Order dispatching via deep reinforcement learning

Abstract

In this thesis, the unique order dispatching problem of Getir, a retail and logistics company, has been studied. Getir serves in many cities and multiple countries, and its service area is expanding day by day. Getir, which serves thousands of customers every day in many different fields, is the pioneer of the market in this field. In this thesis, it has been studied on ultra-fast delivery, which is the first and most known service area of the company, which Getir found and started to apply as a first in the world. The aim of Getir's ultra-fast delivery business model is to deliver orders to its customers within minutes. In this business model, orders are fulfilled from the company's warehouses. It is a very challenging goal to complete order delivery in a very short time. Achieving an ultra-fast delivery goal becomes a real problem due to traffic congestion, high numbers of orders at certain times of the day or on certain days of the week. In addition, due to the Covid-19 pandemic and changing customer habits, people increasingly prefer home delivery and shopping method. For this reason, serious changes can be observed in the expected number of orders on a daily and weekly basis. Previously unknown curfews or other restrictions cause changes in the expected number of orders and their content. Therefore, it is not possible to predict these changes with data analysis and estimation methods. For these reasons, an order dispatching algorithm that can adapt to changing conditions is vital. In the ultra-fast delivery model, the goal is to serve as many customers as possible within the predetermined and promised time. Orders can be placed at any time during the working hours of the warehouses in the customer's service zone. It is decided to accept or reject the incoming order according to the order density of the relevant warehouses in the region and the courier shift plans. In the decision-making algorithm here, we recommend using a deep reinforcement learning algorithm instead of a rule-based structure that does not violate constraints. We suggest that an algorithm should be used that can keep up with the growth rate of Getir, which is a fairly fast growing company, and can adapt to the different characteristics of the regions. Before deep reinforcement learning methods that can be applied for this problem, we describe the related problem of Getir and one of the methods used by the company. We discuss the problems, limitations and shortcomings of the method used. We compare and highlight the differences between the proposed method and the current method. We measure the success of the approaches by comparing the proposed methods and the currently used methods over the actual order data. In the ultra-fast delivery business model, it is aimed to deliver the order to the user within 10-15 minutes.