EURO 2024 Copenhagen
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1904. Online Restaurant Meal Delivery Problem: Order Bundling and Fair Assignment

Invited abstract in session WD-58: Logistics 3, stream VeRoLog - Vehicle Routing and Logistics.

Wednesday, 14:30-16:00
Room: S07 (building: 101)

Authors (first author is the speaker)

1. Ke Fang
Management School, Lancaster University
2. Ahmed Kheiri
University of Manchester
3. Christopher Kirkbride
The Management School, Lancaster University

Abstract

Online food ordering is well-developed nowadays, with many people choosing to order their meal online and waiting at home instead of going to a restaurant. We consider a dynamic online restaurant meal delivery problem (ORMDP) where a pool of drivers deliver food from multiple restaurants to ordering customers. The objectives are to reduce both delivery delays and unfairness for drivers to satisfy those two main stakeholders in the meal delivery process. In practice, order delays increase significantly when the demand for orders is high relative to the number of available drivers. To address this issue, we consider the ORMDP as two sub-problems, order bundling and order assignment. First, we implement a policy-based order bundling algorithm that gather orders into groups within drivers’ capacity. Second, we propose an order assignment algorithm to allocate the groups to drivers considering both delay reduction and fairness to drivers. In order to enhance realism, we emulate the behavior of drivers as they await assignments by employing an Iterative K-means Clustering approach (IKC). This method entails clustering proximate restaurants into zones, from which drivers opt for selections in accordance with probability distributions. Finally, we developed a discrete-time simulation model to test our methods using public data. The experimental results show that order bundling and fair assignment can effectively improve the delivery service in ORMDP.

Keywords

Status: accepted

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