Exact First-Choice Product Line Optimization
Name
SSRN-id3020502.pdf
Description
Accepted version
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631.1 KB
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Author(s)
Bertsimas, Dimitris J
Mišić, Velibor V.
Date Issued
May 2019
Journal
Operations Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Bertsimas, Dimitris and Velibor V. Mišić. "Exact First-Choice Product Line Optimization." Operations Research 67, 3 (May 2019): 599-649. © 2019 INFORMS
Version
Author's final manuscript
Abstract
A fundamental problem faced by firms is that of product line design: given a set of candidate products that may be offered to a collection of customers, what subset of those products should be offered to maximize the profit that is realized when customers make purchases according to their preferences? In this paper, we consider the product line design problem when customers choose according to a first-choice rule and present a new mixed-integer optimization formulation of the problem. We theoretically analyze the strength of our formulation and show that it is stronger than alternative formulations that have been proposed in the literature, thus contributing to a unified understanding of the different formulations for this problem. We also present a novel solution approach for solving our formulation at scale, based on Benders decomposition, which exploits the surprising fact that Benders cuts for both the relaxation and the integer problem can be generated in a computationally efficient manner. We demonstrate the value of our formulation and Benders decomposition approach through two sets of experiments. In the first, we use synthetic instances to show that our formulation is computationally tractable and can be solved an order of magnitude faster for small- to medium-scale instances than the alternate, previously proposed formulations. In the second, we consider a previously studied product line design instance based on a real conjoint data set, involving over 3,000 candidate products and over 300 respondents. We show that this problem, which required a week of computation time to solve in prior work, is solved by our approach to full optimality in approximately 10 minutes.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Operations Research Center
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1287/opre.2018.1825
