Motivated by our collaboration with an online platform operating in North America, we explore the joint optimization of the order fulfillment process with personalized delivery options in the context of e-commerce. Customers can choose from personalized fulfillment options to proceed with the purchase or leave without making a purchase. The retailer periodically makes fulfillment decisions and relies on multiple logistics providers to perform the fulfillment operations.

We model customer behavior with a general discrete choice model and formulate the joint optimization as a stochastic dynamic program. We propose a tractable deterministic approximation and develop a computationally efficient heuristic with a provable performance guarantee.

Using real datasets collected from our industrial partner, we demonstrate the value of personalizing fulfillment options for the customers and jointly optimizing the options with fulfillment assignments. Our results show that demand management through personalized fulfillment options is prominent when customers prefer quicker fulfillment and when fulfillment capacity is limited. However, an optimized fulfillment operation becomes more critical when customers are more willing to wait.