A robust approach to the share-of-choice product design problem

A critical issue when solving the share-of-choice product design problem is the reliability of the optimal solution in the presence of partworth uncertainty. Existing approaches use point estimates of an individual's partworth utilities as input to the product optimization stage, ignoring within-person variability in estimates. Post-optimality sensitivity analysis is occasionally performed to assess the degree to which a solution is negatively impacted by partworth uncertainty. We propose a robust optimization model that explicitly captures variation in partworth estimates during the optimization process. Using a large, commercial dataset, we benchmark our model's performance against its deterministic counterpart. We also present inferential theory to guide the selection of model parameters controlled by the analyst. Results reveal that the new approach produces robust solutions in the face of measurement error. Out-of-sample coverage for individuals drawn from the target population is significantly higher than corresponding solutions from published methods.

► We model the share-of-choice (SOC) product design problem in the presence of partworth uncertainty.
► We provide an exact formulation of the SOC product design problem as a linearized robust optimization problem.
► We conduct an analysis of the sensitivity of solutions to settings of two parameters controlled by an analyst.
► We investigate the benefits of the new approach relative to published deterministic solutions.
► Our model results are significantly better than corresponding solutions from published methods.