Under the background of intelligent manufacturing and Industry 4.0, the traditional flexible job-shop scheduling problem is often constrained by inadequate resource allocation due to the neglect of human factors. To address this issue, a multi-objective optimization model was developed by integrating worker learning effects and fatigue mechanisms, aiming to minimize the makespan, total energy consumption, and labor cost. An improved multi-objective salp swarm algorithm was proposed to solve the problem efficiently. The algorithm incorporated a Circle chaotic initialization mechanism to enhance population diversity. A differential evolution strategy was employed to strengthen local search, and a virtual force-based chain-following mechanism was designed to balance global exploration and local exploitation. The effectiveness of the proposed method was validated on extended benchmark instances using inverted generational distance and hypervolume as evaluation metrics. A real-world automotive wiring harness scheduling case was further analyzed. Experimental results show that the obtained Pareto solutions are closer to the origin and more uniformly distributed. The result offers a novel modeling framework and an effective solution approach for workshop scheduling problems that consider human-related characteristics, contributing theoretical significance to the field.