# Integrated Digital-Twin-Based Decision Support System for
## 1 Introduction  
Relocatable modular buildings (RMBs) have gained widespread recognition for their flexibility, scalability, and mobility, making them a practical solution for addressing fluctuating construction demands. These structures can be rapidly assembled, disassembled, and relocated, offering notable efficiency advantages across various sectors, including education, healthcare, and disaster relief. However, as RMB projects scale up and the number of modules grows, managing the allocation and reuse of these modules becomes increasingly complex, potentially offsetting their inherent benefits [1]. Indeed, sustainable facility maintenance and resource allocation planning are fundamental to improvingbuilding operational efficiency [2; 3]. To maintain the effectiveness of RMB projects, module lifecycle management and logistics must be carefully planned, factoring in module conditions, maintenance, and strategic reuse. Despite the growing adoption of RMBs, the increasing complexity of projects, driven by the scale and volume of modules, has highlighted critical inefficiencies inherent in conventional manual allocation methods [4; 5]. These methods, characterized by their manual and fragmented nature, struggle to address the diverse and dynamic conditions of modules. Factors such as maintenance histories, current conditions, and compatibility with new project requirements often go unaccounted for, resulting in suboptimal module selection, inflated transportation costs, extended project timelines, and increased idle periods for modules [6; 7]. Moreover, RMB introduces distinct operational challenges due to its inherent mobility and reuse-oriented characteristics. The frequent necessity to rear-range modules--whether for new installations, reconfigurations, or relocations--further compounds these complexities [8]. Maximizing module utilization requires minimizing downtime and reducing prolonged storage periods, as extended inactivity increases the risk of degradation and market value loss [9]. In geographically dispersed projects, the importance of optimized logistics planning is magnified, as inefficient transportation routes can escalate costs and amplify environmental impacts [10]. Without integrating static data (e.g., specifications and maintenance records) with dynamic data (e.g., real-time locations and status), traditional manual methods are insufficient to address the multifaceted challenges of RMB management [11]. Given these limitations, a more advanced and systematic approach is essential for optimizing module allocation and streamlining RMB operations. To address these challenges, Digital Twin (DT)-based systems have emerged as a transformative solution for enhancing facility management and logistics coordination in modular construction (MC). DT technology enables the real-time synchronization of physical assets and their digital counterparts, leveraging integrated technologies such as Building Information Modeling (BIM), the Internet of Things (IoT), and Geographic Information Systems (GISs) [12]. Previous studies have demonstrated the effectiveness of DT-based systems in improving decision-making processes, optimizing resource utilization, and enhancing lifecycle management in various construction applications. For instance, DT-enabled platforms have been successfully applied in facility management, logistics planning, and predictive maintenance, enabling stakeholders to anticipate and mitigate operational challenges [13; 14; 15]. However, while DT-based solutions have been widely explored in general construction logistics and supply chain management, their potential for optimizing RMB module allocation and reuse remains underexplored. Given the increasing complexity of RMB projects, there is a pressing need for a DT-enabled decision support system that can provide real-time data integration and decision making for module selection, allocation, and transportation planning. This study aims to develop a DT-based decision support system to optimize the allocation and management of RMB modules.