A Robust Server-Effort Policy for Fluid Processing Networks

TL;DR

This paper develops a robust resource allocation policy for fluid processing networks with uncertain arrival and service rates, especially when servers handle multiple jobs simultaneously, improving upon existing processing rate-based models.

Contribution

It introduces a robust server-effort policy formulation for uncertain fluid networks, outperforming traditional processing rate models in multi-job processing scenarios.

Findings

Server-effort model provides better robustness than processing rate models.

The proposed approach handles multiple simultaneous job processing.

Numerical experiments demonstrate improved performance of the server-effort policy.

Abstract

Multi-Class Processing Networks describe a set of servers that perform multiple classes of jobs on different items. A useful and tractable way to find an optimal control for such a network is to approximate it by a fluid model, resulting in a Separated Continuous Linear Programming (SCLP) problem. Clearly, arrival and service rates in such systems suffer from inherent uncertainty. A recent study addressed this issue by formulating a Robust Counterpart for SCLP models with budgeted uncertainty which provides a solution in terms of processing rates. This solution is transformed into a sequencing policy. However, in cases where servers can process several jobs simultaneously, a sequencing policy cannot be implemented. In this paper, we propose to use in these cases a a resource allocation policy, namely, the proportion of server effort per class. We formulate Robust Counterparts of both processing rates and server-effort uncertain models for four types of uncertainty sets: box, budgeted, one-sided budgeted, and polyhedral. We prove that server-effort model provides a better robust solution than any algebraic transformation of the robust solution of the processing rates model. Finally, to get a grasp of how much our new model improves over the processing rates robust model, we provide results of some numerical experiments.