Optimizing dynamic logistics in megacity using deep reinforcement learning with Transformer for minimizing travel time
Year: 2024
Type: article
Abstract: DTM-DMDVRP ContentThis project is linked to a submission to International Journal of Geographical Information Science, entitled “Optimizing dynamic logistics in megacity using deep reinforcement learning with Transformer for minimizing travel time”, proposed an urban dynamic logistics optimization model aimed at minimizing logistics distribution time.The DTM-DMDVRP folder includes both the data and code necessary to support the DTM-DMDVRP model, along with additional README files.The figure folder contains the original images from the paper.ContentThe "data" folder contains the data used by the model and contains a folder and two files：1)data/clientpoint.csv:This file contains logistics center and customer point data with fields including id and latitude and longitude;2)data/distance.csv:This file contains the travel time between nodes, as measured by road length and average travel speed;3)data/speed_data:This folder contains the dynamic travel time between nodes at different times, which is obtained by the length of the road and the real-time travel speed.The "code" folder contains the core code of the program:1)baseline.py:This script implements the RolloutBaseline algorithm;2)config.py:This script is used to configure model training parameters；3)dataset.py:This script is used to generate the training dataset；4)train.py:Training scripts for models;The "code/Nets" folder contains the implementation of the model：1)model.py:DTM-DMDVRP model script；2)encoder_layers.py:This script includes the components of the encoder network architecture；3)encoder.py:This script contains the detailed implementation of the encoder；4)decoder_layers:This script includes the components of the decoder network architecture；5)decoder_utils:This script is used to implement the environment in deep reinforcement learning;6)decoder：This script contains the detailed implementation of the decoder.The environment required for this project includes:Python 3.12; Pytorch; Scipy; Numpy.
Source: Figshare
Author: Yao, Yao
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6958488109
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