Last-Mile Delivery Optimization
In recent years, online shopping has for many consumers become the standard way of purchasing products. Yet, the number of online orders continues to increase strongly as more and more consumers switch to online shopping of fast-moving consumer goods (FMCG), such as packaged foods, beverages, and non-durable household goods. As a consequence of this trend, FMCG delivery services face the challenge of having to process large numbers of orders at a high pace to guarantee fast delivery.
The key to meeting this challenge is to maximize the efficiency of last-mile delivery operations. To this end, delivery services need methods that are able to derive optimal decisions about vehicle routing and order picking in the course of each business day. Developing such methods is challenging, as optimal decision making requires taking into account the uncertainty about upcoming customer orders of the remaining day.
This research project focuses on designing, developing, and evaluating self-learning optimization algorithms that are able to cope with this challenge. By leveraging real-time data, developed algorithms continuously adapt to customer behavior and respond to changing market signals with economically efficient routing and picking decisions.
