Asset Details
Do you wish to reserve the book?
Reductions of Approximate Linear Programs for Network Revenue Management
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Reductions of Approximate Linear Programs for Network Revenue Management
Do you wish to request the book?
Reductions of Approximate Linear Programs for Network Revenue Management
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Reductions of Approximate Linear Programs for Network Revenue Management
2015
Overview
The linear programming approach to approximate dynamic programming has received considerable attention in the recent network revenue management literature. A major challenge of the approach lies in solving the resulting approximate linear programs (ALPs), which often have a huge number of constraints and/or variables. We show that the ALPs can be dramatically reduced in size for both affine and separable piecewise linear approximations to network revenue management problems, under both independent and discrete choice models of demand. Our key result is the equivalence between each ALP and a corresponding reduced program, which is more compact in size and admits an intuitive probabilistic interpretation. For the affine approximation to network revenue management under an independent demand model, we recover an equivalence result known in the literature, but provide an alternative proof. Our other equivalence results are new. We test the numerical performance of solving the reduced programs directly using off-the-shelf commercial solvers on a set of test instances taken from the literature.
Publisher
INFORMS,Institute for Operations Research and the Management Sciences
Subject
