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name: ortools description: Google Optimization Tools. An open-source software suite for optimization, specialized in vehicle routing, flows, integer and linear programming, and constraint programming. Features the world-class CP-SAT solver. Use for vehicle routing problems (VRP), scheduling, bin packing, knapsack problems, linear programming (LP), integer programming (MIP), network flows, constraint programming, combinatorial optimization, resource allocation, shift scheduling, job-shop scheduling, and discrete optimization problems. version: 9.8 license: Apache-2.0

Google OR-Tools - Combinatorial Optimization

OR-Tools provides specialized solvers for hard combinatorial problems. Its crown jewel is the CP-SAT solver, which uses Constraint Programming and Satisfiability techniques to find optimal solutions for scheduling and resource allocation problems that are impossible for standard linear solvers.

When to Use

• Vehicle Routing (VRP): Finding the best paths for a fleet of vehicles to deliver goods.
• Scheduling: Creating shift rosters, project timelines, or job-shop schedules.
• Bin Packing: Fitting objects of different sizes into a finite number of bins.
• Knapsack Problem: Selecting items to maximize value within a weight limit.
• Linear Programming (LP): Standard resource allocation with continuous variables.
• Integer Programming (MIP): Optimization where variables must be whole numbers (e.g., "number of machines to buy").
• Network Flows: Calculating max flow or min cost in a graph.

Reference Documentation

Official docs: https://developers.google.com/optimization
GitHub: https://github.com/google/or-tools
Search patterns: cp_model.CpModel, pywraplp.Solver, routing_enums_pb2, AddConstraint

Core Principles

Modeling vs. Solving

OR-Tools separates the Definition of the problem (Variables, Constraints, Objective) from the Solver engine. You build a model, then pass it to a solver instance.

CP-SAT (Constraint Programming)

The most modern and recommended solver for discrete problems. Critical Note: CP-SAT works with integers only. If you have floating-point numbers (like 0.5), you must scale them (e.g., multiply by 100 and work with integers).

Status Codes

After solving, always check the status. It can be OPTIMAL, FEASIBLE (a solution found, but maybe not the best), INFEASIBLE (impossible to solve), or LIMIT_REACHED.

Quick Reference

Installation

pip install ortools

Standard Imports

from ortools.sat.python import cp_model
from ortools.linear_solver import pywraplp
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp

Basic Pattern - CP-SAT Solver (Integer Logic)

from ortools.sat.python import cp_model

# 1. Create the model
model = cp_model.CpModel()

# 2. Define variables: NewIntVar(lower_bound, upper_bound, name)
x = model.NewIntVar(0, 10, 'x')
y = model.NewIntVar(0, 10, 'y')

# 3. Add constraints
model.Add(x + y <= 8)
model.Add(x > 2)

# 4. Define Objective
model.Maximize(x + 2 * y)

# 5. Solve
solver = cp_model.CpSolver()
status = solver.Solve(model)

if status == cp_model.OPTIMAL:
    print(f'x = {solver.Value(x)}, y = {solver.Value(y)}')

Critical Rules

✅ DO

• Use CP-SAT for Discrete Tasks - It is significantly faster than MIP solvers for scheduling and logic-heavy problems.
• Scale your Floats - Since CP-SAT is an integer solver, convert 1.25 to 125 and adjust the logic.
• Check Status First - Never access variable values if the status is INFEASIBLE.
• Use AddElement for indexing - To use a variable as an index in an array, use the specialized constraint model.AddElement.
• Set a Time Limit - For complex problems, use solver.parameters.max_time_in_seconds = 60.0 to get the best possible solution within a minute.
• Verify with Value() - Access results using solver.Value(var), not the variable object itself.

❌ DON'T

• Don't use Python if in Constraints - You cannot use if x > 5: model.Add(...). Use boolean implications (OnlyEnforceIf).
• Don't use non-linear math - CP-SAT and LP solvers don't support x * y (where both are variables) or sin(x). For x * y, you need specialized linearization or piecewise approximations.
• Avoid huge domains - Defining a variable with a range of 0 to 1,000,000,000 can slow down the solver. Narrow the bounds as much as possible.

Anti-Patterns (NEVER)

from ortools.sat.python import cp_model

# ❌ BAD: Using standard Python logic inside the model
# if solver.Value(x) > 5: # ❌ Value() is not available during modeling!
#     model.Add(y == 1)

# ✅ GOOD: Conditional constraints (Logical Implication)
b = model.NewBoolVar('b')
model.Add(x > 5).OnlyEnforceIf(b)
model.Add(x <= 5).OnlyEnforceIf(b.Not())
model.Add(y == 1).OnlyEnforceIf(b)

# ❌ BAD: Floating point variables in CP-SAT
# x = model.NewIntVar(0, 1.5, 'x') # ❌ Error!

# ✅ GOOD: Scaling
# x_scaled = model.NewIntVar(0, 150, 'x_scaled') # 150 represents 1.50

Linear Programming (pywraplp)

Resource Allocation (Continuous Variables)

from ortools.linear_solver import pywraplp

# Create solver with GLOP backend (Google Linear Optimization Package)
solver = pywraplp.Solver.CreateSolver('GLOP')

# Define continuous variables
x = solver.NumVar(0, solver.infinity(), 'x')
y = solver.NumVar(0, solver.infinity(), 'y')

# Constraint: x + 2y <= 14
ct = solver.Constraint(-solver.infinity(), 14)
ct.SetCoefficient(x, 1)
ct.SetCoefficient(y, 2)

# Objective: Maximize 3x + 4y
objective = solver.Objective()
objective.SetCoefficient(x, 3)
objective.SetCoefficient(y, 4)
objective.SetMaximization()

solver.Solve()
print(f'Solution: x={x.solution_value()}, y={y.solution_value()}')

Vehicle Routing (VRP)

The Logistics Engine

from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp

def solve_vrp():
    # 1. Distance Matrix (distance between locations)
    data = {'distance_matrix': [[0, 10, 20], [10, 0, 15], [20, 15, 0]],
            'num_vehicles': 1, 'depot': 0}
    
    # 2. Setup Index Manager and Routing Model
    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),
                                           data['num_vehicles'], data['depot'])
    routing = pywrapcp.RoutingModel(manager)
    
    # 3. Create Distance Callback
    def distance_callback(from_index, to_index):
        return data['distance_matrix'][manager.IndexToNode(from_index)][manager.IndexToNode(to_index)]
    
    transit_callback_index = routing.RegisterTransitCallback(distance_callback)
    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
    
    # 4. Solve
    search_parameters = pywrapcp.DefaultRoutingSearchParameters()
    search_parameters.first_solution_strategy = (
        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
    
    solution = routing.SolveWithParameters(search_parameters)
    return solution

Constraint Programming: Scheduling

Job-Shop Example (Tasks with dependencies)

model = cp_model.CpModel()

# Define an Interval Variable (Start, Duration, End)
duration = 10
start_var = model.NewIntVar(0, 100, 'start')
end_var = model.NewIntVar(0, 100, 'end')
interval_var = model.NewIntervalVar(start_var, duration, end_var, 'interval')

# No-overlap constraint (Machines can only do one task at a time)
model.AddNoOverlap([interval_var1, interval_var2, interval_var3])

Practical Workflows

1. Employee Shift Scheduling

def solve_shifts(num_employees, num_days, shifts_per_day):
    model = cp_model.CpModel()
    shifts = {}
    for e in range(num_employees):
        for d in range(num_days):
            for s in range(shifts_per_day):
                shifts[(e, d, s)] = model.NewBoolVar(f'shift_e{e}d{d}s{s}')
                
    # Constraint: Each shift is assigned to exactly one employee
    for d in range(num_days):
        for s in range(shifts_per_day):
            model.Add(sum(shifts[(e, d, s)] for e in range(num_employees)) == 1)
            
    # Constraint: Each employee works at most one shift per day
    for e in range(num_employees):
        for d in range(num_days):
            model.Add(sum(shifts[(e, d, s)] for s in range(shifts_per_day)) <= 1)
            
    solver = cp_model.CpSolver()
    status = solver.Solve(model)
    return shifts, solver

2. Multi-Knapsack (Packing items into bins)

def bin_packing(items, bin_capacities):
    model = cp_model.CpModel()
    # x[i, j] = 1 if item i is in bin j
    x = {}
    for i in range(len(items)):
        for j in range(len(bin_capacities)):
            x[i, j] = model.NewBoolVar(f'x_{i}_{j}')
            
    # Each item in exactly one bin
    for i in range(len(items)):
        model.Add(sum(x[i, j] for j in range(len(bin_capacities))) == 1)
        
    # Bin capacity constraint
    for j in range(len(bin_capacities)):
        model.Add(sum(x[i, j] * items[i] for i in range(len(items))) <= bin_capacities[j])

Performance Optimization

Hinting (Warm Start)

If you have a good initial guess, provide it to the solver to speed up search.

model.AddHint(x, 5)
model.AddHint(y, 2)

Parallel Solving

CP-SAT can use multiple threads to explore different parts of the search tree.

solver = cp_model.CpSolver()
solver.parameters.num_search_workers = 8 # Use 8 CPU cores

Common Pitfalls and Solutions

Floating Point Math Errors

As mentioned, OR-Tools CP-SAT is strictly integer.

# ❌ Problem: model.Add(x * 0.1 <= 5)
# ✅ Solution: 
model.Add(x <= 50) # Multiply both sides by 10

Infeasible Models

If solver.Solve(model) returns INFEASIBLE, it means your constraints are contradictory.

# ✅ Solution: Use 'Sufficient Assmptions' or 'Constraint Relaxation'
# to identify which constraint is causing the conflict.

Symmetry

If items A and B are identical, the solver will waste time checking both "A in Bin 1, B in Bin 2" and "B in Bin 1, A in Bin 2".

# ✅ Solution: Add symmetry-breaking constraints
# model.Add(x_A <= x_B) # Force an ordering

Best Practices

1. Always check solver status before accessing variable values
2. Scale floating-point values to integers when using CP-SAT
3. Set time limits for complex problems to get feasible solutions quickly
4. Use appropriate solver - CP-SAT for discrete, GLOP for continuous LP
5. Break symmetry in models with identical variables to speed up solving
6. Narrow variable domains as much as possible for better performance
7. Use hints when you have good initial guesses
8. Enable parallel solving for large problems when available
9. Verify solutions by checking constraints are satisfied
10. Document your model - variable names and constraint logic

Google OR-Tools is the heavy machinery of the optimization world. It solves the discrete puzzles that power global logistics, airline scheduling, and manufacturing, turning impossible "Trial and Error" into mathematical certainty.