Integrated Digital-Twin-Based Decision Support System for Relocatable Module Allocation Plan: Case Study of Relocatable Modular School System

Relocatable modular buildings (RMBs) offer significant advantages, including flexibility, mobility, and scalability, making them ideal for temporary or rapidly changing scenarios. However, as the scale and quantity of RMB modules increase, their allocation across projects poses complex logistical challenges. Inefficiencies in traditional manual allocation methods, such as suboptimal module selection, increased transportation costs, and project delays, underscore the need for innovative solutions. This study develops a Digital Twin (DT)-based decision support system to optimize the allocation and management of RMB modules. The proposed framework integrates Building Information Modeling (BIM), Internet of Things (IoT), and Geographic Information Systems (GISs), enabling the real-time synchronization of physical assets with their digital counterparts. The DT framework incorporates real-time data acquisition, dynamic module condition assessments, and an algorithm-driven allocation process to streamline resource utilization and logistics planning. The system is validated through a case study of South Korea's first relocatable modular school system project, demonstrating its capability to optimize module allocation, reduce costs, and enhance lifecycle management. This study advances RMB management by offering a practical, data-driven approach, empowering facility managers to leverage real-time data for preventive maintenance, asset optimization, and sustainable resource utilization.