Dynamic scheduling of a multiclass fluid network
Operations Research
State space collapse with application to heavy traffic limits for multiclass queueing networks
Queueing Systems: Theory and Applications
A multiple-choice secretary algorithm with applications to online auctions
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
AdWords and Generalized On-line Matching
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Dynamic Pricing Strategies for Multiproduct Revenue Management Problems
Manufacturing & Service Operations Management
Stochastic Optimal Control: The Discrete-Time Case
Stochastic Optimal Control: The Discrete-Time Case
An Asymptotically Optimal Policy for a Quantity-Based Network Revenue Management Problem
Mathematics of Operations Research
Dynamic Bid Prices in Revenue Management
Operations Research
Online Primal-Dual Algorithms for Covering and Packing
Mathematics of Operations Research
Toward Robust Revenue Management: Competitive Analysis of Online Booking
Operations Research
Bid-Price Controls for Network Revenue Management: Martingale Characterization of Optimal Bid Prices
Mathematics of Operations Research
Dynamic Pricing with a Prior on Market Response
Operations Research
Online Optimization with Uncertain Information
ACM Transactions on Algorithms (TALG)
A Re-Solving Heuristic with Bounded Revenue Loss for Network Revenue Management with Customer Choice
Mathematics of Operations Research
Stochastic optimal control for a general class of dynamic resource allocation problems
ACM SIGMETRICS Performance Evaluation Review - Special issue on the 31st international symposium on computer performance, modeling, measurements and evaluation (IFIPWG 7.3 Performance 2013)
Partner tiering in display advertising
Proceedings of the 7th ACM international conference on Web search and data mining
Rewards, costs and flexibility in dynamic resource allocation: a stochastic optimal control approach
ACM SIGMETRICS Performance Evaluation Review
| Hi-index | 0.00 |
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.