A Column Generation Algorithm for Choice-Based Network Revenue Management
By Juan José Miranda Bront, Isabel Méndez-Díaz, Gustavo J. Vulcano. Operational Research, 2009
336 citations recorded by the source corpus.
Subfields: Management Information Systems, Marketing, Transportation
View Paper: https://www.semanticscholar.org/paper/1495d058cfdb5c3a78f43efd4ec47fa9ffe6b6ac
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Information Systems
- Vivek F. Farias
- Devavrat Shah
- Srikanth Jagabathula
- Huseyin Topaloglu
- James Mario Davis
- Paat Rusmevichientong
- David B. Shmoys
- Guillermo Gallego
In Marketing
- Vivek F. Farias
- Devavrat Shah
- Srikanth Jagabathula
- Huseyin Topaloglu
- Chaoxu Tong
- Paat Rusmevichientong
- David B. Shmoys
- Guillermo Gallego
In Transportation
- Vivek F. Farias
- Guillermo Gallego
- Devavrat Shah
- James Mario Davis
- Srikanth Jagabathula
- Huseyin Topaloglu
Citations in the corpus
- Assortment Optimization Under General Choice (2011, methodology): “Bront et al. (2009) show that the uncapacitated assortment optimization problem under the Mixed-Logit model is NP-hard in the strong sense when there are at least n customer classes.”
- Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters (2014, methodology): “Bront et al. (2009) show that the assortment problem is NP complete in the strong sense under random model parameters, give a mixed integer programming formulation to obtain the optimal assortment, and suggest a greedy heuristic.”
- Assortment Optimization Under Variants of the Nested Logit Model (2014, methodology): “For the pricing problem, Hanson and Martin (1996) notice that the expected revenue function fails to be concave in prices for the multinomial logit model, but significant progress was made by formulating the pricing problem in terms of market shares, as this results in a concave expected revenue fun”
- Joint Stocking and Product Offer Decisions Under the Multinomial Logit Model (2013, background): “For the linear program proposed by Gallego et al. (2004), Bront et al. (2009) consider the case where there are multiple customer types that make a choice according to multinomial logit models with different parameters.”
- Assortment Planning Under the Multinomial Logit Model with Totally Unimodular Constraint Structures (2013, background): “Bront et al. (2009), Mendez-Diaz et al. (2010) and Rusmevichientong, Shmoys and Topaloglu (2010) consider the case where there are multiple customer types and customers of each type choose according to a different MNL.”