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OASIS v1.0.0

DOI PyPI version License: MIT

Description

This is a stable version of the OASIS activity-based framework. This repository includes the simulation code, based on the Python API of the CPLEX solver.

For more details, you can find the technical reports in the Literature folder, and the full documentation here.

Installation guide  

OASIS can be installed with the following command:

pip install oasisabm

We recommend creating a new environment using the provided requirements file to install the correct packages. NB: You need a valid CPLEX user license to use the solver, which can be obtained here.

Tutorial

An example of how to run the simulator is provided in the demo notebooks. Your main function should be added in the runner script, which you can run by typing the following command: python runner.py

Citation

If you found this repository useful, you can acknowledge the authors by citing:

  • Pougala, J., Hillel, T., and Bierlaire M. (2023) OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions, Transportation Research Part C: Emerging Technologies, vol. 155, p. 104291, DOI: https://doi.org/10.1016/j.trc.2023.104291

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oasis's Issues

feature request: drop cplex as the only possible solver

Would be good to support other solvers other than CPLEX, for a few reasons:

  1. The CPLEX python library can't be installed on an Apple Silicon device.
  2. CPLEX is a commercial solver. It effectively means OASIS isn't reliably testable or usable by others. Outside academia, you need to pay for a license. Access to CPLEX on academic high performance computing clusters is also not a given.

There are lots of python libraries that allow you to create an optimisation problem that can be solved with a range of solvers. E.g.:

  1. Pyomo (I would recommend using their kernel library as it reduces the overhead that Pyomo adds to generating the problem).
  2. linopy.

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