## 1 Introduction  
The construction industry faces multifaceted challenges, notably declining productivity and labor shortages, prompting a shift towards Off-Site Construction (OSC) methodologies. OSC methods, which encompass factory production and on-site assembly, have attracted attention for their potential to mitigate challenges by enhancing productivity and reducing labor demands [1, 2]. Within this domain, Prefabricated Prefinished Volumetric Construction and Prefabricated Concrete structures have emerged as promising solutions for diverse architectural needs [3, 4]. The effective management of processes, component supply chains, and material resources is crucial for boosting productivity in OSC projects and contributing to achieving the UN Sustainability Development Goals (SDGs) [5; 6]. Assembly planning at PC sites is a key factor that directly influences productivity [7]. Failure to consider prefabricated building characteristics and adopting impractical schedules can lead to project delays and increased costs [8]. In prefabricated building projects, determining the assembly sequence of the components is crucial for optimizing plans and efficiently coordinating equipment [9]. A comprehensive consideration of on-site variables is essential for adapting to these conditions [10]. For instance, in Reinforced Concrete (RC) composite construction sites, variations in the RC process owing to weather, unregulated concrete quantities, cement shortages, and same-day concrete delivery cancellations often lead to adjustments in the initially planned PC assembly sequence. The traditional approach for determining assembly sequences relies heavily on the practical knowledge of on-site managers and often lacks standardized evaluation criteria [9]. However, the complexities of larger and more intricate construction sites increase the likelihood of information mishandling, highlighting the need for a structured approach. Building Information Modeling (BIM) is useful in systematically managing a variety of project information [11]. Using Artificial Intelligence (AI) technology, the information of the building elements can be effectively visualized in the BIM model. This enhanced technology can allow project participants to manage the actual status of the building on a real-time basis [12]. This study aims to provide objective decision-making support for planning assemblies on evolving PC sites. It seeks to move away from subjective methods and envision adaptable assembly plans suitable for dynamic construction environments. Previous research has mainly focused on identifying the factors that cause delays in component assembly at construction sites. For instance, [PERSON] et al. (2022) [13] highlighted weather conditions and communication gaps among stakeholders as contributors to assembly delays at modular construction sites. Despite numerous studies examining the overall construction progress in prefabricated settings, there remains a noticeable gap in research that specifically investigates on-site assembly factors. Historically, scheduling methods such as the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) have been crucial; however, they face limitations owing to resource constraints, requiring subsequent optimization for practical use [14; 15]. Recent explorations of metaheuristic algorithms, notably Genetic Algorithms (GA), have shown promise in prefabricated construction. These algorithms automate assembly planning and reduce the construction complexity [16; 17]. However, current research mainly focuses on generating initial schedules and overlooks adaptation to on-site conditions, thereby limiting its applicability in real-world scenarios. On the other hand, in a current situation where a sustainability issue is emphasized, the construction practice requires a reduction in the resources required for the project. As a result, resource efficiency in the construction can have a significant impact on the sustainable future [18; 19].