ADAPT
Real-Time Rerouting and Reprioritization
Enable delivery fleets to dynamically adjust routes in response to traffic, road closures, weather, and new demands, using real-time data streams, AI, and mathematical optimization to reduce costs and improve efficiency.
Understanding the Problem
Real-time vehicle rerouting overcomes static planning limitations through continuous optimization powered by real-time data, AI decision-making, and advanced mathematical techniques. With last-mile delivery costs representing 53% of total shipping expenses (up from 41% in 2018), dynamic routing delivers 10-30% operational cost reductions, 15-25% fuel savings, and 20-40% delivery efficiency improvements.
THE CHALLENGE
What Makes it Hard
Solving the NP-hard Vehicle Routing Problem in real-time to optimize delivery routes for hundreds of stops while adapting to changing traffic, weather, customer demands, and vehicle availability constraints.
WHO FACES IT
Combinatorial explosion: A route with 100 stops has over 200,000 possible routing options requiring evaluation in seconds, not hours
Dynamic uncertainties: New customer orders arrive throughout the day, traffic patterns shift, vehicles break down, and weather impacts both demand and vehicle availability
Electric vehicle complexities: EVs require careful route planning for range limitations, charging infrastructure availability, and energy consumption variability
BUSINESS IMPACT
Route optimization reduces costs by 25%, cuts fuel by 30%, and enables 25% more deliveries per driver. UPS saves $300-400M annually eliminating 100M miles.
Cost Reduction
25%
Operational Savings[1]
Route optimization delivers 10-30% reduction in total operational costs through reduced travel time and improved fleet utilization.
Fuel Savings
30%
Cost Reduction[2]
Advanced routing enables 20-30% fuel savings. UPS ORION saves 10M gallons annually, cutting 100M miles from routes.
Delivery Capacity
25%
More Per Driver[3]
Optimization enables 20-30% more deliveries per driver per day without adding vehicles to the fleet.
How We Solve It
Mixed-integer linear programming (Gurobi, CPLEX, HiGHS) provides exact solutions for small instances. Metaheuristics (genetic algorithms, tabu search, adaptive large neighborhood search) deliver near-optimal solutions (within 0.5-1% of optimum) for real-world scale. Hybrid quantum-classical approaches, neural combinatorial optimization and quantum annealing show promise for specific variants.
Hybrid Compute
What We Bring
MILP formulation for exact VRP solutions on small instances with capacity, time window, and flow constraints
Metaheuristic algorithms (genetic, tabu search, ALNS) reaching within 0.5-1% of optimum for hundreds to thousands of delivery points
Dynamic insertion and rolling horizon optimization for real-time route updates as new orders arrive
Hybrid quantum-classical decomposition for large-scale problems (Google OR-Tools, Hexaly, D-Wave Hybrid)
FUTURE POSSIBILITIES
The
Quantum Horizon
Quantum computing targets NP-hard combinatorial nature of the Vehicle Routing Problem through superposition, entanglement, and quantum tunneling. Recent progress pushed boundaries from 4-6 location problems to 13-location problems, which is still far from practical relevance.
Exploratory Work
Quantum computing for VRP remains research and early application phase, with most implementations limited to small-scale instances. QAOA and quantum annealing show promise but face encoding complexity, qubit requirements (O(kN^2) for k vehicles and N locations), and current hardware limitations (~133 qubits on IBM systems). Quantum-inspired classical algorithms provide immediate value without quantum hardware. Hybrid quantum-classical decomposition represents most practical near-term approach. Medium-term (2027-2030) outlook: error-corrected quantum systems enabling practical quantum advantage for specific VRP variants. Long-term (2030+): full-scale quantum optimization of complex routing problems.
Current Research Directions
Hierarchical Multi-Angle QAOA: Solves 13-location VRP through clustered decomposition (advances from 4-6 location limit)
Hybrid quantum-classical decomposition: 13-node VRP requiring 156 qubits achieves results comparable to classical methods
D-Wave hybrid solvers for real-world package delivery: ~2,500 variable binary optimization per truck with heterogeneous fleet support
Interested in quantum research?
Explore proof-of-concept implementations with our team.
Ready to solve this problem?
Talk to our experts about how Strangeworks can help with real-time rerouting and reprioritization.
SOURCES