## 5 The Model's Implementation
### The Experimental Protocol  
The objective is to evaluate the ecological sustainability of logistical operations by monitoring CO\({}_{2}\) emissions. To replicate the dynamic processes of the MASS within a transportation hub, essential characteristics were defined. A detailed list of these parameters, organised by agent type, is provided in Table 4.  
Figure 4: Model interaction: 2 Stage-Opt optimisation and the MASS. The node agents' parameters include capacity, position, and type. Capacity describes the varying ability of different ports to accommodate incoming traffic. The position parameter is specifically set using predetermined grid positions, which creates the geographical structure of the simulation environment. Nodes are divided into two types, type 1 for docking stations for trucks and type 2 for ship ports, which define the precise functionality and operational focus of each node inside the hub. Truck agents are identified by their types, routes, starting points, and capacities. The type of each truck reveals its specifications and adaptability to various logistical duties. The routes given to trucks determine their movement patterns and operational reach within the network. Their initial positions on the grid are equally fixed, ensuring that all trucks start from the same point. Each truck's capacity is chosen so that it can suitably accommodate the cargo. Containers, the cargo units in our MASS, include criteria for occupation, arrival time, destination, and distance. Occupation represents each container's filled capacity upon arrival at the hub. The arrival time of the containers introduces variability into the flow and timing of cargo into the system. Destinations are randomly selected from a set of predefined alternatives, reflecting the logistical challenges and the distances that containers must travel from their point of entry to their final destinations. This case study is used to generate instances that enable tests of the proposed approach in theoretical scenarios based on real proportions. To evaluate theoretical scenarios of how decision-making structures affect the overall system performance by changing factors like the number of trucks and containers across instances, the varying parameters are shown in Table 5.  
On the other hand, the purpose of the KPIs is to evaluate the sustainability and effectiveness of the MASS across various operational designs. The KPIs defined are as follows:  
* CO2 emissions: This environmental KPI provides insight into the ecological impact of the system. The CO2 emissions are calculated using Equation (5). \[\text{CO}_{2}\text{ Emissions}=\sum_{j\in J}\left(d_{j}\times\text{EF}_{j}\times \frac{w_{j}}{W_{j}}\right) \tag{5}\] \begin{table}
\begin{tabular}{l l l} \hline \hline
**Agent Type** & **Parameter** & **Description** \\ \hline Port & Capacity & Assigned from 1 to 3 units \\  & Position & Set based on grid location, e.g., (60, 100) for ships \\  & Type & 1 for truck ports, 2 for ship ports \\ \hline Truck & Types & Selected from predefined types, e.g., (6), (3, 4), (8), etc. \\  & Routes & Assigned randomly highway routes like “A21”, “A16”, “A27”, or “A1” \\ \hline Container & Occupation & Randomly between 0\% and 100\% \\  & Arrival time & Randomly generated to simulate entry \\  & Destination & Randomly assigned from predefined destinations \\  & Distance & Corresponds to a destination with a 10–40 range \\  & Size & Corresponds to a 1.2 m container size based on the \\  & & PI’s foundations \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters for MASS agents. \begin{table}
\begin{tabular}{c c} \hline \hline
**Input Parameter** & **Values** \\ \hline Number of trucks & [30; 35; 40] \\ Number of containers & [26; 28; 30; 35] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Variable input parameters for the MASS in theoretical scenarios.