scholarly journals Capacity Constrained Assortment Optimization Under the Markov Chain Based Choice Model

2015 ◽  
Author(s):  
Antoine DDsir ◽  
Vineet Goyal ◽  
Danny Segev ◽  
Chun Ye
Keyword(s):  
Markov Chain ◽  
Choice Model ◽  
Assortment Optimization ◽  
Capacity Constrained

Constrained Assortment Optimization Under the Markov Chain–based Choice Model

2020 ◽  
Vol 66 (2) ◽  
pp. 698-721 ◽  
Author(s):  
Antoine Désir ◽  
Vineet Goyal ◽  
Danny Segev ◽  
Chun Ye
Keyword(s):  
Markov Chain ◽  
Choice Model ◽  
Assortment Optimization

Assortment Optimization under the Multi-Purchase Multinomial Logit Choice Model

2021 ◽  
Author(s):  
Jacob Feldman ◽  
Danny Segev ◽  
Huseyin Topaloglu ◽  
Laura Wagner ◽  
Yicheng Bai
Keyword(s):  
Choice Model ◽  
Multinomial Logit ◽  
Assortment Optimization

Constrained Assortment Optimization Under the Paired Combinatorial Logit Model

2021 ◽  
Author(s):  
Rohan Ghuge ◽  
Joseph Kwon ◽  
Viswanath Nagarajan ◽  
Adetee Sharma
Keyword(s):  
Logit Model ◽  
Choice Model ◽  
Customer Behavior ◽  
Multidimensional Space ◽  
Customer Choice ◽  
Assortment Optimization ◽  
Selected Subset ◽  
Unconstrained Problem ◽  
Key Aspects ◽  
Customer Choice Model

Assortment optimization involves selecting a subset of products to offer to customers in order to maximize revenue. Often, the selected subset must also satisfy some constraints, such as capacity or space usage. Two key aspects in assortment optimization are (1) modeling customer behavior and (2) computing optimal or near-optimal assortments efficiently. The paired combinatorial logit (PCL) model is a generic customer choice model that allows for arbitrary correlations in the utilities of different products. The PCL model has greater modeling power than other choice models, such as multinomial-logit and nested-logit. In “Constrained Assortment Optimization Under the Paired Combinatorial Logit Model,” Ghuge, Kwon, Nagarajan, and and Sharma provide efficient algorithms that find provably near-optimal solutions for PCL assortment optimization under several types of constraints. These include the basic unconstrained problem (which is already intractable to solve exactly), multidimensional space constraints, and partition constraints. The authors also demonstrate via extensive experiments that their algorithms typically achieve over 95% of the optimal revenues.

Revenue Management Under the Markov Chain Choice Model

2017 ◽  
Vol 65 (5) ◽  
pp. 1322-1342 ◽  
Author(s):  
Jacob B. Feldman ◽  
Huseyin Topaloglu
Keyword(s):  
Markov Chain ◽  
Revenue Management ◽  
Choice Model

Robust Assortment Optimization Under the Markov Chain Model

2021 ◽  
Author(s):  
Antoine Désir ◽  
Vineet Goyal ◽  
Bo Jiang ◽  
Tian Xie ◽  
Jiawei Zhang
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Chain Model ◽  
Assortment Optimization
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