Bounded-Velocity Stochastic Control for Dynamic Resource Allocation

TL;DR

This paper develops a bounded-velocity stochastic control approach for dynamic resource allocation problems with uncertain demand, providing efficient algorithms and demonstrating benefits over existing methods.

Contribution

It introduces a novel bounded-velocity control framework for resource allocation under uncertainty, with practical algorithms and comprehensive computational analysis.

Findings

The proposed control policy effectively manages resources with demand variability.

Algorithms are computationally efficient and easy to implement.

The approach outperforms recent alternative optimization methods.

Abstract

We consider a general class of dynamic resource allocation problems within a stochastic optimal control framework. This class of problems arises in a wide variety of applications, each of which intrinsically involves resources of different types and demand with uncertainty and/or variability. The goal involves dynamically allocating capacity for every resource type in order to serve the uncertain/variable demand, modeled as Brownian motion, and maximize the discounted expected net-benefit over an infinite time horizon based on the rewards and costs associated with the different resource types, subject to flexibility constraints on the rate of change of each type of resource capacity. We derive the optimal control policy within a bounded-velocity stochastic control setting, which includes efficient and easily implementable algorithms for governing the dynamic adjustments to resource allocation capacities over time. Computational experiments investigate various issues of both theoretical and practical interest, quantifying the benefits of our approach over recent alternative optimization approaches.