Luna Use Cases

A collection of ready-to-use optimization problems built on a standardized interface. Each use case provides data models, mathematical formulations, and solution interpretation — so you can focus on solving, benchmarking, and extending.

How It Works

Every use case follows the same workflow:

graph LR
    A[Data] --> C[Instance]
    B[Formulation] --> C
    C --> D[Model]
    D --> E[Solver]
    E --> F[Solution]
    F --> G[Result]

You define problem data, pick a formulation, and combine them into an instance. The instance produces a solver-ready model, and after solving, the result is interpreted back into a structured solution.

from luna_usecases.traveling_salesman_problem import TspData, TspFormulation, TspInstance

data = TspData.from_distance_matrix(distances, city_names=cities)
instance = TspInstance(data=data, formulation=TspFormulation())

model = instance.formulate()
# solution = solver.solve(model)
# result = instance.interpret(solution)

Available Use Cases

Where to Find What