Online Resource Allocation to Process Tasks Under Uncertain Resource Availabilities

Allocating resources to process tasks during runtime (online) is hard. A solution method for such allocation is required to be computationally efficient while being subjected to uncertainties such as resources suddenly becoming (un)available. Resource allocation problems where task processing times differ across resources can be formalized as an assignment or parallel machines scheduling problem. This work presents adaptations to both problem formulations to address resource (un)availabilities. These adaptations require a prediction model to estimate the processing time of a task for all of its authorized resources. We evaluate and compare the proposed adaptations with existing allocation approaches on two process simulation models created from an artificial and a real-life event log. Our results show that both approaches can outperform traditional allocation strategies, such as the shortest queue, random, round-robin, and batch-allocation approaches.