## 1 Introduction  
Recent global economic and societal progress has fostered rapid growth in the aircraft manufacturing industry [1]. Developing commercial aircraft intended for long operation cycles is a complex and time-consuming process [2]. Commercial aircraft consist of multiple functional systems, including structure, propulsion, hydraulics, flight control, fuel, avionics, and environmental management. Furthermore, each system comprises multiple subsystems, and there are numerous physical, functional, and other interfaces within and between the systems.  
The production of commercial aircraft, consisting of parts manufacturing and aircraft assembly, constitutes a complex, long-cycle process that is perpetually challenged by demand fluctuations and order uncertainties. These persistent operational dilemmas have led to the development of a dual-track coordination process, as illustrated in Figure 1. In the order-driven track, manufacturers generate a customized Bill of Materials (BOM) based on validated customer configurations, to execute production. Concurrently, aircraft manufacturers can adopt a two-stage strategy. The forecast-guided track enables engineering teams to establish pre-production baselines through General-Configuration Aircraft (GCA) predefined by market pattern mining, documented in default BOM pro tocols. This pre-configured inventory subsequently incorporates structural or interior decoration modifications after customer orders are confirmed.  
General-Configuration Aircraft (GCA) is a production concept that offers aircraft with a specific or intermediate configuration and incomplete functions. In an ideal scenario, where the demands of customers can be clearly understood, a GCA-derived product should  
Figure 1: A dual-track coordination process for the production of commercial aircraft.  
be the greatest common divisor of all potential customers' needs. However, in practice, constraints such as regulations, safety, test flights, production capacity and efficiency, costs, and technology must be considered. A GCA often needs to perform outfield flights and partial production flight tests to meet subsequent modification requirements and deliveries to other places. Alternatively, to cope with uncertainty related to customer orders, manufacturers usually produce GCA, having equipment sufficient for safe operation but without cabin accessories, most of the associated equipment, and electronic devices.  
Based on the above discussions, a GCA must undergo additional dismantling and re-assembly to ultimately transform it into an aircraft meeting all the customer's specifications. Considering the entire process and workload distribution, it is expected that the major work will be completed in the main production phase, while less work will be done in the dismantling and re-assembly phase, to reduce the total manufacturing labor cost. Therefore, it is particularly important to determine the optimal GCA and continuously optimize it to minimize the overall manufacturing workload and cost.  
To optimize the production time and costs, an appropriate general configuration that leads to a minimal overall workload--including assembly, disassembly, and re-assembly--is critical. However, GCA are currently empirically determined. And it is difficult to consider factors--e.g., customer adaptability, costs at each stage, disassembly sequence, subsequent changes globally, etc.--globally. It is critical to develop an optimization approach to determining the optimal GCA, which can help overcome the problem of uncertain orders and reduce the overall production costs.  
The remainder of this paper is organized as follows. Section 2 reviews the related literature. The stochastic GCA optimization problem is formulated and the SAA approach is developed in Section 3. The results of the aircraft manufacturing industry application are presented in Section 4. Finally, Section 5 provides concluding remarks and some research perspectives.