The objective of this study is to analyse the use of Association Rule Algorithms and Big Data Platforms in the optimal scheduling of production schedules for clothing intelligent manufacturing systems. The study relates to the problems that arise as a result of conventional scheduling in the context of evolving fashion business and suggests optimization solutions. Thus, collecting data, preprocessing them, using the Apriori algorithm to identify patterns, and integrating the results, the proposed methodology considered enabling the optimization of production performance and response rates in real-time using the Hadoop ecosystem. The results depicted thus established the enhancement in the machine usage, cycle time, yield, and efficiency reinforcing the approach adopted. Main observations are 15% increase in the usage of the machines, 3 days reduction in lead times, 3% increase in on time delivery rates, and 2% decrease in defect ratios. It refers the practical usefulness of achieving advanced data analytics in production scheduling and recommends the further improvement, including issues of combining the different kind of models and predictive analytics. The study finds that there is an effective framework in using Association Rule Algorithms and Big Data Platforms to implement effective production schedules and gives manufacturers the necessary tools to gain a competitive edge based on the dynamics in the market.
Kexin Yu (Sun,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: