MOVE
Inbound and Outbound Logistics Optimization
Optimize vehicle routing, inventory management, and production sequencing for automotive logistics to reduce costs, improve delivery performance, and enhance manufacturing efficiency.
Understanding the Problem
The automotive logistics market valued at $337.58 billion in 2024 and projected to reach $811.66 billion by 2035 faces unprecedented cost pressures from volatile trade policies, geopolitical tensions, and the EV transition. More than 40% of industry respondents cite cost pressure as the dominant challenge. Real-world deployments by Ford, Volkswagen, and Toyota demonstrate 20-83% improvements in key operational metrics, with quantum-enhanced solutions beginning to show commercial viability.
THE CHALLENGE
What Makes it Hard
Automotive logistics optimization addresses complex vehicle routing with milk-run pickups from multiple suppliers, just-in-time delivery to manufacturing lines, production sequencing for 1500+ vehicle variants, and inventory management across multi-tier supplier networks, all under constant disruption from supply chain volatility.
WHO FACES IT
Cost pressure from volatile trade policies, tariffs, geopolitical tensions, and EV transition uncertainty squeezing margins structurally
Just-in-time manufacturing vulnerabilities where milk-run approach and JIT delivery create single points of failure bringing production lines to halt
Vehicle routing complexity determining optimal routes for milk runs as NP-hard combinatorial optimization with 6×10^11 possible routes for just 15 points
BUSINESS IMPACT
Logistics optimization delivers 20-50% transportation cost reduction and 83% faster scheduling (30 minutes to under 5), with consistent year-over-year savings.
Scheduling Speed
83%
Faster[1]
Ford Otosan reduced vehicle production sequencing time from 30 minutes to under 5 minutes using quantum-hybrid solvers.
Transportation Costs
20-50%
Reduction[2]
Milk-run logistics optimization consistently delivers 20-50% transportation cost reduction versus point-to-point delivery methods.
Annual Savings
3%
Year Over Year[3]
Toyota Logistics Services achieved 3% annual cost reduction through network optimization, counteracting rising logistics costs.
How We Solve It
Automotive logistics optimization spans multiple problem types: Vehicle Routing Problems (VRP) with time windows, pickup/delivery, and multiple depots formulated as Mixed-Integer Linear Programs; Inventory Routing Problems (IRP) jointly optimizing inventory and routes; Production Sequencing as highly constrained multi-objective optimization; and Network Design problems for strategic facility and sourcing decisions.
Hybrid Compute
What We Bring
Commercial MIP solvers (Gurobi, CPLEX) with branch-and-cut algorithms for small-medium VRP instances finding optimal solutions
Metaheuristic approaches (Tabu Search, Genetic Algorithms, Ant Colony) reaching solutions within 0.5-1% of optimum for large-scale problems
Machine learning enhanced optimization with neural networks learning to predict good solutions and reinforcement learning for routing decisions
Hybrid quantum-classical workflows using quantum annealing for hard subproblems with classical methods for coordination and refinement
FUTURE POSSIBILITIES
The
Quantum Horizon
Quantum computing for automotive logistics shows real-world deployment success. Ford Otosan achieved 83% scheduling time improvement using D-Wave quantum-hybrid solvers for production sequencing. Volkswagen deployed quantum routing for 9 public buses in Lisbon and optimized traffic flow for 10,000 Beijing taxis. However, quantum advantage remains limited to specific problem types with hybrid classical-quantum approaches delivering best results.
Exploratory Work
Near-term (2025-2027): Hybrid quantum-classical expected to deliver a few percentage points improvement in specific areas like routing or scheduling. Most practical applications will be hybrid, not pure quantum. Focus on problems where classical methods struggle: dynamic routing, real-time optimization, very large combinatorial spaces. Mid-term (2028-2032): NISQ devices for specific advantage in narrow applications with hybrid production systems combining quantum and classical. Long-term (2030+): Transformative applications including real-time optimization of entire supply chain networks and coordinated optimization across multiple manufacturers. Consensus: Quantum has great potential but faces current hardware limitations, necessitating further advancements for practical implementation.
Current Research Directions
QAOA for VRP instances up to 21 qubits with clustering approaches enabling scaling to 100+ locations through decomposition
Quantum annealing for production scheduling (Ford Otosan), traffic routing (VW Lisbon), and multi-truck VRP with D-Wave hybrid solvers
Hybrid quantum-classical approaches: clustering-based methods (classical ML clusters locations, quantum optimizes within clusters)
QUADRO framework for drone delivery using QAOA enhanced by classical heuristics, competing with classical methods using <100 qubits
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 inbound and outbound logistics optimization.
SOURCES