Internal Rail Logistics Simulation for the Port of Le Havre

Internal Rail Logistics Simulation for the Port of Le Havre

Problem:

The Port of Le Havre wanted a new multimodal terminal that would connect rail, river, and sea transport. The port needed to compare two internal rail logistics concepts: traditional locomotive-driven railcars and autonomous railcars. The goal was to evaluate costs, service quality, and operational feasibility before construction.

Solution:

The consultants built detailed simulation models using the AnyLogic Rail Library. Two separate models were created to represent passive and autonomous railcar operations. The models simulated rail traffic, cranes, container flows, schedules, space availability, and operational costs across the terminal.

Results:

  • Successfully compared passive vs. autonomous railcar operations.
  • Proved that autonomous cars were cheaper and more efficient.
  • Measured container dwell time and overall Quality of Service.

Problem

The Port of Le Havre, the largest container port in France, needed assistance with the construction of a new multimodal terminal. The new terminal would include the area where trains and river barges bring containers for further sea transportation. In this area, cranes move the containers from the carriers and load them onto supporting railcars that form shuttles, which then carry the containers to sea transports. The movement of these cars was the focus of the simulation model developed by The AnyLogic Company. The simulation model had to compare these two scenarios:

  1. Basic (using simple passive cars driven by locomotives)
  2. Advanced (using autonomous cars able to move without locomotives)

The objective was to measure the costs in each case, the Quality of Service (how long containers remain in the system), and possible improvements in the network structure of the terminal.

Solution

The AnyLogic Rail Library was used to model the transportation network. Movements of railcars, cranes, and other elements of the network were simulated in a low level of abstraction. The consultants had to create two separate models because the two scenarios contained very different logic. The models allowed the user to:

Outcome

The results included statistics collected for both scenarios. Costs were calculated for different elements of the network, such as locomotives, railcars, cranes, and dockers. The data on Quality of Service showed that autonomous cars were more efficient and cheaper than passive ones.

By using the simulation models, the customer could compare the two methods of organization of internal rail logistics, choose the optimal one, and estimate the needed amount of railcars.

The data obtained from the AnyLogic models allowed the customer to prove the feasibility of the terminal construction project to the potential investors.

Multimodal Terminal Simulation Multimodal Terminal Simulation


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