Machine learning-driven solutions for sustainable and dynamic flexible job shop scheduling under worker absences and renewable energy variability

This paper addresses the Dynamic Sustainable Flexible Job Shop Scheduling Problem (DSFJSSP) by going beyond the traditionally emphasized economic dimension — such as makespan, flow time, or resource utilization — to include human and environmental factors, along with their related disruptions. Specifically, it considers human-related constraints such as workers’ skills and ergonomic risks, as well as environmental
aspects like carbon emissions from operations. Additionally, the study investigates the impact of worker absences and variability in renewable energy availability. To solve this problem, a multi-objective non-linear integer programming model is developed and an improved Non-dominated Sorting Genetic Algorithm III
(INSGA-III) is employed et generate the initial scheduling solutions. Three Machine Learning (ML)-based approaches — Q-Learning, Deep Learning, and Deep Q-Learning — are used to determine the most effective rescheduling strategy in response to disruptions. Results show that partial rescheduling maintains a good balance across all objectives and a close adherence to the initial schedule. The right shift strategy is efficient for minor disruptions, while total rescheduling, though potentially effective, is time-consuming and can significantly deviate from the original schedule. The comparison of the considered ML methods confirms that the DQL offers the best adaptability and solution quality for selecting optimal rescheduling strategies. These results underscore the importance of adaptive scheduling in enhancing the resilience and sustainability of dynamic flexible job shop systems.