Apex Logistics Engine

An enterprise fleet management and dispatch optimization platform built for a specialized cold-chain logistics provider. Combines vehicle telematics, real-time traffic prediction, and IoT temperature monitoring to prevent cargo loss and optimize urban delivery routes.

Static dispatching led to 18% delayed deliveries for temperature-sensitive cargo and excessive fuel consumption during metropolitan traffic gridlock. A lack of live telemetry meant refrigeration failures were often discovered only upon warehouse arrival.

Engineered an autonomous dynamic routing engine that recalculates fleet schedules in real time based on GPS telematics, traffic congestion projections, and cold-chain thermal threshold alarms.

Web operations command center built with React and Three.js for 3D geospatial fleet mapping. Real-time solver microservice built in Python FastAPI implementing Vehicle Routing Problem with Time Windows (VRPTW). Apache Kafka event bus streams vehicle sensor readings into TimescaleDB.

Interactive dark/light geospatial dashboard with real-time temperature heatmaps, vehicle route vectors, and automated driver communication triggers.

Full-stack system using Next.js/React, Three.js, Python FastAPI, Apache Kafka, TimescaleDB, and Docker containers orchestrated via Kubernetes.

Cut fleet fuel consumption by 22%. Increased on-time delivery rate to 99.4% across 350+ active vehicles. Reduced daily dispatch schedule computation time from 45 minutes to 12 seconds per shift.