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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM60519

Zarfo: An AI-Driven Ecosystem for Closing the Loop on Urban Food Waste

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM60519

Volume:

10 |

4 |

April 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

FM

Farhat Momin

SK

Seemin Khan

IS

Isha Shaikh

PDD

Prof. Dinesh Deore

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Abstract

Zarfo is a full-stack, AI-powered food waste management and route optimization platform that bridges the gap between surplus food in the hospitality sector and individuals in need. The system integrates a Random Forest Classifier (95.8% accuracy) and Regressor (MAE Rs. 8.24) for real-time Sell/Donate classification and dynamic markdown pricing, a Google Cloud Route Optimization API for Vehicle Routing Problem (VRP) solutions achieving 24% average distance reduction vs. greedy baselines, and a Socket.io real-time layer delivering sub-100ms event propagation across four role-based portals (Hotel, User, Worker, Robin). Built on Node.js + Express.js (MVC), MongoDB, React + Tailwind CSS, and Python FastAPI, the platform processes orders non-blockingly via setImmediate(), ensuring instant user responses while background route computation completes within 0.8–5.2 seconds. Load testing at 10 req/s for 60 seconds yields 100% success rate with P99 latency of 298ms. The system aligns with UN SDG 2 (Zero Hunger), SDG 11 (Sustainable Cities), and SDG 12 (Responsible Consumption).

KEYWORDS

Random Forest Food Redistribution Route Optimization Google Cloud API Socket

Cite This Article

Farhat Momin, Seemin Khan, Isha Shaikh, Prof. Dinesh Deore, "Zarfo: An AI-Driven Ecosystem for Closing the Loop on Urban Food Waste," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, April 2026.

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Publication Date

4/20/2026

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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