An Efficient Vehicle Routing Algorithm for Online Grocer

Abstract

In a growing and highly competitive online grocery market, online grocer Picnic must prioritize optimizing the efficiency of its logistics chain to achieve profitability. Last-mile delivery is a major contributor to operational costs, making its optimization essential. This thesis presents a study on optimizing last-mile delivery operations for Picnic through the application of a Hybrid Genetic Search (HGS) algorithm. The research addresses the Vehicle Routing Problem with Time Windows (VRPTW), factoring in Picnic’s unique operational constraints, including multi-compartment vehicles, a heterogeneous fleet, vehicle time windows, prioritized vehicles, and varied service times.

The study evaluates the performance of the HGS algorithm against Picnic's existing VROOM algorithm in real Picnic instances. Results demonstrate that, on average, HGS consistently outperforms VROOM, achieving significant reductions in total route duration. However, while HGS excels in optimizing route duration, it does not always minimize the number of vehicles used.

This research extends the state-of-the-art Hybrid Genetic Search (HGS) algorithm to tackle the specific challenges of Picnic’s vehicle routing problem. By applying this enhanced algorithm to a real-world setting, it bridges the gap between theoretical optimization advancements and practical industry implementation.