2026-10-16T00:57:30Z https://data.mendeley.com/oai

oai:data.mendeley.com/w7xnfzykyw.2 2024-10-14T09:03:40Z

FENG, BO Dynamic volunteer assignment Mendeley Data Data and Code Files for “Dynamic volunteer assignments” This repository contains partial algorithm code and related datasets used in our research. The following files are included: a2c_algorithm.py (17.1 KB): Python implementation of the Advantage Actor-Critic (A2C) algorithm used for managing multi-class parallel queues. algorithmperformance.zip (28.1 MB): A zipped folder containing performance evaluation data for the various algorithms presented in the paper. ant_colony_optimization.py (5.36 KB): Python implementation of the Ant Colony Optimization algorithm used as a heuristic method in solving the scheduling problem. attention_module.py (6.58 KB): Python code for the attention module used within the deep reinforcement learning framework to enhance model performance. diferrent rules.zip (1.18 MB): A zipped folder with data related to the performance of different scheduling rules and heuristics in managing the multi-class queues. environment.py (5.59 KB): The simulation environment setup, defining the multi-class queueing system and worker service capabilities. genetic_algorithm_optimization.py (5.18 KB): Python implementation of the Genetic Algorithm used as an alternative optimization heuristic. ppo_algorithm.py (18.7 KB): Python implementation of the Proximal Policy Optimization (PPO) algorithm used as a deep reinforcement learning approach for queue management. sensitivity.zip (55 KB): A zipped folder with sensitivity analysis data to evaluate the robustness of the algorithms under various parameter settings. various distribution.zip: A zipped folder containing data for testing the algorithms under various distributions for arrival time . Algorithms Meta Dataset Dataset doi:10.17632/w7xnfzykyw.2 oai:data.mendeley.com/w7xnfzykyw.2 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/w7xnfzykyw 2024-10-14T09:03:40Z