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Operational Control Center Crew Resource Platform

Crew Resource Intelligent Platform

Crew Scheduling

AI-driven crew rostering, rule-traceable compliance and real-time risk monitoring and crew resource assessment for airline flight operations

Explore Crew Resource Platform

Crew Resource Intelligent Platform

Crew Scheduling

ZDH's Crew Resource Intelligent Platform is built to unify rule management, automated rostering and real-time operational monitoring for airline flight crew. The platform manages hundreds of scheduling constraints — from regulatory flight-time and rest limitations to company-specific pairing rules — with every rule traceable back to its governing manual or regulation, version and effective date. A next-generation AI scheduling engine evaluates the cascading impact of every crew assignment across the roster, dramatically compressing planning time while keeping every plan compliant. Real-time dashboards, dynamic risk monitoring and sandbox simulation give crew planners the visibility and foresight to keep operations compliant, resilient and cost-efficient.

Core Functions

Eight integrated capabilities powering end-to-end crew resource management.

Operations cockpit dashboard with scheduling efficiency, cost assessment and live operations map

Real-Time Operations Cockpit

Configurable Scheduling Rules

Regulation-Traceable Rule Library

AI Rule Assistant (Natural-Language Q&A)

AI Scheduling Engine

Crew Pairing & Gantt Workbench

Dynamic Risk Monitoring & Recovery

Sandbox Simulation & What-If Analysis

Product Advantages

AI + Heuristic Automated Scheduling

Breaking from the traditional sequential, pairing-by-pairing assignment model, the auto-scheduling engine combines AI with heuristic algorithms to evaluate the ripple effect of every crew assignment — the “butterfly effect” — across the entire roster, then dynamically rebalances the plan in response. A full month’s roster for 800 crew members that once took more than 13 hours to build by hand is now produced in under 1 hour, on a visual Gantt workbench that groups flights into crew-pairing rings and flags scheduling exceptions as they arise. The engine is developed by ZDH’s in-house team of mathematics PhDs, AI specialists and senior software engineers.

800
crew members / month
13h→1h
roster build time
390+
scheduling rules
Crew scheduling workbench showing flight rings by day and the crew resource pool
AI architecture: resource data, AI rule library, AI algorithm engine, compliance check and conflict detection feeding intelligent decision output and operations monitoring
Rule library listing scheduling rules with severity, expression logic and source regulation citations
AI-assisted rule generation from source regulatory text

Rule-Grounded, Auditable AI Assistant

Every one of the platform’s 390+ scheduling rules — covering personnel qualifications, crew pairing, plateau-airport requirements, international single-pilot operations and more — is mapped to its source regulation or company manual, complete with version number and effective dates. An integrated AI assistant answers natural-language questions about any rule and returns its supporting citation, so every scheduling decision remains fully traceable and audit-ready. Regulatory coverage supports CAAC, FAA, EASA and other multilingual, multi-jurisdiction requirements.

Proactive Risk Monitoring & Recovery

When a flight is delayed, cancelled or diverted, dynamic risk monitoring immediately assesses the impact on every affected crew member and surfaces it for review — flagging duty-time overruns, missing qualifications, insufficient rest and other exceptions before they disrupt operations. Planners can confirm and track a risk, or submit a recovery plan in a single click, closing the loop between disruption and resolution.

Real-time risk monitor listing duty-time and recovery alerts with a flight's rule hits and next steps
Sandbox analysis of duty-hour balance across the crew pool by base and rank

Sandbox Simulation & Scenario Planning

A dedicated sandbox lets crew planners simulate rosters against real historical operations or algorithm-generated scenarios and evaluate seasonal resource matching, quantifying the trade-offs — from long- versus short-term outstation postings and positioning and layover costs, to captain and first-officer hour balancing — before a plan goes live. Built-in analytics surface resource-matching trends, and the system proposes optimization suggestions for planners to act on.