Human-centric assembly planning framework for human–robot collaborative systems with efficient reinforcement self-learning multi-objective evolutionary optimizer

Human–robot collaboration (HRC) assembly systems represent an emerging production paradigm that plays a key role in the transition towards human-centric, sustainable, and resilient manufacturing. However, to improve efficiency, HRC often adopts overly compact workflows, high-intensity work rhythms, and rigid task planning modes. Although these designs can improve efficiency in the short term, they overlook the differences in human and robot capabilities, human execution fluctuation, and dynamic fatigue effects. This may lead to excessive physiological load, increased psychological burden on humans, and more safety risks. Such collaboration paradigms not only conflict with the human-centric principle, but also impact the collaboration sustainability . In this study, we innovatively model a series of complex nonlinear relationships necessary to resolve these conflicts, and based on this, we construct a mixed integer programming framework for human-centric HRC assembly task planning (HAPF). HAPF quantitatively and adaptively expresses multiple conflicting objectives as well as coupled constraints such as resources, time, capabilities, and priorities. It is the first to explicitly address the trade-offs between multiple production objectives in HRC, jointly handle discrete decisions and continuous variables, and integrate resource allocation and task planning challenges into a unified optimization model that adapts to dynamic human variability. Furthermore, the inclusion of complex nonlinearities causes the complexity of the multi-objective and multi-constrained human-centric HRC assembly task planning problem (HAPP) based on HAPF to grow exponentially, making it difficult to solve. Therefore, we also propose a reinforcement self-learning-guided genetic operator and differential evolution operator collaborative optimizer (SL-MGDO) specifically designed to handle and solve HAPP. Experiments are conducted on 16 different instances, combining 4 task scales and 4 durations. Statistical tests indicate that the combination of HAPF-based HAPP with SL-MGDO can provide HRC planning solutions that are either comparable to or superior in efficiency to the previous collaboration mode, with higher execution competency, achieving a flexible, non-rigid advanced HRC process. Moreover, compared to 3 ablation variants and 6 widely studied state-of-the-art metaheuristic algorithms , SL-MGDO is able to obtain significantly better solutions. Furthermore, by applying HAPF and SL-MGDO to an actual HRC computer case assembly task, we further confirm the feasibility and practicality of SL-MGDO in real-world engineering applications .