Computational Approaches for Grocery Home Delivery Services

TL;DR

This paper analyzes and compares various heuristic and optimization methods for solving the Attended Home Delivery problem, focusing on scheduling and routing in grocery delivery services to improve efficiency and scalability.

Contribution

It introduces a flexible framework for solving the Attended Home Delivery problem and provides a comprehensive computational comparison of heuristic and MILP approaches.

Findings

Heuristic methods effectively handle large instances.

Mixed-Integer Linear Programming offers precise solutions for smaller problems.

The approaches are scalable and adaptable to similar routing problems.

Abstract

Grocery home delivery services require customers to be present when their deliveries arrive. Hence, the grocery retailer and the customer must mutually agree on a time window during which the delivery can be guaranteed. This concept is referred to as the Attended Home Delivery (AHD) problem. The phase during which customers place orders, usually through a web service, constitutes the computationally most challenging part of the logistical processes behind such services. The system must determine potential delivery time windows that can be offered to incoming customers and incrementally build the delivery schedule as new orders are placed. Typically, a Vehicle Routing Problem with Time Windows forms the underlying optimization problem. This work is concerned with the use case given by an international grocery retailer's online shopping service. We present an analysis of efficient solution methods that can be employed to AHD services. We provide several heuristic approaches for tackling the steps mentioned above. However, the basic framework can be easily be adapted to be used for many similar Vehicle Routing applications. We provide a comprehensive computational study comparing several algorithmic strategies, combining heuristics utilizing Local Search operations and Mixed-Integer Linear Programs, tackling the booking process. Finally, we analyze the scalability and suitability of the approaches.