# The Environmental Impact of Inland Empty Container Movements Within Two-Depot Systems
## 4. ABM Formulation
### Experimental Design  
To assess the performance of the proposed ABM and highlight its added value, a baseline scenario was developed to emulate current empty movement practices within two-depot systems. A baseline scenario was implemented within the same agent-based framework to reflect the same logistics operations in empty container movements. This scenario replicates centralized, rule-based logistics practices, in contrast to the decentralized  
Figure 2: The NetLogo interface of truck movements for empty containers. and adaptive behavior of the full ABM configuration. _Truck_ agents operate with static shortest path routing and dispatch only when a container request is received, without anticipatory routing or emissions considerations. _Depots_ function independently, replenishing stock at fixed intervals, and fulfilling customer requests on a First-In, First-Out (FIFO) basis. No dynamic reallocation of stock or coordination between depots is performed. _Customers_ submit periodic container requests and remain passive until fulfilment. _Ports_ operate with static schedules and serve agents in arrival order. _Links_ are treated equally regardless of time or usage. A baseline scenario representing current logistics practices was constructed based on prevailing logistics practices, particularly the use of direct truck routing without intermediary depots [58]. Moreover, its outcomes match anticipated trends in systems lacking depot coordination, supporting its credibility for meaningful comparison with the proposed ABM scenario [59; 60]. Key performance metrics, such as delivery times, unmet requests, CO2 emissions, and depot utilization, are used to assess the improvements enabled by the proposed model. Furthermore, the NetLogo model uses Behavior Space to conduct sensitivity analysis by varying the following parameters and testing a serial variable by varying parameters and recording outputs. The experimental design focuses on analyzing the effect of empty container truck movements under two different scenarios: Scenario A (baseline without inland depots) and Scenario B (proposed ABM with inland depots) under the same input conditions. The baseline scenario was constructed to emulate existing centralized practices, where empty containers are delivered directly without intermediate depot coordination or environmental optimization, common in regional container transport systems such as those in the Gulf and East Africa [61]. Table 5 explains the main two key parameters that varied in the experiment. The first one determines whether empty containers are first stored in inland depots before being transported to customers or if they are delivered directly to customer locations. The second one explores the impact of different demand intervals, simulating how varying container request frequencies affect movement performance. Hence, Behavior Space can provide insights for decision-making in reducing emissions and improving transport efficiency. As shown in Table 6, the model tracks several key metrics to evaluate performance. Total emission measures total CO2 emissions in kilograms, reflecting environmental impact, the counts of trucks actively transporting containers, the idle or empty trucks, which contribute to inefficiencies, the average waiting time for customers before receiving their containers, and the average unmet demand per customer. \begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Description** \\ \hline total emission & Total CO2 emissions (kg) \\ \hline count trucks with [loaded?] & Active trucks with containers \\ \hline count trucks with [not loaded?] & Idle/empty trucks \\ \hline mean [wait-days] of customers & Average customer waiting time \\ \hline mean [total-demand] of customers & Average unmet demand per customer \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Output metrics.