eflips-opt 1.0.2

A collection of pre-simulation optimization algorithms for eFLIPS.

eflips-opt

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Part of the eFLIPS/simBA list of projects.

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This repository contains code for optimizing (electric) bus networks. The following optimization problems are considered:

• Depot-Rotation Matching: Given a set of depots and a set of rotations, find the optimal assignment of rotations to depots.
• Simplified Electric Vehicle Scheduling Problem: Given a set of trips, find the rotation of electric buses that minimizes the total number of vehicles needed to serve all trips.

Installation

1. Clone the repository

   git clone [e-mail]:mpm-tu-berlin/eflips-opt.git
   

2. Install the packages listed in poetry.lock and pyproject.toml into your Python environment. Notes:

   • The suggested Python version is 3.11.*, it may work with other versions, but this is not tested.
   • Using the poetry package manager is recommended. It can be installed according to the instructions listed here.

3. In order to use the Depot-Rotation Matching, it is recommended to install the

4. Gurobi solver. The solver can be installed by following the instructions listed here. The solver can be used with a license for academic purposes. If you do not have a license, you can request one here.

Usage

Depot-Rotation Matching

Example scripts for the Depot-Rotation Matching problem can be found in the examples/depot_rotation_matching directory. The general usage is as follows:

from eflips.opt.depot_rotation_matching import DepotRotationOptimizer

# Initialize the optimizer
optimizer = DepotRotationOptimizer(session, SCENARIO_ID)

# Create a depot input. Both existing and new depots can be added.
user_input_depot = [
   {
       "depot_station": 103281393,
       "capacity": 300,
       "vehicle_type": [84, 86, 87, 90],
   },  # Indira-Gandhi-Str
   {
       "name": "Suedost",
       "depot_station": (13.497371828836501, 52.46541010322369),
       "capacity": 260,
       "vehicle_type": [82, 84, 85, 86, 87, 90],
   },  # Südost
]
optimizer.get_depot_from_input(user_input_depot)
optimizer.data_preparation()
optimizer.optimize(time_report=True)
optimizer.write_optimization_results(delete_original_data=True)

Simplified Electric Vehicle Scheduling Problem

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This section is not current, it will be updated soon.

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Example scripts for the Simplified Electric Vehicle Scheduling Problem can be found in the examples/simplified_electric_vehicle_scheduling_problem directory. It creates a graph of all possible trips and their possible connections. The minimum number of rotations is now a minimum path cover of the graph. This is what the minimum_path_cover_rotation_plan() function does. Its result however tries to make rotations as long as possible, ignoring the fact that the bus might need to be recharged (this is useful for identifying terminus stations to electrify though.

In order to find a rotation plan that considers the battery state of the bus, the soc_aware_rotation_plan() function can be used. It iterates over the solution of the minimum_path_cover_rotation_plan() function and heuristically finds a good rotation plan that considers the battery state of the bus.

Known issues

• The result is not optimal in terms of minimum waiting time at each terminus. This could be improved by fine-tuning the candidate selection process in the create_graph_of_possible_connections() function.
• The result is not stable, changing with each new python process. This is due to there being multiple minimum path c covers of the graph. This could possibly be fixed by iterating over all minimum path covers and selecting the one that minimizes the dwell durations or the energy consumption of each bus. See here for a possible implementation (but be careful of the combination explosion)

Usage

# You probably want to create one schedule for each vehicle type, e.g. not connect 12m buses' trips with 18m buses' trips
trips_by_vt = passenger_trips_by_vehicle_type(scenario, session)
for vehicle_type, trips in trips_by_vt.items():
   # The connection graph is what the schedule will be based on
   graph = create_graph_of_possible_connections(trips)
   
   # The minimum path cover rotation plan just makes rotations as long as possible
   rotation_graph = minimum_path_cover_rotation_plan(graph)
   
   # The soc aware rotation plan tries to make rotations that consider the battery state of the bus
   soc_aware_graph = soc_aware_rotation_plan(graph, soc_reserve=SOC_RESERVE)
   
   # The rotation plan can be visualized
   visualize_with_dash_cytoscape(soc_aware_graph)

Development

We utilize the GitHub Flow branching structure. This means that the main branch is always deployable and that all development happens in feature branches. The feature branches are merged into main via pull requests.

We use black for code formatting. You can use pre-commit to ensure the code is formatted correctly before committing. You are also free to use other methods to format the code, but please ensure that the code is formatted correctly before committing.

Please make sure that your poetry.lock and pyproject.toml files are consistent before committing. You can use poetry check to check this. This is also checked by pre-commit.

License

This project is licensed under the AGPLv3 license - see the LICENSE file for details.

Funding Notice

This code was developed as part of the project eBus2030+ funded by the Federal German Ministry for Digital and Transport (BMDV) under grant number 03EMF0402.