On upper bounds for assortment optimization under the mixture of multinomial logit models
作者： Sumit Kunnumkal
DOI: 10.1016/J.ORL.2015.01.010
关键词: Revenue 、 Upper and lower bounds 、 No-arbitrage bounds 、 Mathematics 、 Mathematical optimization 、 Customer choice 、 Multinomial logistic regression 、 Optimization problem
摘要: The assortment optimization problem under the mixture of multinomial logit models is NP-complete and there are different approximation methods to obtain upper bounds on optimal expected revenue. In this paper, we analytically compare obtained by methods. We propose a new, tractable approach construct an bound revenue show that it obtains tightest among existing approaches in literature bounds.
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