Platform-Aggregated Manufacturing Service Collaboration: A Collaborative Optimization Approach for Delay-Constrained Applications

To tackle challenges of low profitability and high response delays in multitask competitive production environments with fluctuating capacity, this article studies a collaborative optimization approach of task admission and service scheduling with dynamic pricing aid. First, in the context of platform-aggregated manufacturing service collaboration, we introduce service queues to account for response delays and develop a novel nonlinear profit optimization model. This model optimizes task admission, service scheduling, and pricing decisions simultaneously, to adapt the admitted task load to the fluctuating capacity and enhance the throughput utilities of heterogeneous services. To solve this large-scale, nonlinear optimization problem, we then propose a novel distributed online task admission and service scheduling optimization strategy by constructing a Lyapunov quadratic function. It coordinates the optimal decisions for each service in a computationally efficient manner without requiring prior knowledge of task statistics or data training. Moreover, we analytically illustrate that our approach can achieve the optimal time average profit while bounding time average queue length over temporal fluctuations. Numerical results from real workload traces demonstrate the effectiveness of our approach compared to three existing strategies, offering valuable insights for platform operations.