Assortment optimisation problem: A distribution-free approach. (September 2020) - Chan, Rebecca; Li, Zhaolin; Matsypura, Dmytro
Highlights: We study a single period assortment optimization problem with multinomial logit model of consumer choice and static substitution. We make no distributional assumptions on product demand. We use max-min approach to formulate an optimization problem to maximize the worst-case profit. We propose three heuristic algorithms to solve the problem. One of the heuristics performs extremely well with an average optimality gap of 0.07%. Abstract: Assortment optimisation is a critical decision that is regularly made by retailers. The decision involves a trade-off between offering a larger assortment of products but smaller inventories of each product and offering a smaller number of varieties with more inventory of each product. We propose a robust, distribution-free formulation of the assortment optimisation problem such that the assortment and inventory levels can be jointly optimised without making specific assumptions on the demand distributions of each product. We take a max-min approach to the problem that provides a guaranteed lower bound to the expected profit when only the mean and variance of the demand distribution are known. We propose and test three heuristic algorithms that provide solutions in O ( n log ( n )) time and identify two cases where one of the heuristics is guaranteed to return optimal policies. Through numerical studies, we demonstrate that one of the heuristics performs extremely well, with an average optimality gap of 0.07% when simulated under varying conditions. We perform a sensitivity analysis of product and store demand attributes on the performance of the heuristic. Finally, we extend the problem by including maximum cardinality constraints on the assortment size and perform numerical studies to test the performance of the heuristics.