This repository contains a high-performance AIMMS example for Vessel Scheduling and Route Optimization. It demonstrates how to manage complex maritime logistics, delivering large cargoes using oil tankers while minimizing operational and spot-market costs.
Maritime scheduling is a combinatorial challenge where the number of possible routes grows exponentially with the number of vessels and cargoes. This model solves:
- Cost Optimization: Minimizing the sum of operational costs, vessel idle costs, and spot-market penalties.
- Route Generation: Dynamically generating valid routes based on cargo loading windows and vessel availability.
- Strategic Allocation: Deciding whether to assign a cargo to a time-chartered vessel or leave it for the voyage-charter (spot) market.
To get the most out of this model, including the details on route generation logic and Python integration, we highly recommend our dedicated guide:
👉 Read the Full Article: Vessel Scheduling
- AIMMS: You will need AIMMS installed to run the model. Download the Free Academic Edition here.
- Python: Python 3.11+ is required to run the
searouteandpandasintegration. - WebUI: This model is optimized for the AIMMS WebUI, featuring editable Gantt Charts and data-dependent CSS styling.
- Download the Release: Go to the Releases page and download the latest
.zip. - Setup Python: Ensure your Python environment has
searouteandpandasinstalled. - Open the Project: Launch the
.aimmsfile. - Generate & Solve: Use the WebUI status bar to first generate the maritime routes and then solve the mathematical optimization.
This example is maintained by the AIMMS User Support Team.
- Found an issue? Open an issue.
- Questions? Reach out via the AIMMS Community.
Maintained by the AIMMS User Support Team. We optimize the way you build optimization.