Logistics optimization
Plan delivery routes, fleet assignments and schedules with mathematical optimization and rerun plans quickly when orders, vehicles or road conditions change, using GPU-accelerated solvers alongside existing planning systems.
The problem
Planners must assign orders to vehicles and drivers, sequence stops within time windows and capacity limits, and react when trucks break down, orders arrive late or roads close. Large problems can take solvers a long time, which can push teams to compute a plan once in advance and adjust it by hand during the day.
The engineering problem is to express these decisions as optimization models, solve them fast enough to replan during the day, and connect the results to dispatch and warehouse systems.
The approach
Routing is usually modeled as a vehicle routing problem, and wider planning as linear or mixed-integer programs. NVIDIA cuOpt is an open source, GPU-accelerated optimization engine that supports vehicle routing, linear programming and quadratic programming, with mixed-integer programming in beta. It works with modeling interfaces such as AMPL, CVXPY, PuLP, JuMP and GAMSPy, installs through pip, Conda, Docker or NGC, and can run standalone or inside agent workflows, including with LLM NIM microservices that help turn business questions into models.
Established CPU solvers, open source or commercial, handle many logistics problems well, especially smaller instances or cases that need proven optimal answers. GPU solving is most interesting for large instances or frequent re-solves where time to a good feasible plan matters.1
Conceptual architecture
Diagram as a list
Applications & solutions
- Orders, fleet and constraint dataOrders, time windows, capacities, drivers and depotsConnects to Optimization model (VRP, LP or MILP)
- Travel time and distance matrixRoad network costs from a routing engineConnects to Optimization model (VRP, LP or MILP)
- Fleet simulation (optional digital twin)Tests plans before releaseConnects to Optimization model (VRP, LP or MILP)
- Dispatch and transport management systemSends routes to drivers and tracks execution
Models & frameworks
- Optimization model (VRP, LP or MILP)Mathematical formulation of the planning decisionConnects to GPU solver (cuOpt)
Inference & runtime software
- LLM assistant for formulationHelps planners turn questions into model changesConnects to Optimization model (VRP, LP or MILP)
Accelerated computing
- GPU solver (cuOpt)Finds feasible, improved plans quicklyConnects to Dispatch and transport management system
Technologies and their roles
cuopt1
Optimization engine
Open source GPU solver for routing, linear and quadratic programs, with mixed-integer programming in beta and common modeling interfaces.
nim1
Language model serving
cuOpt lists LLM NIM microservices for turning natural-language business problems into models.
cuda-x-data-science2
Data preparation
GPU dataframes can prepare large order and telemetry tables before optimization.
omniverse1
Fleet simulation
cuOpt lists Omniverse digital twins for fleet simulation among its integrations.
What you need first
- Clean order, location, time window and vehicle data
- A travel time or distance matrix from a routing engine
- Operations research skills to formulate and validate models
- Integration with dispatch and transport management systems
- A baseline plan to compare results against
Risks and how to reduce them
- The model ignores real-world constraints
- Review plans with dispatchers and add missing rules before automating.
- Beta solver features1
- Mixed-integer programming in cuOpt is beta; validate solutions and keep a fallback solver.
- Plans change too often for drivers
- Replan only on defined events and freeze near-term stops.
- Speed traded for accuracy1
- NVIDIA notes its LP speed-ups apply when lower-accuracy solutions are acceptable; check solution quality.
Related
Sources
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