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Department of Mechanical engineering, EIT, Faridabad, Haryana, India.
For manufacturing processes to meet evolving market demands, they must be executed in the most efficient manner while maintaining flexibility against unanticipated disruptions. Often, traditional scheduling methods overlook dynamic variability and real-time interruptions. An inverse scheduling framework for a multi-machine flowshop system is proposed in this research with the primary objective of makespan minimisation. Several issue situations are analysed to validate the algorithmic efficacy, and metaheuristic techniques are used to obtain near-optimal solutions. Additionally, to handle unforeseen disruptions, a predictive–reactive scheduling paradigm is developed by incorporating idle buffers within processing intervals. Disruptions are simulated and characterised according to their frequency and repair time using fuzzy set theory. When disruptions reach predefined tolerance criteria, rescheduling begins.The model also takes work location effects and learning phenomena into account when predicting processing time. Furthermore, a model for scheduling fuzzy multi-objective parallel machines is introduced and compared to deterministic approaches. Weighted tardiness, earliness, flow time, and machine deterioration cost are all reduced at the same time.To further illustrate the advantages of the suggested method, comparative performance indicators such as convergence rate, solution stability, and computational efficiency are investigated. Sensitivity analysis is used to assess how learning parameters and the severity of disruptions affect scheduling performance. The findings demonstrate that in complex manufacturing settings, the combined fuzzy and metaheuristic framework greatly increases system resilience, lowers operating costs, and boosts decision-making reliability.
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