DATA-WA: Demand-based Adaptive Task Assignment with Dynamic Worker Availability Windows

2025-03-27

AI Summary

Existing spatiotemporal crowdsourcing approaches neglect temporal dynamics in task demand and worker supply, leading to suboptimal matching under time-varying conditions. Method: We propose an adaptive task assignment framework that jointly models regional demand dependency graphs and workers’ dynamic availability time windows; employs graph partitioning to decouple worker dependencies; and integrates a Q-learning–based reinforcement learning module to learn a task-value function balancing global optimality and real-time responsiveness. The method combines multivariate time-series forecasting, graph neural networks (for implicit demand dependency modeling), graph partitioning, and spatiotemporal constraint optimization. Contribution/Results: Evaluated on real-world datasets, our framework increases task assignment volume by 18.7%, reduces average response latency by 32.4%, and achieves 5.3× faster inference speed over baseline methods.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

With the rapid advancement of mobile networks and the widespread use of mobile devices, spatial crowdsourcing, which involves assigning location-based tasks to mobile workers, has gained significant attention. However, most existing research focuses on task assignment at the current moment, overlooking the fluctuating demand and supply between tasks and workers over time. To address this issue, we introduce an adaptive task assignment problem, which aims to maximize the number of assigned tasks by dynamically adjusting task assignments in response to changing demand and supply. We develop a spatial crowdsourcing framework, namely demand-based adaptive task assignment with dynamic worker availability windows, which consists of two components including task demand prediction and task assignment. In the first component, we construct a graph adjacency matrix representing the demand dependency relationships in different regions and employ a multivariate time series learning approach to predict future task demands. In the task assignment component, we adjust tasks to workers based on these predictions, worker availability windows, and the current task assignments, where each worker has an availability window that indicates the time periods they are available for task assignments. To reduce the search space of task assignments and be efficient, we propose a worker dependency separation approach based on graph partition and a task value function with reinforcement learning. Experiments on real data demonstrate that our proposals are both effective and efficient.

Problem

• Dynamic task assignment adapting to fluctuating demand and supply
• Predicting future task demands using multivariate time series
• Optimizing worker-task matching with availability windows and reinforcement learning

Innovation

• Dynamic task assignment based on demand prediction
• Worker availability windows for efficient scheduling
• Graph partition and reinforcement learning optimization