The goal of this software is to determine system optima of intermodal transportation networks. To do so, this software provides the column generation approach proposed in the paper including our pricing filter and our A-star approach to solve the pricing problems. A preprint of this paper is available on arXiv.
This software contains four folders: data, Generator, Results, Solver.
data: contains data for the case study of our paper.Generator: generates a problem instance for our case study viaìnstance_generator.Results: contain the results of our case studySolver: solves the instance viacolumn_generation.py
The results in the paper were generated by this software that had been carried out using Python 3.8.11 and Gurobi 9.5 on a desktop computer with Intel(R) Core(TM) i9-9900, 3.1 GHz CPU and 16 GB of RAM, running Ubuntu 20.04.
To replicate the results of an instance of our case study run python ./start_run.py [mode] [passengers] [seed] [Filter On] [A-star used].
The following input arguments are valid:
| Argument | Inputs |
|---|---|
| mode | s = subway, b = bus, sbt = subway-bus-tram |
| passengers | subway = {132, 308, 486, 662}, bus = {2632, 7896, 13160, 18424, 23688}, bus-subway-tram = {6255, 18765, 31275, 43785, 56295} |
| seed | 0-9 |
| Filter On | True, False |
| A-star used | True, False |
Running an instance on the bus network with 2632 passengers and seed 0 with our pricing filter active and the A-star algorithm can be done via
python ./start_run.py b 2632 0 True True