Dynamic volunteer assignment

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 .《动态志愿者任务指派》配套数据与代码文件 本仓库包含本研究中使用的部分算法代码与相关数据集，具体包含以下文件： a2c_algorithm.py（17.1 KB）：用于管理多类别并行队列的优势演员-评论家（Advantage Actor-Critic, A2C）算法的Python实现。 algorithmperformance.zip（28.1 MB）：包含本文中各类算法性能评估数据的压缩文件夹。 ant_colony_optimization.py（5.36 KB）：用于求解调度问题的启发式方法——蚁群优化（Ant Colony Optimization）算法的Python实现。 attention_module.py（6.58 KB）：深度强化学习框架中用于提升模型性能的注意力模块的Python代码。 diferrent rules.zip（1.18 MB）：包含各类调度规则与启发式方法在多类别队列管理中的性能相关数据的压缩文件夹。 environment.py（5.59 KB）：仿真环境搭建代码，定义了多类别排队系统与工作人员服务能力。 genetic_algorithm_optimization.py（5.18 KB）：作为备选优化启发式方法的遗传算法（Genetic Algorithm）的Python实现。 ppo_algorithm.py（18.7 KB）：用于队列管理的深度强化学习方法——近端策略优化（Proximal Policy Optimization, PPO）算法的Python实现。 sensitivity.zip（55 KB）：包含灵敏度分析数据的压缩文件夹，用于评估各类算法在不同参数设置下的鲁棒性。 various distribution.zip：包含用于测试算法在各类到达时间分布下性能的数据集的压缩文件夹。