Partitioning Orders in Online Shopping Services.

The rapid growth of the Internet has led to the widespread use of newer and richer models of online shopping and delivery services. The race to efficient large scale on-demand delivery has transformed such services into complex networks of shoppers (typically working in the stores), stores, and consumers. The efficiency of processing orders in stores is critical to the profitability of the business model. Motivated by this setting, we consider the following problem: given a set of shopping orders each consisting of a few items, how to best partition the orders among a given number of shoppers working for an online shopping service? Formulating this as an optimization problem, we propose a family of simple and efficient algorithms that admit natural constraints such as number of items a shopper can process in this setting. In addition to showing provable guarantees for the algorithms, we also demonstrate their efficiency in practice on real-world data, outperforming strong baselines.