Multi-Layer Scheduling in Gig Platforms Using a Generative Diffusion Model With Duality Guidance

In recent years, gig platforms have emerged as a new paradigm, seamlessly connecting workers and tasks while leveraging workers' collective intelligence, participation, and shared resources. Traditionally, platforms have operated under the assumption of worker homogeneity, where service capabilities and associated service costs are similar. However, in mobile computing scenarios, such as mobile crowdsensing, the diversity in worker capabilities and costs renders the supply and demand matching into a complex problem characterized by multiple layers of workers possessing distinct attributes. The dynamic nature of incoming task requests requires the continual reallocation of these workers, thereby introducing a time-dependent overhead. In this paper, we introduce a framework, called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>G</u>enerative Diffusion Model with Duality G<u>uid</u>ance</i>, termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Guid</i>, to address the intricate multi-layer scheduling problem. We formalize a time-slotted long-term optimization problem that captures the spatiotemporal dynamics of task requests and worker services, as well as the intricate time-coupled overhead. Our framework employs a generative diffusion model to explore the complex solution space of the problem and generate superior solutions. To effectively manage time coupling, we utilize dual optimization theory to generate time slot-aware information, guiding the generative diffusion model towards solutions that assure long-term performance. We provide a rigorous theoretical analysis demonstrating that our guidance solution ensures a parameterized competitive ratio guarantee relative to the theoretically optimal solution. Our comprehensive experiments further illustrate that the proposed method outperforms benchmark techniques, achieving reduced overhead compared to seven baseline methods.