Long-term average throughput-utilization utility maximization in platform-aggregated manufacturing service collaboration

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Enhancing capacity utilization of manufacturing resources is of utmost importance in tackling the current challenges of meeting customized and small-batch market demands. Given the research highlights on platform-based manufacturing service collaboration (MSC) offering high-quality service solutions, efficient service scheduling strategies are urgently needed to maximize overall utility amidst great computational complexity and unpredictable task arrivals. To address this issue, this paper proposes a novel distributed online task dispatch and service scheduling (DOTDSS) strategy in platform-aggregated MSC. What sets our method apart is its goal to optimize a long-term average utility performance with considering queuing dynamics of manufacturing services in multi-task processing, thereby maintaining sustainable platform operations. Firstly, we jointly consider task dispatch and service scheduling decisions into the formulation of a quality-of-service aware (QoS) stochastics optimization problem. The newly constructed logarithmic utility function effectively strikes a trade-off between the throughput and capacity utilization of manufacturing services with diverse capabilities. By incorporating the goal of reducing queue lengths, we then transform the optimization problem into a form with less computational complexity and guaranteed optimality using Lyapunov optimization. We further propose a DOTDSS strategy that relies solely on the current system state and queue information to generate scalable MSC solutions. It does not need to predict task arrival statistics in advance, and it exhibits great adaptability to uncertainties in task arrivals and service availabilities. Finally, numerical results based on simulation data and real workload traces demonstrate the effectiveness of our method. It also shows that the aggregation collaboration pattern among a group of candidates can achieve better performance than that by the optimal candidate alone.