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Robust assortment optimization under sequential product unavailability
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DOI：10.1016/j.ejor.2022.03.033.png)
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Abstract
En 中文
Assortment planning is a central piece in the revenue management strategy of every retail company. In this paper, we study a robust assortment optimization problem for substitutable products under a sequential ranking-based choice model and a cardinality constraint. Our choice model captures the increasing customer frustration of finding multiple products unavailable as a factor affecting purchasing decisions. To model the highly uncertain order in which a customer explores the products to buy, we present a bi-level optimization approach to maximize the expected revenue assuming that the customer visits a sequence of unavailable products that minimizes the likelihood of staying in the store. We show that the resulting problem is NP-hard and develop exact and greedy solution approaches that can solve different instances efficiently in terms of both solution time and optimality gap. We develop a model extension that includes multiple customer categories and also show a special case of the problem that can be solved in polynomial time. We perform a computational study to demonstrate the performance of our methods and illustrate the sensitivity of the optimal assortment to variations in the input parameters. (C) 2022 Elsevier B.V. All rights reserved.
Keywords:
Retailing
Assortment planning
Ranking-based choice model
Multinomial logit choice model
Robust optimization
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W