Within the evolving industrial paradigm, research is increasingly focused on the transition toward manufacturing systems that are not only sustainable and resilient but also human-centric. In this context, the varying levels of experience, skills, productivity, and physical capabilities among operators present significant challenges in achieving a balanced and equitable distribution of tasks within manufacturing planning and scheduling. Tailoring workload assignments to individual operator characteristics can profoundly improve production planning. However, this human-centric dimension remains largely neglected in mainstream sustainable scheduling, which typically emphasizes economic and environmental objectives. To address this gap, this paper introduces a novel, human-centric approach to sustainable scheduling in flexible job-shop environments, combining three key criteria: Mean Flow Time (economic and environmental dimension), Total Energy Consumption (environmental and economic dimension) and equity in operator fatigue (social dimension). This research explores two distinct scenarios using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a multi-objective genetic algorithm, combined with discrete-event simulation: one where all operators have homogeneous fatigue and recovery profiles, and one where these profiles are heterogeneous. This integration highlights the importance of addressing individual human attributes into industrial planning, and opens up promising prospects for production environments that are not only sustainable but fair and human-centric.