Batch EOL products human-robot collaborative remanufacturing process planning and scheduling for industry 5.0

With the advancement of technology, the growing volume of end-of-life (EOL) products in manufacturing poses significant challenges to achieving sustainability and green transformation. Proper treatment of EOL components is a critical step toward green and sustainable manufacturing development. Remanufacturing, capable of transforming EOL products into items with performance comparable to or surpassing new products, has become a vital method for achieving sustainable manufacturing. Recently, Industry 5.0 has introduced a "human-centric" philosophy, elevating workers from passive participants to indispensable elements in enterprises. As a critical bridge between EOL products and remanufactured goods, integrating this human-centric philosophy into remanufacturing processing planning is of profound significance. Human-robot collaborative processing modes serve as a key means to realize this philosophy; However, research on human-robot collaborative strategies in remanufacturing processing remains underexplored. To address this gap, this study proposes a batch-oriented EOL human-robot collaborative remanufacturing process planning and job-shop scheduling (BHRCRPS) model based on two-layer coding theory. The upper-level model selects human-robot collaboration modes aligned with the human-centric philosophy by minimizing worker fatigue and maximizing robot task completion. The lower-level model, constrained by the upper-level results, establishes a multi-objective model incorporating time, cost, and energy consumption for remanufacturing process planning and shop floor scheduling, aiming to enhance efficiency, profitability, and energy savings. To efficiently solve the BHRCRPS model, a hybrid algorithm integrating the Grey Wolf Optimizer (GWO) and Rat Swarm Optimizer (RSO) is developed. Finally, the applicability of the model is validated through a case study on worn worm gear remanufacturing, and the superiority of the proposed GWO-RSO algorithm is demonstrated by comparisons with state-of-the-art algorithms.