Vehicle routing problems (often shortened to “VRP”) sit at the core of route optimization for delivery, service, and transportation fleets. A VRP asks a simple business question: which stops should each vehicle visit, in what order, and when? The details make it powerful: capacity limits, customer time windows, driver rules, depot choices, and service commitments.
This FAQ focuses on how VRP models connect to real operations and where optimization with Gurobi typically fits.
A VRP is a planning problem in which customer stops are assigned to vehicles and sequenced to meet operational constraints while optimizing an objective. Common objectives include minimizing total distance, total time, fleet size, late deliveries, or a weighted mix aligned to service-level and cost KPIs. VRPs are the workhorses behind daily fleet routing, last-mile delivery, and dispatch planning.
“Shortest path” problems identify the best path between two points. The traveling salesperson problem (TSP) finds the shortest tour that visits all stops exactly oncewith one vehicle and returns to the starting point. A VRP generalizes this to multiple vehicles and adds business constraints, such as vehicle capacities, time windows, driver shifts, and multiple depots. In practice, VRP is what you use when operations are constrained and multi-vehicle.
Teams often start with a capacitated VRP (limits on weight, volume, or stops per route) and quickly add real requirements, such as:
Each variant changes feasibility and the tradeoffs between cost and service. In practice, these variants are often combined.
Most production routing models revolve around a small set of levers:
A good model captures the constraints that actually cause dispatch failures (missed windows, overloads, overtime)—not every edge case on day one.
A VRP is typically measured by a mix of cost and service outcomes, such as total miles, drive time, number of vehicles used, on–time arrival rate, total overtime, and route balance across drivers. Environmental metrics (such as fuel or CO2 proxies) can also be embedded through cost weights or explicit limits. The right KPI set depends on your operating model: parcel networks often prioritize on–time arrivals and route density, while field service may prioritize appointment compliance and technician utilization.
Many VRP formulations are naturally expressed as mixed-integer linear programming (MILP). The Gurobi Optimizer is a mathematical optimization solver used to solve these models and return high-quality route plans consistent with constraints. When solved to completion, Gurobi provides a provably optimal solution, or proves that the model is infeasible or unbounded. For MIP models, if you stop early (for example, due to a planning time limit), it returns the best incumbent solution found along with an optimality gap, which helps teams quantify solution quality.
Time windows turn routing into a schedule plus a route. They can create hard feasibility issues (a stop cannot be served within its promised window given travel times and prior commitments) and can force extra vehicles or different depot choices. Operationally, time windows are where data quality matters: inconsistent service times, unrealistic promised windows, or missing break rules can make a model look infeasible even when dispatchers routinely rely on informal workarounds.
Gurobi solves deterministic models, so uncertainty is handled by modeling choices rather than automatic forecasting. Common approaches include scenario sets (multiple travel-time or demand realizations), buffers on travel or service time, or robust or stochastic formulations at a high level. The goal is to stress-test routes against plausible conditions, then choose a plan that balances efficiency with service risk.
Routing is a decision support workflow, not just a one-time solve. Typical practices include re-optimizing on disruptions (late starts, cancellations), validatingfeasibility against current constraints and data, and monitoring gaps between planned and actual execution. If operators manually adjust routes, those changes can violate constraints, so it is important to recheck feasibility or re-optimize with the new conditions.
Vehicle routing problems translate real fleet decisions into an optimization model that can meaningfully improve cost, service, and reliability. The most successful VRP efforts start with a clear KPI target, model the constraints that drive dispatch pain, and iterate as data and policies mature.
With MILP-based route optimization, Gurobi provides a strong foundation for producing feasible, explainable routing plans and quantifying tradeoffs when perfect plans are not possible.
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