## 4 Performance Evaluation of Proposed Scheme  
### Simulation Environment  
The dataset employed in this study is sourced from the Ministry of Transportation, Iraq which is the General Company for (land, train, water, and air transport) [30]. The temporal scope of the data spans from 2017 to 2022, offering a comprehensive insight into the dynamics of the transportation sector over a six-year period. For the General Company for Navy Transport, detailed emissions data were collected, encompassing two distinct fuel types: 'Diesel(oil)' and 'Gasoline'. This information includes the quantity of fuel consumedper liter, carbon dioxide emissions per ton (CO\({}_{2}\)), hydrocarbon emissions per ton (HC), and nitrogen oxide emissions per ton (NO\({}_{2}\)). The dataset reveals a nuanced pattern of emissions, shedding light on the environmental impact of the company's operations, particularly in terms of nitrogen oxides (Nox), sulfur dioxide (SO\({}_{2}\)), and methane (CH\({}_{4}\)). Furthermore, the dataset encompasses the operational and environmental metrics of Iraqi Airways, covering the years 2017 to 2021. Key parameters include the number of trips, and gas emissions measured in international tons. The dataset indicates a significant growth in the number of trips over the years, potentially indicative of the airline's expansion. Simultaneously, the gas emissions data suggest a considerable environmental impact associated with the airline's operations. Additionally, the dataset extends to the transportation sector beyond air travel, incorporating data from trucks, railways, and airplanes. For trucks, the dataset includes monthly quantities transported, distances covered, and associated costs. Railway data involve monthly quantities transported, revenues, ton-kilometers, and total costs. Similarly, airplane data incorporate loaded and unloaded weights, loaded and unloaded costs, and total costs.  
These data serve as the foundation for a sophisticated approach integrating DEA and HMM in the context of multimodal transportation within the supply chain industry. To tackle the intricacies of multimodal transportation optimization, the dataset undergoes strategic segmentation into three distinct groups. This segmentation facilitates an iterative refinement of our algorithm, a critical step in achieving an optimal solution. The incorporation of DEA, a non-parametric method, allows for the assessment of relative efficiencies among DMUs consuming multiple inputs to produce multiple outputs. This evaluation is pivotal for identifying optimal transportation modes within the supply chain. Concurrently, HMM, a probabilistic model, captures the dynamic nature of the transportation network. By modeling the underlying hidden states and observable emissions, HMM provides valuable insights into the evolving conditions of the transportation system. The integration of DEA and HMM enhances the model's capabilities, allowing stakeholders to make informed decisions based on a holistic evaluation of efficiency, cost-effectiveness, and the dynamic nature of the transportation network. We have given some of the simulation parameters which are specifically used in different transportation modes of supply chain networks in Iraq in Table 1.