AI-Powered Logistics Orchestration
Implements a **real-time multi-agent AI system** with 6 specialized agents powered by different LLMs (Claude, GPT-4, Gemini) that process IoT telemetry from fleet vehicles, optimize routes dynamically, resolve conflicts using MCDA analysis, and generate actionable decisions with 94%+ confidence scores and detailed reasoning chains..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Scenario selection interface presenting weather disruption, fleet optimization, and emergency response options
Multi-agent orchestration workflow showing task breakdown, execution flow, and real-time agent coordination
Weather disruption response plan with accelerated batch strategy and actionable logistics recommendations
Comprehensive analysis results with technical metrics, cost savings, delivery optimization, and confidence scores
AI Agents
Specialized autonomous agents working in coordination
Master Orchestrator
Complex logistics scenarios require coordinating multiple specialized analyses and aggregating diverse agent outputs into coherent executable decisions.
Core Logic
Manages **task delegation** across all specialized agents using Claude 3.5 Sonnet (200K context). Handles workflow management, decision aggregation, and synthesizes recommendations. Coordinates the 5-phase thinking stream: Analyzing, Reasoning, Planning, Evaluating, and Deciding.
Risk Analyzer
Logistics operations face multiple risk factors including weather, equipment failures, traffic incidents, and supply disruptions that require proactive identification and mitigation.
Core Logic
Powered by **GPT-4 Turbo** (128K context), performs comprehensive risk identification, impact analysis, and probability assessment. Generates mitigation strategies with confidence scoring, provides 24-hour/7-day/30-day risk forecasts, and calculates risk-adjusted recommendations.
Route Optimizer
Delivery routes must balance multiple constraints including traffic conditions, time windows, vehicle capacity, and driver hours while minimizing fuel costs and emissions.
Core Logic
Implements **A* pathfinding algorithms** using Claude 3 Haiku for rapid optimization. Performs real-time traffic analysis, time-window optimization, and multi-stop planning. Calculates optimal batch strategies (e.g., Accelerated Morning Batch) with estimated fuel savings and carbon reduction.
Resource Allocator
Fleet resources must be efficiently allocated across competing delivery demands while considering vehicle capacity, driver availability, maintenance schedules, and load balancing.
Core Logic
Leverages **Gemini 1.5 Pro** (1M context window) for comprehensive capacity planning. Performs load balancing across 28+ fleet vehicles, utilization optimization targeting 85%+ efficiency, and constraint satisfaction to ensure all deliveries meet SLA requirements.
Conflict Resolver
Logistics decisions often involve competing priorities and trade-offs between cost, speed, reliability, and customer satisfaction that require systematic resolution.
Core Logic
Applies **Multi-Criteria Decision Analysis (MCDA)** using GPT-4o (128K context). Evaluates and ranks solution alternatives, performs trade-off evaluation with pros/cons analysis, builds consensus across agent recommendations, and generates confidence-weighted final decisions.
Communication Coordinator
Logistics changes require timely notifications to drivers, customers, and stakeholders with appropriate escalation for critical issues.
Core Logic
Handles **notification routing** and message templating using Claude 3 Haiku for speed. Manages escalation workflows for critical alerts, generates status update communications, and maintains audit logs of all stakeholder notifications with delivery confirmation.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization