Paper Info
Title
Model Predictive Control for Dynamic Resource Allocation
Abstract
The present paper develops a simple, easy to interpret algorithm for a large class of dynamic allocation problems with unknown, volatile demand. Potential applications include ad display problems and network revenue management problems. The algorithm operates in an online fashion and relies on reoptimization and forecast updates. The algorithm is robust (as witnessed by uniform worst-case guarantees for arbitrarily volatile demand) and in the event that demand volatility (or equivalently deviations in realized demand from forecasts) is not large, the method is simultaneously optimal. Computational experiments, including experiments with data from real-world problem instances, demonstrate the practicality and value of the approach. From a theoretical perspective, we introduce a new device---a balancing property---that allows us to understand the impact of changing bases in our scheme.
Year
DOI
Venue
2012
10.1287/moor.1120.0548
Math. Oper. Res.
Keywords
DocType
Volume
demand volatility,ad display problem,large class,network revenue management problem,equivalently deviation,balancing property,forecast updates,Dynamic Resource Allocation,Model Predictive Control,dynamic allocation problem,computational experiment,volatile demand
Journal
37
Issue
ISSN
Citations 
3
0364-765X
19
PageRank 
References 
Authors
1.12
18
2
Name
Order
Citations
PageRank
Dragos Florin Ciocan1302.33
Vivek F. Farias237124.24