Or-tools是谷歌人工智能系列的运筹优化包，非常良心的开源工具包了。OR-Tools是一个用于优化的开源软件套件，专为解决世界上最棘手的车辆路线问题、流程优化、整数和线性规划以及约束规划等问题。

业内使用举例

算法在哈罗顺风车中的应用

原文链接：

https://mp.weixin.qq.com/s/VZjEAzGkrhnEtAK2INNSgg

仿真软件Anylogic培训课程 原文链接：

https://mp.weixin.qq.com/s/wueSqQEwEqg9Cnw9zRSPoQ

基础使用介绍

1.线性规划

约束条件：

目标函数：max(3x+4y)

#约束规划
from ortools.linear_solver import pywraplp
def LinearProgrammingExample():
    """Linear programming sample."""
    # Instantiate a Glop solver, naming it LinearExample.
    solver = pywraplp.Solver.CreateSolver('GLOP')
    # Create the two variables and let them take on any non-negative value.
    x = solver.NumVar(0, solver.infinity(), 'x')
    y = solver.NumVar(0, solver.infinity(), 'y')
    print('Number of variables =', solver.NumVariables())
    # Constraint 0: x + 2y <= 14.
    solver.Add(x + 2 * y <= 14.0)
    # Constraint 1: 3x - y >= 0.
    solver.Add(3 * x - y >= 0.0)
    # Constraint 2: x - y <= 2.
    solver.Add(x - y <= 2.0)
    print('Number of constraints =', solver.NumConstraints())
    # Objective function: 3x + 4y.
    solver.Maximize(3 * x + 4 * y)
    # Solve the system.
    status = solver.Solve()
    if status == pywraplp.Solver.OPTIMAL:
        print('Solution:')
        print('Objective value =', solver.Objective().Value())
        print('x =', x.solution_value())
        print('y =', y.solution_value())
    else:
        print('The problem does not have an optimal solution.')
    print('\nAdvanced usage:')
    print('Problem solved in %f milliseconds' % solver.wall_time())
    print('Problem solved in %d iterations' % solver.iterations())
LinearProgrammingExample()

2.背包问题

有50件商品， 价格如下：360, 83, 59, 130, 431, 67, 230, 52, 93, 125, 670, 892, 600, 38, 48, 147,78, 256, 63, 17, 120, 164, 432, 35, 92, 110, 22, 42, 50, 323, 514, 28,87, 73, 78, 15, 26, 78, 210, 36, 85, 189, 274, 43, 33, 10, 19, 389, 276,312

体积如下：7, 0, 30, 22, 80, 94, 11, 81, 70, 64, 59, 18, 0, 36, 3, 8, 15, 42, 9, 0,42, 47, 52, 32, 26, 48, 55, 6, 29, 84, 2, 4, 18, 56, 7, 29, 93, 44, 71,3, 86, 66, 31, 65, 0, 79, 20, 65, 52, 13

背包最大装载体积为850

from ortools.algorithms import pywrapknapsack_solver
def main():
    # Create the solver.
    solver = pywrapknapsack_solver.KnapsackSolver(
        pywrapknapsack_solver.KnapsackSolver.
        KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER, 'KnapsackExample')
    values = [
        360, 83, 59, 130, 431, 67, 230, 52, 93, 125, 670, 892, 600, 38, 48, 147,
        78, 256, 63, 17, 120, 164, 432, 35, 92, 110, 22, 42, 50, 323, 514, 28,
        87, 73, 78, 15, 26, 78, 210, 36, 85, 189, 274, 43, 33, 10, 19, 389, 276,
        312
    ]
    weights = [[
        7, 0, 30, 22, 80, 94, 11, 81, 70, 64, 59, 18, 0, 36, 3, 8, 15, 42, 9, 0,
        42, 47, 52, 32, 26, 48, 55, 6, 29, 84, 2, 4, 18, 56, 7, 29, 93, 44, 71,
        3, 86, 66, 31, 65, 0, 79, 20, 65, 52, 13
    ]]
    capacities = [850]
    solver.Init(values, weights, capacities)
    computed_value = solver.Solve()
    packed_items = []
    packed_weights = []
    total_weight = 0
    print('Total value =', computed_value)
    for i in range(len(values)):
        if solver.BestSolutionContains(i):
            packed_items.append(i)
            packed_weights.append(weights[0][i])
            total_weight += weights[0][i]
    print('Total weight:', total_weight)
    print('Packed items:', packed_items)
    print('Packed_weights:', packed_weights)
if __name__ == '__main__':
    main()