Operational research (OR) and statistical analyses for the textile industry


Overview of special issue

The widespread use of optimization techniques in industry and process planning has changed the way of managing different stages of the traditional manufacturing processes. In this regard, the textile industry and its goods suffer from market uncertainty and this uncertainty negatively affects the production process throughout its supply chain from fiber production, fabric weaving, and knitting units to garment manufacturing and retail sales (Ghasemy Yaghin 2020; Darvishi et al. 2020). There is a need to disseminate and propose suitable techniques to guide managerial decision making. In this respect, OR- and statistical-based analytical models could provide valuable insights for the textile industry. Through this special issue, we seek to inform the textile manager in their journey to improving effectiveness and efficiency in the operations process. 

A variety of problems such as the lack of well trained machine operators to find stable setting points of the weaving machines (Goly et al. 2015), assembly production imbalance (Chen et al. 2012), incorrect cotton blending, infeasible production plan (Leung et al. 2003), raw material procurement vulnerabilities (Ghasemy Yaghin et al. 2020; Karami et al. 2020) or even technology-related issues can lead to the inefficiency of the textile companies. Today, the emergence of smart textile with applications in the military, public safety, healthcare, space exploration, sports, and consumer fitness fields generates huge amounts of data to be analyzed. Meanwhile, apparel fashion retailing has to adopt disruptive business solutions to reduce the long lead-times and tap market trends for sustained competitive advantage (Hu and Yu 2014). In attempting to address these problems, practitioners have been applying trial-and-error strategies or ad-hoc decisions. These strategies and decisions, even if implemented properly, does not always guarantee optimal performance of the textile manufacturers. 

This special issue calls for studies that bridge the nexus between OR and statistical decision-making techniques and textile management research while augmenting our understanding of productivity and competency in the textile sector. We are especially interested in those studies that apply linear and non-linear programming, multi-objective optimization, design of experiments (DOE), regression analysis and other pertinent analytical techniques to derive deep managerial insights in the textile industry.  

Indicative list of themes

  • Building empirical models using statistical analyses for textile and clothing production processes 
  • Yarn, fabric and apparel production planning and control 
  • Cotton blending 
  • Textile recycling and reverse logistics 
  • Apparel assembly line balancing 
  • Textile production productivity and quality control  
  • Textile technology management 
  • Workforce planning, work measurement and time study in textile manufacturers 
  • Visibility and transparency in textile fashion supply chains 
  • Sustainability (environmental and social impacts) planning of textile processes  
  • Big data (extracted from smart textiles) modeling and data analytics through OR and statistical learning techniques 


Chen, J. C., Chen, C.-C., Su, L.-H.,  Wu, H.-B.,  Sun, C.-J. 2012. Assembly line balancing in garment industry, Expert Systems with Applications, 39(12), 10073-10081.

Darvishi, F., Ghasemy Yaghin, R., Sadeghi, A. 2020. Integrated fabric procurement and multi-site apparel production planning with cross-docking: A hybrid fuzzy-robust stochastic programming approach. Applied Soft Computing, 106267.

Ghasemy Yaghin, R. 2020. Enhancing supply chain production-marketing planning with geometric multivariate demand function (a case study of textile industry). Computers & Industrial Engineering 140, 106220.

Ghasemy Yaghin, R., Sarlak, P., Ghareaghaji, A.A., 2020. Robust master planning of a socially responsible supply chain under fuzzy-stochastic uncertainty (A case study of clothing industry). Engineering Applications of Artificial Intelligence, 94, 103715.

Gloy, Y.-S., Sandjaja, F., Gries, T., 2015. Model based self-optimization of the weaving process, CIRP Journal of Manufacturing Science and Technology. 9, 88-96

Hu, Z-H., Yu, X., 2014. Optimization of fast-fashion apparel transshipment among retailers, Textile Research Journal, 84(20), 2127–2139.

Karami S., Ghasemy Yaghin, R., Mousazadegan, F., 2020. Supplier selection and evaluation in the garment supply chain: an integrated DEA–PCA–VIKOR approach, The Journal of The Textile Institute, https://doi.org/10.1080/00405000.2020.1768771

Leung, S. C. H., Wu, Y., & Lai, K. K. 2003. Multi-site aggregate production planning with multiple objectives: A goal programming approach. Production Planning & Control, 14, 425–436.

Submission information

Submissions close: 25 September 2022

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