AI-aided metaheuristic optimized framework for job sequencing and machine failure prediction in T-shirt production system
This research proposes a study on AI-assisted optimized Job-Shop Production System (JPS) to improve performance, reduce cost and time, and detect machine failure. In the current industrial landscape, optimizing Job Shop Production Systems (JPS) is critical for achieving higher efficiency, reduced costs, and improved resource utilization. This study presents a comprehensive review of both conventional and artificial intelligence (AI)/machine learning (ML)-based approaches applied to JPS, with particular emphasis on job sequencing and machine failure prediction. The system is modeled as a multi-stage job-shop with diverse tasks and constraints, highlighting challenges in sequencing, resource allocation, and machine reliability. This work addresses job-shop scheduling in a T-shirt manufacturing system using Grey Wolf Optimization (GWO). While conventional GWO provides feasible job sequencing, it suffers from premature convergence. An Improved Grey Wolf Optimization (IGWO) algorithm is therefore proposed to enhance exploration and convergence efficiency. Simulation results show that IGWO achieves better job sequencing with reduced make-span, lower production cost, and improved resource utilization compared to standard GWO.