Intelligent Logistics Scheduling System for Food Delivery Using Cloud Computing, Big Data, and Deep Learning

The paper introduces a system solution and tool platform that applies big‑data technology to solve order scheduling problems in the restaurant delivery logistics domain, featuring cloud‑based computation models, deep‑learning prediction algorithms, and extensive data visualisation.

Background: The O2O food‑ordering industry transforms traditional dine‑in consumption into flexible home delivery, reducing costs for users and merchants. Efficient logistics is crucial, requiring intelligent scheduling that integrates massive historical order data, rider location data, and merchant characteristics to achieve global optimal resource allocation while maximizing user experience.

Cloud Computing Model: A multi‑objective dynamic optimization problem is addressed by hierarchical modeling to reduce computational complexity. Basic constraints (time, distance) are captured at the lower layer, while high‑level variables (region, weather, overall capacity) are tuned as optimization parameters. A bipartite graph maximum‑weight matching algorithm finds the optimal rider‑order assignments, and cloud‑based virtual queues manage order dispatch timing to adapt to real‑time changes.

Deep Learning Prediction: Leveraging millions of daily orders, the system extracts tens of millions of training samples, builds comprehensive merchant and dish tag features, and designs a deep‑learning evaluation framework sensitive to different preparation time intervals. The model improves dish‑preparation time estimation by 3%‑14% and reduces average delivery time by about 0.8 minutes for 95% of orders.

Visualization Platform: Real‑time rider trajectory tracking, comprehensive parameter logging for post‑hoc analysis, and geographic efficiency heatmaps help stakeholders understand and trust the scheduling system, reducing debugging costs and enabling interactive exploration of logistics performance.

Conclusion: By combining cloud computing, AI‑driven deep learning, and big‑data analytics, Baidu Takeaway’s intelligent logistics scheduling system achieves significant improvements in dispatch efficiency and user experience, and points toward future AI‑enhanced, self‑optimising logistics solutions.