Online Ordering Platform City Distribution Based on Genetic Algorithm

TL;DR

This paper presents a genetic algorithm-based method to optimize vehicle routing for online food delivery, reducing costs and improving delivery times for urban distribution.

Contribution

It introduces a novel vehicle routing model with soft time windows and applies a genetic algorithm to minimize distribution costs considering multiple constraints.

Findings

Reduced distribution costs compared to original routing methods

Improved delivery time adherence

Effective vehicle path design for urban distribution

Abstract

Since the rising of the takeaway ordering platform, the M platform has taken the lead in the industry with its high-quality service. The increasing order volume leads the competition between platforms to reduce the distribution cost, which increases rapidly because of the unreasonable distribution route. By analyzing platform distribution's current situation, we study the vehicle routing problem of urban distribution on the M platform and minimize the distribution cost. Considering the constraints of the customer's expected delivery time and vehicle condition, we combine the different arrival times of the vehicle routing problem model using three soft time windows and solve the problem using a genetic algorithm (GA). The results show that our model and algorithm can design the vehicle path superior to the original model in terms of distribution cost and delivery time, thus providing decision support for the M platform to save distribution cost in urban distribution in the future.