Assortment Optimization under the Decision Forest Model
Yi-Chun Akchen, Velibor V. Mi\v{s}i\'c

TL;DR
This paper develops a mixed-integer optimization approach for assortment planning under the decision forest model, enabling firms to exploit complex customer behaviors for increased revenue.
Contribution
It introduces two formulations for the assortment optimization problem under the decision forest model and proposes an efficient Benders decomposition method for large-scale instances.
Findings
The formulations are theoretically compared in strength.
Benders decomposition efficiently solves large instances.
The approach outperforms heuristics in experiments.
Abstract
We study the problem of finding the optimal assortment that maximizes expected revenue under the decision forest model, a recently proposed nonparametric choice model that is capable of representing any discrete choice model and in particular, can be used to represent non-rational customer behavior. This problem is of practical importance because it allows a firm to tailor its product offerings to profitably exploit deviations from rational customer behavior, but at the same time is challenging due to the extremely general nature of the decision forest model. We approach this problem from a mixed-integer optimization perspective and present two different formulations. We theoretically compare the two formulations in strength, and analyze when they are integral in the special case of a single tree. We further propose a methodology for solving the two formulations at a large-scale based…
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Taxonomy
TopicsSupply Chain and Inventory Management · Consumer Market Behavior and Pricing · Forecasting Techniques and Applications
