## 1 Introduction  
Selective assembly is a manufacturing process for improving the precision of assembled components, particularly in environments where part variations can significantly affect product quality and functionality. Selective assembly is a process used to assist manufacturing systems in carefully matching parts based on their dimensional characteristics to compensate for machining errors and products [1]. Apart from selective assembly, major manufacturing process strategies can be broadly categorized into modular assembly systems and adaptive assembly processes. The modular assembly system is an assembly system that structures the assembly process into independent modules and applies standardized modules designed to perform specific assembly tasks [2]. [PERSON] and [PERSON] explained that applying modularization in the product design stage enhances product flexibility, quality, and ease of manufacturing [3]. [PERSON] applied this concept to construction projects and proposed a method to improve productivity, reduce on-site work, enhance safety, and achieve long-term cost savings [4]. Adaptive assembly processes are an assembly strategy that optimizes processes based on work characteristics. [PERSON] and [PERSON] defined this as a system that optimizes assembly support according to the worker's level of experience, learning speed, and individual tendencies [5]. [PERSON], [PERSON], and [PERSON] proposed a system integrated with AR and a Pattern Recognition System, emphasizing high adaptability and cost reduction [6]. [PERSON] and [PERSON] introduced a hybrid system that combines traditional manual assembly systems (MAS) and automated assembly systems (AAS), achieving experimental results of a 38.6% reduction in cycle time and a 62.8% increase in productivity [7]. Compared to the two previously mentioned assembly manufacturing strategies, selective assembly is the only quality control method that enables the assembly of high-precision products from low-precision components in complex product manufacturing. From an economic perspective, it is the most optimized approach for controlling costs and energy consumption [8]. This can be managed through digital twins and online inspection technologies, sensor signal processing techniques, and the optimization of combination selection algorithms [9]. In particular, [PERSON] and [PERSON] proposed selective assembly using a genetic algorithm, emphasizing that algorithms and optimization techniques are essential for enhancing the effectiveness of selective assembly [10]. Ball bearings and pistons are representative products that typically utilize selective assembly [11]. These products are essential across various industries, from small machinery to the automotive and aerospace sectors. For components used in the automotive and aerospace industries, which require high precision, selective assembly plays a crucial role in achieving these stringent requirements. Despite its advantages, selective assembly exhibits some drawbacks, notably in managing surplus parts and ensuring high process capability within the confinement of assembly tolerances [12]. The most fundamental solution to overcoming these drawbacks is optimizing the selective assembly algorithm, as it can reduce surplus parts and improve the process environment without causing additional physical losses or economic costs. [PERSON] emphasized that optimizing component combinations through a high-quality combination selection algorithm and maximizing the benefits of selective assembly can make a significant contribution to improving economic efficiency [13]. Algorithms and strategies have been developed from various perspectives to optimize the selective assembly process, and the recent research trends and related research are as follows. For example, the selective assembly process can be treated as an optimization problem.