Where QuantFenix DeliversMeasurable Value

Real-world optimization problems where our multi-objective routing targets cost and performance improvements

Optimization Use Cases

Upload your CSV → QuantFenix finds the best-value backend and generates your audit-ready report

Vehicle Routing Problem (VRP)

Logistics, Transport, E-commerce

Optimize delivery routes for multiple vehicles with capacity constraints

Data: routes.csv – ~500 nodes, ~30 vehicles

Routing: Classic baseline + Hybrid via supported quantum backends when instance grows

−20–45% compute cost, −10–30% runtime (indicative)

Knapsack / Portfolio Optimization

Procurement, Hedge Funds

Maximize value within budget constraints for investments or purchases

Data: portfolio.csv – weight, value, budget

Routing: Dual-canary: classic baseline vs. hybrid

−15–30% cost per optimal run; +5–15% better ratio (indicative)

Shift Scheduling

Staffing, Manufacturing, Healthcare

Optimize staff schedules with availability constraints and business rules

Data: schedule.csv – shifts, availability, rules

Routing: Classic baseline; Hybrid at high constraint density

−15–35% time-to-solution, +5–20% coverage rate (indicative)

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Supply Chain Routing

Distribution, Energy

Optimize distribution networks with capacity and cost constraints

Data: supply.csv – depots, capacity, cost

Routing: Classic for small problems; Hybrid for multiple restrictions

−15–35% compute cost, −10–25% latency (indicative)

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Production Line Balancing

Manufacturing, Automotive, Electronics

Balance production lines for optimal throughput and energy efficiency

Data: line.csv – steps, machine time, sequences

Routing: Hybrid simulation via supported backends when energy goals exist

−10–20% energy consumption, −15–25% overcapacity (indicative)

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Demand Forecast Optimization

Retail, Food Suppliers

Optimize demand forecasting with constraint-aware ML models

Data: sales.csv – SKU, region, demand

Routing: Classic ML-forecast + constrained optimizer; hybrid when needed

+8–18% forecast accuracy, −10–25% compute cost (indicative)

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University Lab / PoC

Universities, Innovation Labs

Reproducible research with full audit trails and manifest files

Data: example.csv – 10–20 nodes

Routing: Simulator backends

100% reproducibility (manifest) (indicative)

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Energy Grid Load Balancing

Energy & IoT

Optimize energy grid load distribution for stability and cost

Data: grid.csv – nodes, load, price/MW

Routing: Hybrid auto-switches based on data size & budget

−20–35% operational cost, +10–20% stability (indicative)

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Why These Results Matter

Indicative ranges; actual outcomes verified in pilot with audit report and locked baseline

Cost Reduction

Targets defined in pilot; typical ranges shown in benchmarks

Performance Gains

Faster time-to-solution and improved accuracy across all problem types

Full Auditability

Complete audit trails and reproducible results for every optimization run

Ready to optimize your problem?

Upload your CSV and get instant cost analysis with our multi-objective routing