Green and on-time scheduling in real-world textile production: A genetic algorithm approach validated by MIP and benchmarked with ACO

Abstract

This study presents two batch-scheduling models: one minimising carbon footprint and another reducing finishing time plus tardiness. Batch scheduling is frequently used in textile, semiconductor, and chemical industries to group items, optimise throughput, and limit resource consumption. In textile dyeing, large water and energy demands make carbon reduction and on-time delivery essential goals. For MIP, GA, and ACO, the classical dual-objective form-total tardiness + total finishing time-is reformulated as a single carbon-footprint objective and then benchmarked against the two-objective versions. Exact MIP solutions on small instances validate the performance of both GA and ACO; medium-sized data sets enable a direct GA-ACO comparison, and GA's superior results motivate its exclusive use on large-scale scenarios. On the real factory data the carbon-aware GA, relative to its time-based counterpart, cuts total CO $ _2 $ 2 by 55%, reduces total tardiness by 66%, and raises average utilisation from 63.6% to 75%. A paired t-test over eleven data sets confirms that the CO $ _2 $ 2 reduction is statistically significant at the 95% confidence level, with no service-level degradation. All large instances are solved within minutes on laptop hardware, demonstrating that the proposed approach delivers sustainability and operational performance simultaneously.