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

2025-07-21

AI Summary

Existing food delivery platforms neglect environmental sustainability, resulting in excessive carbon emissions. Method: This paper proposes a three-tier collaborative optimization framework for urban food delivery, integrating demand forecasting, rider route planning, and order assignment. It formulates order assignment as a capacitated network flow problem—a novel modeling approach—and designs a greedy algorithm leveraging submodularity and monotonicity to minimize vehicle utilization. Contribution/Results: The framework ensures timeliness and spatial matching while significantly reducing fleet requirements and per-order carbon emissions. Experiments demonstrate an 18.7% reduction in scheduled vehicles and a 22.3% decrease in carbon emissions compared to baseline methods. The approach provides a scalable algorithmic paradigm and practical pathway toward efficient, low-carbon urban instant delivery systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Minimize environmental impact of urban food delivery
• Optimize demand prediction and delivery routing
• Reduce vehicle count via network flow allocation

Innovation

• Eco-friendly framework integrating prediction and routing
• Greedy algorithm leveraging submodular objective properties
• Network flow model for optimal order allocation