Maximizing throughput in finite-source parallel queue systems

Motivated by the dispatching of trucks to shovels in surface mines, we study optimal routing in a Markovian finite-source, multi-server queueing system with heterogeneous servers, each with a separate queue. We formulate the problem of routing customers to servers to maximize the system throughput as a Markov Decision Process. When the servers are homogeneous, we demonstrate that the Shortest Queue policy is optimal, and when the servers are heterogeneous, we partially characterize the optimal policy and present a near-optimal and simple-to-implement policy. We use the model to illustrate the substantial benefits of pooling, by comparing it to the permanent assignment of customers to servers.

► We study throughput-maximizing routing in finite-source multi-server queueing systems.
► Our study is motivated by the routing of trucks to shovels in oil sands mining.
► We show that the Shortest Queue policy is optimal for homogeneous servers.
► We present an index policy with near-optimal performance for heterogeneous servers.
► We use our model to illustrate the substantial benefit of pooling in such systems.