Real Time Large Network Re-Scheduling

Quick Start Installation

You need to have installed conda and git (temporarily for FLATland). Then,

In order to run the experiments,

# create conda environment rsp
conda env create -f rsp_environment.yml

# activate the conda env (if you want to use a different env name, run conda env create -f rsp_environment.yml --name other-env-name)
conda activate rsp

# PYTHONPATH
export PYTHONPATH=$PWD/src/python:$PWD/src/asp:$PYTHONPATH

# run pipeline (see section "Getting Started with Experiments" below)
python src/python/rsp/rsp_overleaf_pipeline.py

# ..... do some development....

# (optionally) update the conda env if rsp_environment.yml was modified
conda env update -f rsp_environment.yml

# run tests
python -m pytest

Setup Jupyter Notebooks

Taken from this post, this is a short introduction on how to use Jupyter Notebooks with git.

Start by installing the jupytext extensions

pip install jupytext --upgrade

and make the accessible by your notebooks in the conda env by installing (Guide)

conda install nb_conda

Generate a Jupyter config, if you don’t have one yet, with jupyter notebook --generate-config edit .jupyter/jupyter_notebook_config.py and append the following:

c.NotebookApp.contents_manager_class="jupytext.TextFileContentsManager"
c.ContentsManager.default_jupytext_formats = ".ipynb,.Rmd"

and restart Jupyter, i.e. run

jupyter notebook

Note: .jupyter is mostly present in your home directory.

Open an existing notebook or create a new one. Disable Jupyter’s autosave to do round-trip editing, just add the following in the top cell and execute.

%autosave 0

You can edit the .Rmd file in Jupyter as well as text editors, and this can be used to check version control changes.

Cloning the repo and create notebook

Open the .Rmd file in jupyter from its file browser. You can use the .Rmd file directly but it will not persist output between sessions, so we are gonna create a jupyter notebook.

Click File->Save (Cmd/Ctrl+S).
Close the .Rmd file (File->Close and Halt)

Now open the .ipynb in Jupyter. Start editing and saving. Your .Rmd file will keep updating itself.

Pre-commit hook

In order to run pre-commit hooks when you run git commit on the command line

conda activate rsp
conda install -c conda-forge pre-commit
pre-commit install

# test the pre-commit
pre-commit run --all

The pre-commit is only run on the files changed.

Details:

pre-commit. A framework for managing and maintaining multi-language pre-commit hooks.

Automatic mpeg conversion of FLATland

In order to have automatic mpeg conversion, we use the python-wrapper ffmpeg-python. For this to work, ffmpeg must be installed and on the PATH.

Getting Started with Experiments

You should only need three files:

src/python/rsp/rsp_overleaf_pipeline.py: defines parameters of your experiments
src/python/rsp/utils/global_data_configuration.py: defines data location Here, you can define parameters on three levels:

level	parameter range data structure	code of `src/python/rsp/pipeline/rsp_pipeline.py`	deterministic (yes/no)
infrastructure	`InfrastructureParametersRange`	`generate_infras_and_schedules`	yes
schedule	`ScheduleParametersRange`	`generate_infras_and_schedules`	no
re-schedule	`ScheduleParametersRange`	`run_agenda`	no

The Cartesian product of parameter settings at all three levels defines an experiment agenda of experiments. The infrastructure level is deterministic, the schedule and re-schedule level are not deterministic; examples:

if you choose three sizes and two seeds for infrastructure generation, this will produce 6 infrastructure
if you choose two seeds for schedule generation, this will produce 2 schedules for each infrastructure
if you choose two seeds for re-scheduling, this will run two re-scheduling experiments for every pair of infrastructure and schedule

In addition, it is possible to run multiple agendas with different solver settings (these are not part of parameter ranges, since these settings are categorical and not quantitative).

The data layout will look as follows:

.
├── h1_2020_10_08T16_32_00
│   ├── README.md
│   ├── h1_2020_10_08T16_32_00_baseline_2020_10_14T19_06_38
│   │   ├── data
│   │   │   ├── err.txt
│   │   │   ├── experiment_2064_2020_10_14T19_07_39.pkl
│   │   │   ├── experiment_2064_2020_10_14T19_07_39.csv
│   │   │   ├── experiment_2067_2020_10_14T19_07_41.pkl
│   │   │   ├── experiment_2067_2020_10_14T19_07_41.csv

│   │   │   ├── experiment_2961_2020_10_15T05_49_18.pkl
│   │   │   ├── experiment_2961_2020_10_15T05_49_18.csv
│   │   │   └── log.txt
│   │   ├── experiment_agenda.pkl
│   │   └── sha.txt
    └── infra
│       └── 000
│           ├── infrastructure.pkl
│           ├── infrastructure_parameters.pkl
│           └── schedule
│               ├── 000
│               │   ├── schedule.pkl
│               │   └── schedule_parameters.pkl
│               ├── 001
│               │   ├── schedule.pkl
│               │   └── schedule_parameters.pkl
│

The pkl files contain all results (required for detailed analysis notebook), whereas csv files contain only tabular information (as required by computation times notebook). See below use case 3 on how to generate pkl if you only have csv.

Experiment results are gathered in ExperimentResultsAnalysis and then expanded for analysis into ExperimentResultsAnalysis/ExperimentResultsOnlineUnrestricted.

Here's an overview of the experiment results data structures and where they are used:

location	data structure
pkl	`ExperimentResults` (unexpanded)
in Memory only	`ExperimentResultsAnalysis`/`ExperimentResultsOnlineUnrestricted` with `dict`s
csv/DataFrame	`ExperimentResultsAnalysis`/`ExperimentResultsOnlineUnrestricted` without columns of type `object`.

Here's the main part of src/python/rsp/rsp_overleaf_pipeline.py:

    rsp_pipeline(
        infra_parameters_range=INFRA_PARAMETERS_RANGE,
        schedule_parameters_range=SCHEDULE_PARAMETERS_RANGE,
        reschedule_parameters_range=RESCHEDULE_PARAMETERS_RANGE,
        base_directory="PUBLICATION_DATA",
        experiment_output_base_directory=None,
        experiment_filter=experiment_filter_first_ten_of_each_schedule,
        grid_mode=False,
        speed_data={
            1.0: 0.25,  # Fast passenger train
            1.0 / 2.0: 0.25,  # Fast freight train
            1.0 / 3.0: 0.25,  # Slow commuter train
            1.0 / 4.0: 0.25,  # Slow freight train
        },
    )

It consists of infrastructure and schedule generation (infra subfolder) and one or more agenda runs (h1_2020_08_24T21_04_42_baseline_2020_10_12T13_59_59 subfolder for the run with baseline solver settings).

See the use cases below for details how to use these options of generate_infras_and_schedules and run_agenda.

Use case 1a: run all three levels

Configure INFRAS_AND_SCHEDULES_FOLDER in src/python/rsp/utils/global_data_configuration.py to point to the base directory for your data.

INFRAS_AND_SCHEDULES_FOLDER = "../rsp-data/h1_2020_10_08T16_32_00"

Infrastructures will be generated into a subfolder infra under this base folder. In addition, if you comment out the argument

# experiment_output_base_directory=...

the experiment agenda will also get a new timestamped subfolder here; if you uncomment the argument, you will need to define

BASELINE_DATA_FOLDER = "../rsp-data/h1_2020_10_08T16_32_00/h1_2020_10_08T16_32_00_baseline_2020_10_21T18_16_25"

appropriately.

Use case 1b: only use baseline solver settings

Comment out calls to rsp_pipeline you don't need in rsp_pipeline_baseline_and_calibrations.

Use case 2a: you've aborted scheduling and want to run experiments on the schedules you already have

Comment out generate_infras_and_schedules(...) in rsp_pipeline. The agenda will only contain experiments for the existing schedules.

Use case 2b: you've aborted experiments and want to run a certain subset of experiments into the same data folder

Configure BASELINE_DATA_FOLDER in src/python/rsp/utils/global_data_configuration.py to point to the location you want to have your experiments in; this will be a subfolder of your base directory for data. In order to apply a filter on the agenda, you will need to give a different filter:

def experiment_filter_first_ten_of_each_schedule(experiment: ExperimentParameters):
    return experiment.re_schedule_parameters.malfunction_agent_id < 10 and experiment.experiment_id >= 2000

if __name__ == "__main__":
    ...
    rsp_pipeline_baseline_and_calibrations(
        ...
        experiment_filter=experiment_filter_first_ten_of_each_schedule,
        ...
    )

Use case 2c: you want to generate more schedules after you've already run experiments

In this case, an agenda has already been put to file that needs to be extended. You will need to tweak:

Define an appropriate filter (see experiment_filter_first_ten_of_each_schedule) for the missing experiments (they will have larger experiment ids than so far)
Run scheduling with the same INFRAS_AND_SCHEDULES_FOLDER as before; this will add the missing schedules incrementally;
Use a new BASELINE_DATA_FOLDER for running the experiments. Be sure you use the same parameters as before.
Copy the older experiments to the new location.

Use case 3: you have generated data with `csv_only=True` and want to generate the full data for some experiments

Define a filter and re-run the agenda from the output directory with csv_only=False:

    def filter_experiment_agenda(params: ExperimentParameters):
        return params.experiment_id == 0

    run_experiment_agenda(
        experiment_base_directory="../rsp-data/my-agenda",
        experiment_output_directory="../rsp-data/my-agenda/my-run",
        csv_only=False,
        filter_experiment_agenda=filter_experiment_agenda,
    )

The agenda will be read from the experiment_output_directory.

For a full example, see test_rerun_single_experiment_after_csv_only().

Use case 4: you want to re-rerun the same agenda

Make a new run directory: mkdir -p ../rsp-data/my-agenda/my-new-run
Copy the old agenda from the old to the new run directory: cp ../rsp-data/my-agenda/my-old-run/experiment_agenda.pkl ../rsp-data/my-agenda/my-new-run

Run the agenda from a new main or from detailed_experiment_analysis.Rmd with run_experiment = True with the following call:

 run_experiment_agenda(
     experiment_base_directory="../rsp-data/my-agenda",
     experiment_output_directory="../rsp-data/my-agenda/my-new-run",
     csv_only=False,
     filter_experiment_agenda=filter_experiment_agenda,
 )

Caveats:

Be sure to have git-lfs data checked out (see README.md in rsp-data)
Experiments that already have run (.pkl present, not only .csv) will be skipped. Re-running such an experiment is not supported (just remove the .pkl as a workaround).
The ERROR SUMMARY at the end lists all errors, not only those from the re-run. Check the dates!

Coding Guidelines

See CODING.md.

Cite

Please cite this repo along with the pre-print:

@misc{nygren2023scope,
      title={Scope Restriction for Scalable Real-Time Railway Rescheduling: An Exploratory Study},
      author={Erik Nygren and Christian Eichenberger and Emma Frejinger},
      year={2023},
      eprint={2305.03574},
      archivePrefix={arXiv},
      primaryClass={math.OC}
}

Paper with an extended technical appendix (revision 2023-05-05)

Disclaimer

Authors:

Christian Eichenberger
Erik Nygren
Adrian Egli
Christian Baumberger

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README.md

README.md

Real Time Large Network Re-Scheduling

Quick Start Installation

Setup Jupyter Notebooks

Cloning the repo and create notebook

Pre-commit hook

Automatic mpeg conversion of FLATland

Getting Started with Experiments

Use case 1a: run all three levels

Use case 1b: only use baseline solver settings

Use case 2a: you've aborted scheduling and want to run experiments on the schedules you already have

Use case 2b: you've aborted experiments and want to run a certain subset of experiments into the same data folder

Use case 2c: you want to generate more schedules after you've already run experiments

Use case 3: you have generated data with `csv_only=True` and want to generate the full data for some experiments

Use case 4: you want to re-rerun the same agenda

Coding Guidelines

Cite

Disclaimer

Authors:

Files

README.md

Latest commit

History

README.md

File metadata and controls

Real Time Large Network Re-Scheduling

Quick Start Installation

Setup Jupyter Notebooks

Cloning the repo and create notebook

Pre-commit hook

Automatic mpeg conversion of FLATland

Getting Started with Experiments

Use case 1a: run all three levels

Use case 1b: only use baseline solver settings

Use case 2a: you've aborted scheduling and want to run experiments on the schedules you already have

Use case 2b: you've aborted experiments and want to run a certain subset of experiments into the same data folder

Use case 2c: you want to generate more schedules after you've already run experiments

Use case 3: you have generated data with csv_only=True and want to generate the full data for some experiments

Use case 4: you want to re-rerun the same agenda

Coding Guidelines

Cite

Disclaimer

Authors:

Use case 3: you have generated data with `csv_only=True` and want to generate the full data for some experiments