a garment design model based on the Bayesian classifier and decision tree algorithm to investigate how computer technologies can be applied to model garment design knowledge. This model can enable inexperienced designers to develop garment products quickly and efficiently to meet the customisation needs of customers, thus enhancing the market competitiveness of garment enterprises.

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Section: Introductionmentioning

confidence: 99%

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Yan

2022

Computational Intelligence and Neuroscience

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“…16 With the new paradigm shift in mass customization, the traditional garment production model needs to be optimized to have a more sustainable structure in order to meet demand for flexibility, low-cost, and high-efficiency. 17 The present article is based on an empirical study conducted at Southern Range Nyanza Limited (NYTIL) garment facility which regularly receive orders in large quantities and thus has imposed constant pressure to meet the demands of customers, but the existing line balancing condition is inferior and ineffective for achieving the line production target, even if the operators were to undergo forced overtime. Therefore, this article proposed a complex garment assembly line balancing using SBO and it is organized as follows; Section 2 briefly provide the relevant literature on the study, while Section 3 describes the methodology of the study.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, sewing or assembly line is the most critical phenomenon of garment manufacturing as it generally composes of huge number of operations and resources 16 . With the new paradigm shift in mass customization, the traditional garment production model needs to be optimized to have a more sustainable structure in order to meet demand for flexibility, low‐cost, and high‐efficiency 17 . The present article is based on an empirical study conducted at Southern Range Nyanza Limited (NYTIL) garment facility which regularly receive orders in large quantities and thus has imposed constant pressure to meet the demands of customers, but the existing line balancing condition is inferior and ineffective for achieving the line production target, even if the operators were to undergo forced overtime.…”

Section: Introductionmentioning

confidence: 99%

A complex garment assembly line balancing using simulation‐based optimization

Bongomin

Mwasiagi

Nganyi

et al. 2020

Engineering Reports

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The nascent wave of disruptive competition in the current business environment brought about by the fourth industrial revolution (Fashion 4.0 or Apparel 4.0) is enormous. Therefore, it is important for the apparel industry to be flexible enough to respond quickly to the unstable customers' demand through continuous improvement of their process efficiency and productivity. This study proposed assembly line balancing problem (ALBP) for complex garment assembly line using simulation-based optimization under stochastic task times. The proposed ALBP solution approach aimed at minimizing the cycle time for a given number of workstations with consideration of constraints on number of resources, precedence relations, and resource types. The empirical study was conducted at Southern Range Nyanza Limited (NYTIL) garment facility and a complex trouser assembly line with 69 workstations was considered. The discrete event simulation of the trouser assembly line was developed using Arena simulation software. The local optimal solution was obtained from simulation experiments which was adopted for the optimization process. The OptQuest tool was used to solve a single objective optimization with discrete control values. The results showed that the average throughput increased by 30% for local optimal line balancing and 55% for global optimal line balancing. Consequently, the cycle time reduced by 23% and 36%, respectively.

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Development of a central order processing system for optimizing demand-driven textile supply chains: a real case based simulation study

Thomassey

Zeng

2018

Ann Oper Res

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A Resource Sharing Mechanism for Sustainable Production in the Garment Industry

Cited by 7 publications

References 23 publications

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

A complex garment assembly line balancing using simulation‐based optimization

Development of a central order processing system for optimizing demand-driven textile supply chains: a real case based simulation study

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