Smart Planning of Intermodal Rail Freight in Switzerland

With Switzerland's ambition to reach net-zero greenhouse gas emissions by 2050, improving the efficiency and sustainability of the freight transport sector is a key priority. Intermodal rail freight, as a low-emission transport alternative, plays an important role in this transition. However, optimal planning of such systems requires advanced models capable of integrating user behavior with operational constraints. This thesis uses Mixed-Integer Linear Programming (MILP) models to optimize intermodal rail networks in Switzerland. The MILP model allows decision-makers to configure intermodal systems in ways that maximize overall efficiency—considering variables such as pricing, frequency, and capacity—while accounting for user preferences as well. This thesis focuses exclusively on developing and applying a MILP model for planning and optimizing intermodal rail freight systems in Switzerland, incorporating inputs that capture the heterogeneity of shipper preferences.

With Switzerland's ambition to reach net-zero greenhouse gas emissions by 2050, improving the efficiency and sustainability of the freight transport sector is a key priority. Intermodal rail freight, as a low-emission transport alternative, plays an important role in this transition. However, optimal planning of such systems requires advanced models capable of integrating user behavior with operational constraints. This thesis uses Mixed-Integer Linear Programming (MILP) models to optimize intermodal rail networks in Switzerland. The MILP model allows decision-makers to configure intermodal systems in ways that maximize overall efficiency—considering variables such as pricing, frequency, and capacity—while accounting for user preferences as well. This thesis focuses exclusively on developing and applying a MILP model for planning and optimizing intermodal rail freight systems in Switzerland, incorporating inputs that capture the heterogeneity of shipper preferences.

1. Develop a Mixed-Integer Linear Programming Model 2. Simulate and Optimize Rail Freight Operations: Apply the MILP model to optimize pricing, scheduling, terminal usage, and resource allocation across selected Swiss corridors. 3. Provide operational insights to intermodal operators and policymakers on how to efficiently adjust pricing, frequency, and routing in alignment with shipper’s preferences and system constraints.

1. Develop a Mixed-Integer Linear Programming Model
2. Simulate and Optimize Rail Freight Operations: Apply the MILP model to optimize pricing, scheduling, terminal usage, and resource allocation across selected Swiss corridors.
3. Provide operational insights to intermodal operators and policymakers on how to efficiently adjust pricing, frequency, and routing in alignment with shipper’s preferences and system constraints.

We are looking for a highly motivated student with excellent analytical skills. While a background in mathematics, operations research, computer science, engineering, management, or related fields is helpful, it is not mandatory. Experience in programming is beneficial. This opportunity also offers the possibility of contributing to a publication. Please include your CV, transcript of records, and a brief motivation letter. If you have any questions, please feel free to contact Dr.-Ing. Jing Shan at jing.shan@ethz.ch. We look forward to receiving your application!

We are looking for a highly motivated student with excellent analytical skills. While a background in mathematics, operations research, computer science, engineering, management, or related fields is helpful, it is not mandatory. Experience in programming is beneficial. This opportunity also offers the possibility of contributing to a publication. Please include your CV, transcript of records, and a brief motivation letter.
If you have any questions, please feel free to contact Dr.-Ing. Jing Shan at jing.shan@ethz.ch. We look forward to receiving your application!