Investigation of Efficient Solutions to Large Scale Product Portfolio Selection Problems in EC Logistics

Abstract

Selection of product portfolio and determination of its inclusion ratio is fundamental management issues in e-commerce (EC) logistics business. EC logistics business is a form of business in which products are stocked in advance. Upon receiving an order from a customer via the Internet, the company allocates the stocked products and ships them to the customer. The problem is to control logistics costs by taking into account risks, such as seasonality of individual products, changes in trends, and sudden fluctuations in demand, to increase expected profits continuously. We investigate a method of product portfolio optimization using Markowitz's mean-variance model as a starting point for solving this problem. The general computational complexity of the mean-variance model scales with the target number of items n as ∝ n3. Since n is of order a million, a significant issue is whether it can efficiently find an optimal or good solution using such an extensive data set in a finite amount of time. We seek a method to obtain a feasible solution to this problem within an acceptable time frame for business operations using computer resources that are currently relatively easy to acquire by an average business. In this study, we report on our investigation of classical methods such as divide-and-conquer, compact decomposition, and multi-factor models, as well as relatively new methods such as quantum annealing.