A Gurobi optimization example is easiest to understand when you start from a real decision: what to do, how well, and under which limits. In practice, teams use Gurobi Optimization to choose production plans, route vehicles, assign staff, and allocate budgets while respecting capacity, service, and policy constraints. Â
This FAQ collects common example patterns, what to model, and how to evaluate outcomes without getting lost in solver internals.Â
The best examples mirror how a business actually makes decisions. A useful example has (1) clear decisions (what is chosen), (2) an objective tied to a KPI (cost, margin, lateness, emissions), and (3) constraints that reflect reality (capacity, labor rules, demand satisfaction, SLAs). Even a small pilot should include at least one tradeoff that a spreadsheet struggles to handle, such as discrete yes-no choices, competing priorities, or shared resources across time.
Production planning is a common starting point. Decisions include how much to make in each period and whether to run overtime. The objective often minimizes total cost (production, setup, overtime, inventory, backorders if allowed). Constraints capture machine capacity, material availability, minimum batch sizes, and service targets. This example shows why optimization modeling matters: the best plan is not just locally efficient, it coordinates limited capacity across products and weeks.Â
Vehicle routing adds network structure and sequencing decisions. Decisions include which stops each vehicle serves and in what order, sometimes with pickup and delivery pairing. Objectives can minimize miles, driver hours, or late deliveries. Constraints include time windows, vehicle capacities, maximum route duration, and depot rules. Routing examples are typically mixed-integer because the serve-or-not and order decisions are discrete.
Yes. Workforce assignment examples cover call centers, hospitals, retail, and field service. Decisions include staffing levels by shift, assigning employees to roles, and approving overtime. Objectives can minimize labor cost while meeting forecasted demand and skill coverage. Constraints include labor regulations, rest rules, fairness policies, and required certifications. These models often combine continuous choices (hours) with discrete choices (assignment), making them good MILP examples.
LP models use continuous decisions, such as how much flow or inventory to hold, and are appropriate when fractional decisions are meaningful. MILP models include integer or binary decisions, such as turning a line on or off, selecting a supplier, opening a facility, or assigning a job to a machine. LP and MILP are not the same: MILP can express richer business logic but may be harder to solve depending on structure and data. A practical rule is to start with the simplest model that captures the decisions you actually control, then add integrality only where fractional results would be unacceptable.
Focus on decision-relevant data, not every data field you have. Typical inputs include demand or required service levels, capacities, costs or penalties aligned to KPIs, eligibility rules (who can do what), and calendars (shifts, lead times, time windows). The highest-impact work is usually validating units, time alignment, and whether constraints reflect real policies. Poor data consistency can make a model infeasible or produce plans that look optimal but are operationally unusable.
When solved to completion, Gurobi can provide a proven optimal solution or prove infeasibility or unboundedness for the model you defined. If you stop early, Gurobireturns the best incumbent found and an optimality gap, which tells you how far that incumbent could be from the best possible objective value. In business terms, the gap helps you decide whether the current plan is good enough to execute now or whether it is worth allowing more solve time or refining the model.
Gurobi solves deterministic models, so uncertainty is handled by how you formulate inputs. Common approaches include scenario analysis (separate runs for different demand or travel times), parameter sweeps (stress-testing key costs or capacities), and robust or stochastic formulations at a high level. The goal is to understand sensitivity and risk exposure, not to claim guaranteed protection against all outcomes. Keep scenarios actionable: tie them to decisions like safety stock, extra vehicles, or flexible labor.
Evaluate an optimization example with a before-and-after framework using your own baseline. Compare objective components (cost, penalties, overtime), service metrics (fill rate, on-time delivery), and operational metrics (capacity utilization, changeovers, route hours). Many teams find the biggest value in making tradeoffs explicit and repeatable, not just in a single best plan.
A strong Gurobi optimization example starts from a real decision and converts it into an optimization model with a clear KPI and credible constraints. Whether you choose an LP for continuous flows or a MILP for discrete assignments and selections, the key is aligning data and constraints with how the operation runs.
Use solution status and optimality gap to communicate decision confidence, and use scenarios to test uncertainty. That approach turns a demo into a decision process you can trust and improve.
Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.
Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.