Multi-Objective Sustainable Flexible Job Shop Scheduling Problem: Balancing Economic, Ecological, and Social Criteria

Industry 5.0 makes it imperative to reevaluate the manner of using resources in manufacturing systems to
ensure sustainability. In this context, scheduling problems are encountering new environmental and humanrelated
challenges, and the concept of sustainable scheduling has gained importance, aiming to balance
economic, environmental, and human factors. In this paper, we propose two multi-objective mathematical
models to simultaneously address these three factors as objective functions. In the first model, we consider
the operator safety while using the Occupational Repetitive Action (OCRA) index to assess ergonomic risks
related to task execution. The second model includes workers’ preferences in terms of machines, shifts
and task variety. The objective is to improve the general well-being of workers by proposing a schedule
that respects as much as possible their preferences. Both models integrate the travel time of workers and
products between machines. To solve these NP-hard scheduling problems, we use the Non-dominated Sorting
Genetic Algorithms II and III (NSGA-II and NSGA-III), enhanced with a Q-learning strategy for parameter
selection and a variable neighborhood search based on reinforcement learning. The obtained results provide
a comprehensive analysis of the interactions between these criteria, demonstrating the capability of such
approach to achieve a favorable balance between multiple objectives while addressing the new challenges of
production scheduling in Industry 5.0 context.