Predict, Reposition, and Allocate: A Greedy and Flow-Based Architecture for Sustainable Urban Food Delivery

TL;DR

This paper introduces an eco-friendly food delivery optimization framework that combines demand prediction, routing, and order allocation to reduce environmental impact while maintaining efficiency in urban settings.

Contribution

It presents a novel integrated framework using greedy algorithms and network flow models for sustainable food delivery, addressing NP-hard routing and order allocation challenges.

Findings

Reduces vehicle count in urban food delivery.

Minimizes environmental impact through optimized routing and allocation.

Creates a sustainable and efficient delivery ecosystem.

Abstract

The rapid proliferation of food delivery platforms has reshaped urban mobility but has also contributed significantly to environmental degradation through increased greenhouse gas emissions. Existing optimization mechanisms produce sub-optimal outcomes as they do not consider environmental sustainability their optimization objective. This study proposes a novel eco-friendly food delivery optimization framework that integrates demand prediction, delivery person routing, and order allocation to minimize environmental impact while maintaining service efficiency. Since recommending routes is NP-Hard, the proposed approach utilizes the submodular and monotone properties of the objective function and designs an efficient greedy optimization algorithm. Thereafter, it formulates order allocation problem as a network flow optimization model, which, to the best of our knowledge, has not been explored in the context of food delivery. A three-layered network architecture is designed to match orders with delivery personnel based on capacity constraints and spatial demand. Through this framework, the proposed approach reduces the vehicle count, and creates a sustainable food delivery ecosystem.