# Multi-Agent Simulation Approach for Modular Integrated Construction Supply Chain
## 4 Results and Discussion
### Discussion  
In this section, we compare the results obtained for the two scenarios. The analysis of the results as shown in Figure 9 from both scenarios highlights several key points. In both scenarios, the total cost initially decreases as the number of vehicles per supplier increases, reaching a minimum at three vehicles per supplier. Beyond this point, further increasing the number of vehicles leads to higher total costs. This trend suggests that there is an optimal number of vehicles for each supplier that balances the fixed and variable costs while minimizing penalties. Using three vehicles per supplier is the optimal configuration. This setup achieves the lowest total cost by eliminating penalties. This result underscores the importance of optimizing the number of logistical resources to ensure cost efficiency and timely project completion. This finding aligns with the work of [57], which highlights the significance of optimal vehicle allocation in prefabricated construction logistics. Their study demonstrates that an automated approach to vehicle allocation can substantially reduce the transportation costs associated with inefficient vehicle use. Moreover, we can observe that Scenario 2 with the fully MiC process generally results in lower total costs compared to Scenario 1 across various vehicle configurations. This indicates that the MiC approach offers better cost savings due to centralized assembly and optimized transportation logistics. This finding aligns with the results of comparative studies which highlight that modular construction can lead to a 10-25% decrease in construction costs. One of the primary factors contributing to these cost savings is reduced material transportation, as components are produced off-site and then assembled on-site, leading to more efficient logistics and lower overall expenses [58]. On the other side, the presence of penalties significantly affects the total cost when the number of vehicles is insufficient to meet demand promptly. In both scenarios, configurations with fewer than three vehicles per supplier incur penalties, which substantially increase the total cost. This highlights the critical impact of ensuring adequate logistical capacity to avoid project delays. This observation is consistent with findings in the literature, which identify poor supply chain capacity, including delays in the delivery of modular components, as critical risk factors that can derail the success of MiC projects [59]. Figure 9: Comparison of the total costs for different numbers of vehicles per supplier. A notable observation is that the difference in total costs between the two scenarios becomes constant beyond the optimal number of vehicles. This phenomenon can be attributed to several factors. Once the number of vehicles per supplier reaches the optimal point of three vehicles, penalties for delays are eliminated in both scenarios. This means that any additional vehicles do not contribute to reducing penalties further, as there are no penalties left to reduce. Furthermore, the variable costs, which depend on the distance traveled and the number of trips, stabilize once the logistics operations reach optimal efficiency. Both scenarios achieve a similar level of efficiency in terms of transportation distance and frequency, leading to a constant difference in variable costs. Additionally, beyond the optimal point, the additional fixed costs of deploying more vehicles dominate the total cost structure. These fixed costs are similar in both scenarios, leading to a constant difference in total costs. The fixed costs per vehicle per day become the primary factor of increased costs, and since they are applied uniformly across both scenarios, the difference remains constant. The findings from this study provide valuable insights for construction project managers and logistics planners in the MiC industry. Understanding the optimal number of vehicles per supplier helps in making informed decisions about resource allocation, ultimately leading to cost savings and timely project completion.