# Multi-Agent Simulation Approach for Modular Integrated Construction Supply Chain
## 1 Introduction
The detailed digital representations of BIM facilitate precise prefabrication and assembly, improving coordination and reducing errors. IoT enhances these capabilities by providing real-time data on the status and condition of components and modules during transportation and installation [15; 16; 17; 18]. Moreover, Artificial Intelligence (AI) is recognized for its potential to significantly improve MiC operations. AI can automate complex data management tasks and improve traceability throughout the MiC supply chain, providing stakeholders with real-time visibility of logistics operations. This transparency is essential for making informed decisions quickly, improving operational efficiency and reducing delays. AI can also facilitate predictive analytics, enabling potential delays or problems to be anticipated before they occur [8]. Figure 1: Progression of off-site construction techniques: evolution from basic prefabricated components to the advanced modular integrated construction (MiC). Recent works in the literature emphasize that MiC is not only about improving efficiency and productivity but also about fostering sustainability and environmental responsibility [3]. MiC supports the principles of the circular economy by promoting resource efficiency, reducing construction waste, and enabling the reuse and recycling of building components [19]. This is achieved through design for disassembly (DfD) and the use of recycled materials, which significantly lower the environmental impact of construction projects [20; 21]. By integrating circular economy strategies such as reducing, reusing, and recycling materials, MiC helps create a more sustainable and resource-efficient built environment [22]. Moreover, from a MiC supply chain perspective, optimizing components and modules logistics and adopting closed-loop supply chains can significantly minimize CO\({}_{2}\) emissions, as efficient transport logistics inherently reduce fuel consumption and emissions [23]. These practices align with the broader objectives of the 2030 Agenda for Sustainable Development adopted by the United Nations [24]. Despite the advancements in MiC methods, the literature reveals a notable lack of quantitative analyses concerning the cost-effectiveness and sustainability benefits of MiC [9; 25]. In addition, sustainability assessments are often qualitative rather than quantitative, limiting the ability of decision-makers to make comprehensive assessments based on solid empirical data [9]. In addition to the economic and environmental considerations, the literature indicates an absence of advanced, suitable models for optimizing transport planning and supply chain configurations in the context of MiC [3; 7]. In particular, there are gaps in strategies for efficiently managing the logistics of large, integrated modules requiring special handling and routing procedures. Stochastic programming appears to be a recommended method for filling these gaps [7]. This approach makes it possible to model the uncertainties and variabilities inherent in construction logistics, such as delivery delays, variable module sizes and fluctuating costs, thereby improving supply chain resilience. Moreover, the recent literature review in MiC identifies research opportunities across all phases of construction [3]. This includes the use of decision-making systems in design, and the adoption of Industry 4.0 technologies in module production for increased operational effectiveness. In module logistics, advanced models optimize the location of module fabrication centers and vehicle routes, enhancing performance while considering environmental and economical impacts. On-site, sophisticated planning models accelerate assembly and optimize project durations, demonstrating a shift towards more technologically integrated construction practices. To address these challenges, this paper explores the potential of multi-agent simulation using AnyLogic to enhance the MiC supply chain.