使用指南：https://developers.google.cn/optimization/introduction/python?hl=zh-cn
實(shí)例鏈接：https://github.com/google/or-tools/tree/stable/ortools/linear_solver/samples
求解器
ortools中的幾種常用求解器：
- SCIP：處理混合整數(shù)規(guī)劃（線性約束或非線性約束），處理復(fù)雜問題，可能比較耗時(shí)
- GLOP：大規(guī)模不涉及整數(shù)變量的線性優(yōu)化問題
- CP-SAT： 能夠找到所有滿足約束條件的解，而不僅僅是找到一個(gè)最優(yōu)解（比如排班安排，有多種可行解）
TSP問題
官網(wǎng)示例代碼：https://github.com/google/or-tools/blob/stable/ortools/constraint_solver/samples/tsp.py
from ortools.constraint_solver import pywrapcp
營(yíng)養(yǎng)問題
裝箱問題
官網(wǎng)示例代碼：https://github.com/google/or-tools/blob/stable/ortools/linear_solver/samples/bin_packing_mip.py
推薦寫法：solver.Add(sum(x[i, j] for j in data["bins"]) == 1)
作業(yè)問題
官網(wǎng)示例代碼：https://github.com/google/or-tools/blob/stable/examples/contrib/assignment.py
boolvar的寫法
work = {}
for worker in range(num_workers):
work[worker] = solver.BoolVar(f"work[{worker}]")
for worker in range(num_workers):
solver.Add(
work[worker] == solver.Sum([x[worker, task] for task in range(num_tasks)])
)
Ab都為真才真的寫法：
constraint_g1 = solver.Constraint(1, 1)
for i in range(len(group1)):
# ab can be transformed into 0 <= a + b - 2p <= 1 with p in [0,1]
# p is True if a AND b, False otherwise
constraint = solver.Constraint(0, 1)
constraint.SetCoefficient(work[group1[i][0]], 1)
constraint.SetCoefficient(work[group1[i][1]], 1)
p = solver.BoolVar(f"g1_p{i}")
constraint.SetCoefficient(p, -2)
constraint_g1.SetCoefficient(p, 1)
日程問題
官方示例代碼：https://github.com/google/or-tools/blob/main/examples/python/shift_scheduling_sat.py
