MOVE

Transportation Network Design

Design strategic transportation networks including facility locations, lane selections, and mode choices to optimize long-term logistics costs, capacity, and service levels.

Understanding the Problem

Transportation network optimization addresses one of logistics' most impactful challenges, where last-mile delivery represents 50-60% of total shipping costs. Organizations must navigate traffic congestion (18% of problems), strict delivery time windows (20%), truck capacity constraints (22%), and real-time disruptions while meeting rising customer expectations for speed and tracking. The Vehicle Routing Problem (VRP) and its variants form the mathematical foundation for these optimization challenges.

THE CHALLENGE

What Makes it Hard

Transportation optimization requires solving the NP-hard Vehicle Routing Problem to find optimal routes for fleets visiting customers while satisfying capacity constraints, time windows, pickup/delivery requirements, and real-time adaptability needs. Routing engines must apply 250+ operational constraints including driver availability, vehicle types, service levels, and road restrictions across volatile urban conditions and frequent disruptions.

WHO FACES IT

Logistics and transportation managersFleet operations directorsDistribution center and warehouse managersSupply chain executives and plannersSustainability and environmental compliance teams

Combinatorial complexity of VRP creates exponentially growing solution spaces as problem size increases

Real-time adaptability requirements under volatile traffic, weather, and demand conditions

Multi-modal and intermodal coordination challenges requiring mode selection and transfer point optimization

BUSINESS IMPACT

Transportation optimization delivers 3,700% ROI, 15-25% cost savings, and 10 million gallons of annual fuel savings. UPS ORION saves $300-400M yearly.

ROI

3,700%

In 6 Months^[1]

North American pulp manufacturer achieved 3,700% ROI in 6 months using anyLogistix freight planning tools.

Cost Reduction

15-25%

Transportation Savings^[2]

Well-deployed TMS generates 15-25% savings on transport costs with 20-30% administrative productivity gains.

Fuel Savings

10M

Gallons/Year^[3]

UPS ORION saves 10M gallons of fuel annually, 100M miles less traveled, and $300-400M in annual savings.

How We Solve It

Solve Vehicle Routing Problems using mixed-integer linear programming for small-to-medium instances, advanced metaheuristics (genetic algorithms, ant colony optimization) for large-scale problems, and hybrid quantum-classical algorithms for complex scenarios. Integration with real-time traffic data, GPS tracking, and AI-driven route learning enables dynamic optimization, while digital twins allow scenario testing without disrupting real operations.

Heterogeneous
Hybrid Compute

What We Bring

MILP-based exact solutions for VRP variants (CVRP, VRPTW, VRPPD, EVRP) up to hundreds of stops

Advanced metaheuristics (GA, ACO, hybrid approaches) handling thousands of customers and complex constraints

GPU-accelerated optimization enabling real-time CVaR-based route planning under uncertainty

Quantum-inspired algorithms and QAOA for large-scale combinatorial routing problems

FUTURE POSSIBILITIES

The
Quantum Horizon

Volkswagen demonstrated 30% fleet efficiency improvement using quantum route optimization in Lisbon traffic. Current quantum hardware (IBM NISQ devices) limited to 5 nodes and 2 vehicles, but hybrid quantum-classical frameworks show promise. Digital annealing (Fujitsu) and D-Wave hybrid solvers demonstrate competitive performance versus Gurobi for industrially relevant scenarios.

Exploratory Work

Technology advancing faster than predicted with supply chain leaders advised to prepare now for quantum computing's arrival. While current quantum hardware cannot solve production-scale routing, quantum-inspired classical algorithms deliver benefits today. The most likely path involves hybrid frameworks leveraging quantum sampling for candidate solutions refined by classical local search, though transparent benchmarking is needed to understand where quantum advantages truly exist. Autonomous vehicles and 10,000x simulation speed targets will further drive quantum optimization adoption.

Current Research Directions

QAOA formulations for VRP showing promise but scaling quadratically with customer count (qubit requirements)

Quantum Walk-based Optimization Algorithm (QWOA) demonstrating better convergence than QAOA by restricting to valid solution subspace

Hybrid quantum-classical algorithms dividing problems into quantum-optimized sub-problems with classical orchestration and ML-enhanced noise mitigation