solve_integer_program_tool
Solve integer and mixed-integer programming problems for discrete optimization decisions such as facility location, project selection, crew scheduling, network design, cutting stock, and capital budgeting.
Instructions
Solve an integer or mixed-integer programming problem using PuLP.
This tool solves optimization problems where some or all variables must
take integer values, which is useful for discrete decision problems.
Use cases:
- Facility location: Decide where to build warehouses or service centers
- Project selection: Choose which projects to fund (binary decisions)
- Crew scheduling: Assign integer numbers of staff to shifts
- Network design: Design networks with discrete components
- Cutting stock: Minimize waste when cutting materials
- Capital budgeting: Select investments when partial investments aren't allowed
Args:
objective: Objective function with 'sense' and 'coefficients'
variables: Variable definitions with types "continuous", "integer", or "binary"
constraints: List of linear constraints
solver: Solver to use ("CBC", "GLPK", "GUROBI", "CPLEX")
time_limit_seconds: Maximum time to spend solving (optional)
Returns:
Optimization result with integer/binary variable values
Example:
# Binary knapsack: select items to maximize value within weight limit
solve_integer_program(
objective={"sense": "maximize", "coefficients": {"item1": 10, "item2": 15}},
variables={
"item1": {"type": "binary"},
"item2": {"type": "binary"}
},
constraints=[
{"expression": {"item1": 5, "item2": 8}, "operator": "<=", "rhs": 10}
]
)
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| objective | Yes | ||
| variables | Yes | ||
| constraints | Yes | ||
| solver | No | CBC | |
| time_limit_seconds | No |