Dynamic and Sustainable Flexible Job Shop Scheduling Problem under Worker Unavailability Risk

In the current context of Industry 5.0, sustainable
scheduling has emerged as an evolution of classical scheduling,
now integrating environmental and human-centric considerations.
The objective is to strike a balance between economic,
environmental, and societal concerns. Additionally, there is a
growing need to enhance the resilience in the Industry 5.0 era,
necessitating dynamic systems capable of reacting to unforeseen
disruptive events. This paper introduces a multi-objective dynamic
scheduling model designed to simultaneously minimize
the makespan, the energy consumption, and a standardized
ergonomic risk factor. The initial schedule is generated using
a non-dominated sorting algorithm (NSGA-III), and in the
event of worker absence during production, a rescheduling
process is initiated. The choice of rescheduling strategy is
determined using a Q-learning algorithm, allowing continuous
improvement in the selection of the optimal strategy depending
on the scenario. Results, derived from experiments conducted
on literature instances, demonstrate the model’s effectiveness in
swiftly generating new efficient schedules. The motivation for
this research stems from the increasing demand for sustainable
industrial practices that not only enhance productivity but also
reduce environmental impact and improve worker well-being,
thereby contributing to the development of more resilient and
sustainable industrial systems.