Self-adjusting binary search trees
Journal of the ACM (JACM)
The weighted majority algorithm
Information and Computation
Online computation and competitive analysis
Online computation and competitive analysis
Adaptive Ordering and Pricing for Perishable Products
Operations Research
The Censored Newsvendor and the Optimal Acquisition of Information
Operations Research
Path kernels and multiplicative updates
The Journal of Machine Learning Research
Learning Algorithms for Separable Approximations of Discrete Stochastic Optimization Problems
Mathematics of Operations Research
Efficient algorithms for online decision problems
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Provably near-optimal sampling-based algorithms for Stochastic inventory control models
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Prediction, Learning, and Games
Prediction, Learning, and Games
Manufacturing & Service Operations Management
Relative Entropy, Exponential Utility, and Robust Dynamic Pricing
Operations Research
Regret in the Newsvendor Model with Partial Information
Operations Research
Analysis of Perishable-Inventory Systems with Censored Demand Data
Operations Research
A Risk-Reward Competitive Analysis for the Newsboy Problem with Range Information
COCOA '09 Proceedings of the 3rd International Conference on Combinatorial Optimization and Applications
A practical inventory control policy using operational statistics
Operations Research Letters
| Hi-index | 0.00 |
The newsvendor problem describes the dilemma of a newspaper salesman--how many papers should he purchase each day to resell, when he doesn't know the demand? We develop approaches for this well known problem in operations research, both for when the actual demand is known at the end of each day, and for when just the amount sold is known, i.e., the demand is censored. We present three results: (1) the first known algorithm with a bound on its worst-case performance for the censored demand newsvendor problem, (2) an algorithm with improved worst-case performance bounds for the regular newsvendor problem compared to previously known algorithms, and (3) more precise bounds on the performance of the two algorithms when they are seeded with an approximate "guess" on the optimal solution. In addition (4) we test the algorithms in a variety of simulated and real world conditions, and compare the results to those by previously known approaches. Our tests indicate that our algorithms perform comparably and often better than known approaches.