This material replicates all of the computational experiments in the paper "History-Dependent Fluid Approximations and Performance Guarantees for Revenue Management with Markov-Modulated Demands." Detailed instructions to run the code are included in the read-me file in the zip file.
Code for single-leg revenue management with fare-locking
This material includes the code and data for the paper "An Approximation Algorithm for Capacity Allocation over a Single Flight Leg with Fare-Locking." The code implements the 1/2-approximate policy and a bid-price policy that uses bid-prices extracted from a linear programming formulation. All test problems are generated on the fly in the code.
Network revenue management datasets
This material includes network revenue management datasets together with the performances of different benchmark strategies. The test problems were used in the paper "Using Lagrangian Relaxation to Compute Capacity-Dependent Bid Prices in Network Revenue Management."
Choice modeling datasets
This material includes choice modeling datasets with subsets of products offered to customers and the products chosen by the customers out of the offered subsets. The choices of the customers are governed by a preference ranking based choice model. The accompanying Matlab code fits a Markov chain choice model and a multinomial logit model to the data.
Stochastic programming datasets
This material includes two-stage stochastic programming datasets in SMPS format.
Min-cost network flow and min-cost flow augmenting path packages
This material includes a Java implementation of the min-cost network flow and min-cost flow augmenting path algorithms. NetworkTest.java includes an example that shows how to use the library. Useful for standalone applications that avoids commercial solvers.
If you use the material here, please include a reference.