fliscopt

Algorithms for flight scheduling optimization.

Science Score: 67.0%

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Keywords

genetic-algorithm hacktoberfest hacktoberfest2021 heuristic-search-algorithms optimization-algorithms pypy3 python python3

Last synced: 4 months ago · JSON representation ·

Repository

Algorithms for flight scheduling optimization.

Basic Info

Host: GitHub
Owner: Agrover112
License: mit
Language: Python
Default Branch: master
Homepage: https://pypi.org/project/fliscopt/
Size: 70.3 MB

Statistics

Stars: 45
Watchers: 2
Forks: 12
Open Issues: 7
Releases: 0

Topics

genetic-algorithm hacktoberfest hacktoberfest2021 heuristic-search-algorithms optimization-algorithms pypy3 python python3

Created almost 5 years ago · Last pushed over 2 years ago

Metadata Files

Readme Changelog Contributing Funding License Code of conduct Citation

Fliscopt

Stars Forks License Issues

FLIght SCheduling OPTimization 🛫 or fliscopt is a simple optimization library for flight scheduling and related problems in the discrete domain. Flight scheduling opt here refers to generating schedules of optimal cost for round-trips.

The library supports plotting, asynchronous multiprocessing, and unimodal optimization benchmarks. The following repository contains code for the paper "XYZ". The experiments were performed in PyPy3.7 and CPython 3.8.10.

Following algorithms have been implemented and test as of date:

Algorithms: - Hill Climbing - Random Search - Simulated Annealing - Genetic Algorithm - Genetic Algorithm in Reverse Mode - Genetic Algorithm with Reversals - Genetic Algorithm with Random Search as a Reversal/Reverse Process - Iterated Chaining

Take a look at the docs or the Appendix for more details.

Getting Started

Install the library using pip: bash pip install fliscopt Or for unreleased versions: bash pip install git+https://github.com/Agrover112/fliscopt/fliscopt@branchname Or for development: bash git clone https://github.com/Agrover112/fliscopt.git cd fliscopt pip install .

Download the flights.txt file from the following link and add it to a data/ directory within your parent directory.

A sample code demonstrating how to use fliscopt: ```python from fliscopt.utils.util import printschedule, readfile,plotscores from fliscopt.rs import RandomSearch from fliscopt.ga import GA, ReverseGA, GAReversals, GARSReversals from fliscopt.hc import HillClimb from fliscopt.chaining import IteratedChaining from fliscopt.fitness import fitnessfunction,domain,griewank

readfile('flights.txt') sga=GAReversals(seedinit=False,search=False,nk=250,numbergenerations=1000) soln, cost, scores, nfe, seed = sga.run(domain=domain['domain'], fitnessfunction=fitnessfunction,seed=5) plotscores(scores, sga.getbase(),fname='flightscheduling', savefig=False)

sga2=GARSReversals(seedinit=False,search=False,nk=250,numbergenerations=1000) soln, cost, scores, nfe, seed = sga2.run(domain=domain['domain'], fitnessfunction=fitnessfunction,seed=5) plotscores(scores, sga2.getbase(),fname='flightscheduling', save_fig=False)

``` This results in the following two plots:

Checkout out the examples in the examples directory or run in Google Collab

For PyPy users

The instructions for setup are mentioned in the setup directory. Alternatively, you can set up using this bash script. A requirements file is provided just in case. The script creates and activates a PyPy Conda environment with all libraries and dependencies. bash cd ./setup.sh source setup.sh Then install using:

bash pypy -mpip install fliscopt

Testing

After adding any new algorithm, you can run the tests to check if the code is working properly. bash ./run_tests.sh

Results

Experimental Results

Results were compared by using the same seeds. The following table shows the results of the experiments. Flight scheduling results have been shown below.

| Algorithm | Mean cost | Std.dev | Min cost | Max cost | n.fe. | Time(millsecond) | |-----------|-----------|---------|----------|----------|-------|------------------| | SGA | 2780.9 | 205.75 | 2356 | 3081 | 1000 | 9.36 |
| GAwRo | 2629.8 | 213.79 | 2356 | 3004 | 1000 | 9.66 |
| GAwR | 2593 | 183.89 | 2356 | 2973 | 1099 | 10.16 |
| HC | 4177.7 | 817.72 | 2759 | 5839 | 328 | 0.33 |
| RS | 4545.3 | 271.95 | 4143 | 5165 | 100 | 0.17 |
| SA | 3726.5 | 578.16 | 2759 | 4679 | 512 | 0.24 |
| RS+HC | 3050.7 | 399.72 | 2356 | 3771 | 1657 | 2.23 |
| GARSRev | 2592.9 | 168.45 | 2356 | 2888 | 1099 | 9.97 |

Accessing results

After running the experiments, the results are stored in the results directory. The results are stored in the following format in subdirectories: . ├── multi_proc │ ├── ackley_N2 │ │ ├── genetic_algorithm_results.csv │ │ ├── genetic_algorithm_reversed_results.csv │ │ ├── genetic_algorithm_with_reversals_results.csv │ │ ├── hill_climb_results.csv │ │ ├── random_search_results.csv │ │ └── simulated_annealing_results.csv │ ├── booth/... | | | | │ └── zakharov │ ├── genetic_algorithm_results.csv │ ├── genetic_algorithm_reversed_results │ ├── genetic_algorithm_with_reversals_results.csv │ ├── random_search_results.csv │ └── simulated_annealing_results.csv ├── plots │ ├── ackley_N2 │ ├── fitness_function │ │ ├── hill_climb.png │ │ └── random_search.png │ ├── flight_scheduling │ │ ├── simulated_annealing.png │ │ ├── sol_chaining.png │ │ └── sol_chaining_a1.png │ └── griewank

References

Read and Cite the following References for detailed understanding and use of our project.

[1] Grover, A., Yadav, V., Alicea, B. (2023). Flipping the Switch on Local Exploration: Genetic Algorithms with Reversals. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol 608. Springer, Singapore

[2] Alicea B., Grover A., Lim A. ,Parent J, Unified Theory of Switching. Flash Talk presented at: 4th Neuromatch Conference; December 1 - 2, 2021

Contributing Guidelines

Refer Contributing.md and Project Board for mode details. This repository follows conventional commits!

Owner

Login: Agrover112
Kind: user

Repositories: 113
Profile: https://github.com/Agrover112

Humans trying to understand machines and people.

Citation (CITATION.cff)

cff-version: 1.1.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Ankit
    given-names: Grover
    orcid : https://orcid.org/0000-0003-3724-6605
title: fliscopt
version: v0.4.1
doi: 10.5281/zenodo.7018486
date-released: 2022-08-24
url : "https://doi.org/10.5281/zenodo.7018486"

GitHub Events

Total

Issues event: 1
Watch event: 7
Issue comment event: 3

Last Year

Issues event: 1
Watch event: 7
Issue comment event: 3

Committers

Last synced: 6 months ago

All Time

Total Commits: 272
Total Committers: 13
Avg Commits per committer: 20.923
Development Distribution Score (DDS): 0.393

Past Year

Commits: 1
Committers: 1
Avg Commits per committer: 1.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Ankit Grover	a**2@g**m	165
Agrover112	4****2	56
Conventional Changelog Action	c**n@g**m	14
Shubhayu Basu	9****B	9
Anik-Bardhan	6****n	8
Shubhayu Basu	s**3@g**m	7
Hitesh	h**i@g**m	3
LittleBigProgramming	2****g	3
Anirudh Anup	a**9@g**m	2
CharlesAverill	c**0@g**m	2
Subham Ghosh	s**m@S**l	1
Tengzhen	t**o@g**m	1
Tom Schmelzer	3****o	1

Committer Domains (Top 20 + Academic)

github.com: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 19
Total pull requests: 46
Average time to close issues: 15 days
Average time to close pull requests: about 15 hours
Total issue authors: 2
Total pull request authors: 14
Average comments per issue: 4.16
Average comments per pull request: 0.89
Merged pull requests: 38
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 1
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 1
Pull request authors: 0
Average comments per issue: 0.0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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Top Authors

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Agrover112 (18)
mpfaffenberger (1)

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Agrover112 (28)
Gizmotronn (6)
subhamgcon (1)
kkhitesh (1)
schmelto (1)
Nirodha-Github (1)
So-bonkers (1)
tawhidkhn63 (1)
Anik-Bardhan (1)
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ShubhayuB (1)
gptkrsh (1)
LittleBigProgramming (1)

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Hacktoberfest (15) good first issue (12) help wanted (8) bug (5) Hacktoberfest-accepted (5) enhancement (4) documentation (4)

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Packages

Total packages: 1
Total downloads:
- pypi 18 last-month

Total dependent packages: 0
Total dependent repositories: 1
Total versions: 6
Total maintainers: 1

pypi.org: fliscopt

Flight scheduling optimization using Genetic Algorithm variants and other algorithms.

Documentation: https://fliscopt.readthedocs.io/
License: MIT
Latest release: 0.3.0
published almost 4 years ago

Versions: 6
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 18 Last month

Rankings

Dependent packages count: 10.0%

Dependent repos count: 21.7%

Average: 24.5%

Downloads: 41.9%

Maintainers (1)

agrover112

Last synced: 4 months ago

Dependencies

requirements.txt pypi

matplotlib >=3.3.
rich >=10.1.

setup.py pypi

matplotlib *
rich *

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TriPSs/conventional-changelog-action v3.7.1 composite
actions/checkout v2 composite
actions/create-release v1 composite

.github/workflows/sync.yml actions

tgymnich/fork-sync v1.4 composite

package.json npm

fliscopt

Science Score: 67.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

Fliscopt

Getting Started

For PyPy users

Testing

Results

Experimental Results

Accessing results

References

Contributing Guidelines

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

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Issue Authors

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Packages

pypi.org: fliscopt

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Maintainers (1)

Dependencies