Ant Colony Optimization for EVRPTW 🌐

Welcome Ant Colony Optimization (ACO) project, where advanced algorithms and electric vehicles meet to solve the Electric Vehicle Routing Problem with Time Windows (EVRPTW). 🚚⏱️

Project Overview 📜

This repository contains a Python-based implementation of ACO algorithms, designed to optimize routing paths for electric vehicles considering specific time windows and recharging requirements.

Documentation 📖

For more detailed information and usage instructions, check out our documentation.

Installation and Dependencies 🔧

Ensure Python is installed along with the following packages:

• numpy
• dask
• pandas
• yaml
• csv

Steps for Installation:

1. Clone the Repository:
   • Use the command git clone https://github.com/F-a-b-r-i-z-i-o/Ant_Colony_Optimization_for_Evrptw.git to clone the EVRPTW repository to your local machine.
2. Create and Activate Virtual Environment:
   • Navigate to the EVRPTW directory (cd EVRPTW).
   • Create a virtual environment named env within this directory. Use the command python3 -m venv env.
   • Activate the virtual environment using the script.sh script with the command . ./active_venv.sh.
3. Install Dependencies:
   • Install required dependencies by executing pip3 install -r requirements.txt. This will install all the necessary packages as listed in the requirements.txt file.

By following these steps, you'll set up the necessary environment to run the Ant Colony Optimization simulations for EVRPTW.

Results 📊

CPLEX vs MACS vs ACSD BEST VALUES FOUND

E-VRPTW	CPLEX best	secs	MACS best	secs	ACSD best	secs	$\Delta%$
C101-5	257.75	81	257.75	0.002	N/A	0.0	0.0
C103-5	176.05	5	176.05	0.21	N/A	0.0	0.0
C206-5	242.55	518	242.55	0.008	242.55	0.47	0.0
C208-5	158.48	15	158.48	0.0002	158.48	0.037	0.0
R104-5	136.69	1	136.69	0.005	140.28	0.0351	2.26
R105-5	156.08	3	156.08	0.001	156.08	0.082	0.0
R202-5	128.78	1	128.78	0.08	128.28	0.97	0.0
R203-5	179.06	5	179.06	1.11	197.77	1.79	10.44
RC105-5	241.30	764	241.30	2.37	241.30	5.87	0.0
RC108-5	253.93	311	253.93	0.002	253.93	0.057	0.0
RC204-5	176.39	54	176.39	0.001	179.81	0.089	1.93
RC208-5	167.98	21	167.98	0.003	181.67	0.034	8.15
C101-10	393.76	171	393.76	4.53	N/A	0.0	0.0
C104-10	273.93	360	273.93	24.1	N/A	0.0	0.0
C202-10	304.06	300	304.06	2.85	304.06	4.90	0.0
C205-10	228.28	4	228.28	20.70	287.29	93.89	25.84
R102-10	249.19	389	249.19	1.57	268.87	20.87	7.89
R103-10	207.05	119	207.05	13.50	207.05	35.68	0.0
R201-10	241.51	177	241.51	1.14	246.63	13.89	2.11
R203-10	218.21	573	218.21	15.45	302.78	56.89	38.75
RC102-10	423.51	810	423.51	11.85	423.51	78.98	0.0
RC108-10	345.93	39	345.93	7.99	398.87	30.87	15.30
RC201-10	412.86	7200	412.86	0.02	412.86	89.67	0.0
RC205-10	325.98	399	325.98	25.57	N/A	0.0	0.0
C103-15	348.29	7200	348.29	24.36	N/A	0.0	0.0
C106-15	275.13	17	275.13	21.88	N/A	0.0	0.0
C202-15	383.62	7200	383.62	59.46	555.58	363.68	44.82
C208-15	300.55	5060	300.55	44.1	398.63	237.87	32.63
R102-15	413.93	7200	413.93	25.84	415.15	234.68	0.29
R105-15	336.15	7200	336.15	13.42	403.20	398.78	19.95
R202-15	358.00	7200	358.00	7.32	400.26	287.89	11.80
R209-15	313.24	7200	313.24	9.01	444.78	345.89	41.99
RC103-15	397.67	7200	397.67	24.52	500.12	456.82	25.76
RC108-15	370.25	7200	370.25	26.96	467.86	554.89	26.36
RC202-15	394.39	7200	394.39	73.38	467.56	556.98	18.55
RC204-15	407.45	7200	382.22	15.51	409.89	666.89	7.23

Note: "N/A" indicates that the instance does not generate eligible solutions.

Other results are available Results

Path Visualization 🔄

Fitness Trend Graph 📉

Paper Citations 📄

• Michalis Mavrovouniotis. "A Multiple Ant Colony System for the Electric Vehicle Routing Problem with Time Windows". KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus. View Paper.

Contributing 🤝

Contributions to enhance the project are welcome. Please feel free to fork the repository, make improvements, and submit pull requests.

License 📄

This project is released under MIT License.

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Enjoy 2F_