Xiaobo Li, Department of Industrial Systems Engineering and Management, National University of Singapore
Hailong Sun, Institute of Operations Research and Analytics, National University of Singapore
Chung-Piaw Teo, Institute of Operations Research and Analytics, National University of Singapore

This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
ABSTRACT

We study the bundle size pricing (BSP) problem where a monopolist sells bundles of products to customers, and the price of each bundle depends only on the size (number of items) of the bundle. Although this pricing mechanism is attractive in practice, finding optimal bundle prices is difficult since it involves characterizing distributions of the maximum partial sums of order statistics. In this paper, we propose to solve the BSP problem under a discrete choice model using only the first and second moments of customer valuations. Correlations between valuations of bundles are captured by the covariance matrix. We show that the BSP problem under this model is convex and can be efficiently solved using off-the-shelf solvers. Our approach is flexible in optimizing prices for any given bundle size. Numerical results show that it performs very well compared with state-of-the-art heuristics. This provides a unified and efficient approach to solve the BSP problem under various distributions and dimensions.