【建模算法】Python调用Gurobi求解TSP问题
TSP (traveling salesman problem，旅行商问题)是典型的NP完全问题，即其最坏情况下的时间复杂度随着问题规模的增大按指数方式增长，到目前为止还未找到一个多项式时间的有效算法。本文探讨了Python调用Gurobi优化器求解TSP问题。
一、问题描述
本案例以31个城市为例，假定31个城市的位置坐标如表1所列。寻找出一条最短的遍历31个城市的路径。
| 城市编号 | X坐标 | Y坐标 | 城市编号 | X坐标 | Y坐标 |
|---|---|---|---|---|---|
| 1 | 1.304 | 2.312 | 17 | 3.918 | 2.179 |
| 2 | 3.639 | 1.315 | 18 | 4.061 | 2.37 |
| 3 | 4.177 | 2.244 | 19 | 3.78 | 2.212 |
| 4 | 3.712 | 1.399 | 20 | 3.676 | 2.578 |
| 5 | 3.488 | 1.535 | 21 | 4.029 | 2.838 |
| 6 | 3.326 | 1.556 | 22 | 4.263 | 2.931 |
| 7 | 3.238 | 1.229 | 23 | 3.429 | 1.908 |
| 8 | 4.196 | 1.044 | 24 | 3.507 | 2.376 |
| 9 | 4.312 | 0.79 | 25 | 3.394 | 2.643 |
| 10 | 4.386 | 0.57 | 26 | 3.439 | 3.201 |
| 11 | 3.007 | 1.97 | 27 | 2.935 | 3.24 |
| 12 | 2.562 | 1.756 | 28 | 3.14 | 3.55 |
| 13 | 2.788 | 1.491 | 29 | 2.545 | 2.357 |
| 14 | 2.381 | 1.676 | 30 | 2.778 | 2.826 |
| 15 | 1.332 | 0.695 | 31 | 2.37 | 2.975 |
| 16 | 3.715 | 1.678 |
二、Gurobi 求解器简介
Gurobi 是全球综合能力排名靠前的数学规划求解器。目前最新版本 9.5.1，可以求解的问题类型包括：
（1）线性约束和目标模型（连续变量、混合整数）
（2）二阶锥模型 （连续变量、混合整数）
（3）二次凸约束和目标模型（连续变量、混合整数）
（4）二次非凸（双线性、二次等式约束等）约束和目标模型（连续变量、混合整数）
（5）非线性模型（除式、高阶多项式、指数、对数、三角函数、范数等）（连续变量、混合整数）
对于以上模型，可以叠加的功能包括但不限于：
（1）约束和目标中带有 最大、最小、绝对值等数学函数，或者带有 AND、OR、INDICATOR 逻辑条件的模型 （连续变量、混合整数）
（2）多目标优化
（3）需要获得部分或者全部可行解或者最优解的模型
（4）不可行或者无界分析
（5）优化参数自动调优功能
（6）分布式计算或者多线程计算
更多Gurobi 功能参见 http://www.gurobi.cn/
三、TSP问题的数学模型
假设为第i个城市与第j个城市之间的距离，num为城市数，
为第i个城市与第j个城市之间的决策变量，此为0-1变量，0代表第i个城市与第j个城市之间不相连、1代表第i个城市与第j个城市之间相连。
要建立的目标为经过所有城市距离最短，约束条件为每个城市只经过一次。可以建立如下模型：
s.t.
j=1,2,…,num
i=1,2,…,num
为0或1，
为实数。约束条件前两个就是保证每个城市只去一次，最后一个约束条件即为防止子回路的出现。
四、python调用Gurobi优化器求解TSP问题的步骤
安装gurobi可以自行网上找教程，此处不再赘述。
- 导入包
from gurobipy import *
import numpy as np
- 创建模型
model=Model("question1")
- 定义变量,计算矩阵
vtype=GRB.BINARY 二进制变量，0-1变量
vtype=GRB.CONTINUOUS 连续变量
vtype=GRB.INTEGER 整数变量
lb 是变量下限，ub是变量上限
在这个问题中，为31__31的矩阵0-1变量，
为311的实数变量。
sdpvar实型变量，intvar 整型变量 binvar 0-1型变量
对于此问题的变量可以定义为：
#读取31座城市坐标
coord = []
with open("data.txt", "r") as lines:
lines = lines.readlines()
for line in lines:
xy = line.split()
coord.append(xy)
coord = np.array(coord)
w, h = coord.shape
coordinates = np.zeros((w, h), float)
for i in range(w):
for j in range(h):
coordinates[i, j] = float(coord[i, j])
# x点坐标
data_x=coordinates[:,0]
# y点坐标
data_y=coordinates[:,1]
data_num=len(data_x)
distance_juzheng=np.zeros((data_num,data_num))
for i in range(0,data_num):
for j in range(0,data_num):
if (i==j):
distance_juzheng[i,j]=100000
else:
distance_juzheng[i,j]=np.sqrt(np.square((data_x[i]-data_x[j]))+np.square((data_y[i]-data_y[j])))
#定义变量
x=model.addMVar((data_num,data_num),lb=0,ub=1,vtype=GRB.BINARY) #lb变量下限， ub变量上限
u=model.addMVar((data_num),vtype=GRB.CONTINUOUS) #lb变量下限， ub变量上限
-
目标函数
#构造目标函数
objsum=[]
for i in range(0,data_num):
for j in range(0,data_num):
objsum=objsum+x[i,j]*distance_juzheng[i,j]
model.setObjective(objsum,GRB.MINIMIZE)
- 约束条件
#构造约束条件
for i in range(0,data_num):
constrsum=sum(x[i,:])
model.addConstr(constrsum==1)
for j in range(0,data_num):
constrsum1=sum(x[:,j])
model.addConstr(constrsum1==1)
for i in range(1,data_num):
for j in range(1,data_num):
if(i!=j):
model.addConstr((u[i]-u[j]+data_num*x[i,j])<=data_num-1)
- 优化求解
model.optimize()
五、求解结果
最优路径：
1-> 15-> 14-> 12-> 13-> 7-> 10-> 9-> 8-> 2-> 4-> 16-> 5-> 6-> 11-> 23-> 19-> 17-> 3-> 18-> 22-> 21-> 20-> 24-> 25-> 26-> 28-> 27-> 30-> 31-> 29-> 1
轨迹图：
完整代码
#求解31座城市的TSP问题完整代码
from gurobipy import *
import numpy as np
model=Model("question1")
#读取31座城市坐标
coord = []
with open("data.txt", "r") as lines:
lines = lines.readlines()
for line in lines:
xy = line.split()
coord.append(xy)
coord = np.array(coord)
w, h = coord.shape
coordinates = np.zeros((w, h), float)
for i in range(w):
for j in range(h):
coordinates[i, j] = float(coord[i, j])
# x点坐标
data_x=coordinates[:,0]
# y点坐标
data_y=coordinates[:,1]
data_num=len(data_x)
distance_juzheng=np.zeros((data_num,data_num))
for i in range(0,data_num):
for j in range(0,data_num):
if (i==j):
distance_juzheng[i,j]=100000
else:
distance_juzheng[i,j]=np.sqrt(np.square((data_x[i]-data_x[j]))+np.square((data_y[i]-data_y[j])))
#定义变量
x=model.addMVar((data_num,data_num),lb=0,ub=1,vtype=GRB.BINARY) #lb变量下限， ub变量上限
u=model.addMVar((data_num),vtype=GRB.CONTINUOUS) #lb变量下限， ub变量上限
#构造目标函数
objsum=[]
for i in range(0,data_num):
for j in range(0,data_num):
objsum=objsum+x[i,j]*distance_juzheng[i,j]
model.setObjective(objsum,GRB.MINIMIZE)
#构造约束条件
for i in range(0,data_num):
constrsum=sum(x[i,:])
model.addConstr(constrsum==1)
for j in range(0,data_num):
constrsum1=sum(x[:,j])
model.addConstr(constrsum1==1)
for i in range(1,data_num):
for j in range(1,data_num):
if(i!=j):
model.addConstr((u[i]-u[j]+data_num*x[i,j])<=data_num-1)
model.optimize()
best=(x.X).astype(int)
print(f"最优目标值为：{model.ObjVal}")
print(f"最优分配为：{best}")
运行结果：
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 932 rows, 992 columns and 4532 nonzeros
Model fingerprint: 0xbe4e71f7
Variable types: 31 continuous, 961 integer (961 binary)
Coefficient statistics:
Matrix range [1e+00, 3e+01]
Objective range [1e-01, 1e+05]
Bounds range [1e+00, 1e+00]
RHS range [1e+00, 3e+01]
Presolve removed 0 rows and 1 columns
Presolve time: 0.02s
Presolved: 932 rows, 991 columns, 4532 nonzeros
Variable types: 30 continuous, 961 integer (961 binary)
Found heuristic solution: objective 2900002.5107
Root relaxation: objective 1.242587e+01, 107 iterations, 0.00 seconds (0.00 work units)
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
0 0 12.42587 0 45 2900002.51 12.42587 100% - 0s
H 0 0 2900002.4836 12.42587 100% - 0s
H 0 0 2800004.9958 12.42587 100% - 0s
0 0 13.38934 0 65 2800005.00 13.38934 100% - 0s
0 0 13.52139 0 61 2800005.00 13.52139 100% - 0s
0 0 13.52274 0 69 2800005.00 13.52274 100% - 0s
0 0 13.52282 0 68 2800005.00 13.52282 100% - 0s
0 0 14.53402 0 59 2800005.00 14.53402 100% - 0s
H 0 0 1800012.4470 14.53402 100% - 0s
0 0 14.64368 0 60 1800012.45 14.64368 100% - 0s
0 0 14.64368 0 60 1800012.45 14.64368 100% - 0s
0 0 14.64882 0 62 1800012.45 14.64882 100% - 0s
0 0 14.65151 0 61 1800012.45 14.65151 100% - 0s
H 0 0 1600012.0222 14.65151 100% - 0s
0 0 14.65956 0 59 1600012.02 14.65956 100% - 0s
H 0 0 1100028.0719 14.65956 100% - 0s
0 0 14.65956 0 59 1100028.07 14.65956 100% - 0s
0 0 14.65956 0 59 1100028.07 14.65956 100% - 0s
0 0 14.65956 0 51 1100028.07 14.65956 100% - 0s
H 0 0 16.2382171 14.65956 9.72% - 0s
0 0 14.66899 0 45 16.23822 14.66899 9.66% - 0s
0 0 14.66899 0 54 16.23822 14.66899 9.66% - 0s
0 0 14.66899 0 61 16.23822 14.66899 9.66% - 0s
0 0 14.66899 0 61 16.23822 14.66899 9.66% - 0s
0 0 14.66899 0 59 16.23822 14.66899 9.66% - 0s
0 0 14.66899 0 59 16.23822 14.66899 9.66% - 0s
0 0 14.66899 0 59 16.23822 14.66899 9.66% - 0s
H 0 0 15.7186599 14.66899 6.68% - 0s
0 0 14.66899 0 51 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 51 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 51 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 51 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 51 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 51 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 45 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 61 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 60 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 60 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 58 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 58 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 58 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 58 15.71866 14.66899 6.68% - 0s
0 0 14.66899 0 58 15.71866 14.66899 6.68% - 0s
0 2 14.66899 0 58 15.71866 14.66899 6.68% - 0s
H 1628 1026 15.7186599 14.73997 6.23% 8.5 2s
*12769 8050 57 15.6996836 14.84307 5.46% 8.4 4s
13210 8619 14.91088 24 32 15.69968 14.84449 5.45% 8.3 5s
*24056 15401 39 15.6721259 14.87890 5.06% 8.1 6s
*24057 15251 38 15.6493581 14.87890 4.92% 8.1 6s
H29434 17104 15.4330841 14.89825 3.47% 8.1 7s
*33812 17603 83 15.4105727 14.89825 3.32% 8.6 8s
44242 20220 15.18573 46 72 15.41057 14.93102 3.11% 8.7 10s
H46430 19702 15.3874549 14.93102 2.97% 9.0 11s
58661 22557 15.07210 56 56 15.38745 14.95224 2.83% 9.0 15s
H58816 21525 15.3825444 14.95224 2.80% 9.0 18s
67759 24235 15.04976 95 41 15.38254 14.95224 2.80% 9.1 20s
117683 32944 cutoff 105 15.38254 14.99875 2.49% 9.1 25s
157428 47324 15.37361 77 28 15.38254 15.03425 2.26% 9.1 30s
201865 61834 cutoff 67 15.38254 15.06842 2.04% 9.2 35s
243220 72133 15.26096 66 29 15.38254 15.09487 1.87% 9.3 40s
279757 78847 15.30388 69 12 15.38254 15.11361 1.75% 9.4 45s
309860 83532 cutoff 85 15.38254 15.12884 1.65% 9.5 50s
338190 86252 15.37650 76 6 15.38254 15.14324 1.56% 9.5 55s
368798 88155 cutoff 95 15.38254 15.15807 1.46% 9.6 60s
398096 88732 15.25925 71 21 15.38254 15.17184 1.37% 9.6 65s
426981 87036 infeasible 78 15.38254 15.18432 1.29% 9.6 70s
456017 84800 cutoff 73 15.38254 15.19700 1.21% 9.7 75s
483613 82030 infeasible 80 15.38254 15.20841 1.13% 9.7 80s
509368 77406 15.28221 72 10 15.38254 15.22011 1.06% 9.8 85s
537451 71269 cutoff 79 15.38254 15.23224 0.98% 9.8 90s
568470 62502 cutoff 96 15.38254 15.24598 0.89% 9.8 95s
593492 55036 15.35594 77 17 15.38254 15.25763 0.81% 9.8 100s
625294 43717 15.32996 88 6 15.38254 15.27493 0.70% 9.8 105s
656274 30390 15.30927 76 6 15.38254 15.29557 0.57% 9.8 110s
691398 9606 15.35426 81 18 15.38254 15.33712 0.30% 9.7 115s
Cutting planes:
Learned: 33
Gomory: 51
Cover: 197
MIR: 35
Flow cover: 241
Inf proof: 142
Zero half: 24
Explored 703817 nodes (6750483 simplex iterations) in 116.41 seconds (158.76 work units)
Thread count was 8 (of 8 available processors)
Solution count 10: 15.3825 15.3875 15.4106 ... 1.10003e+06
Optimal solution found (tolerance 1.00e-04)
Best objective 1.538254443339e+01, best bound 1.538254443339e+01, gap 0.0000%
最优目标值为：15.382544433386597
最优分配为：[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0]
[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]]
输出最优路径：
#输出路径：
ans=np.where(best==1)
ans_x=ans[0]
ans_y=ans[1]
x=ans_x[0]
print("最优路径：")
print(x+1,end='')
for i in range(31):
print('->',ans_y[x]+1,end='')
x=ans_y[x]
结果：
最优路径：
1-> 15-> 14-> 12-> 13-> 7-> 10-> 9-> 8-> 2-> 4-> 16-> 5-> 6-> 11-> 23-> 19-> 17-> 3-> 18-> 22-> 21-> 20-> 24-> 25-> 26-> 28-> 27-> 30-> 31-> 29-> 1
与LKH算法得到的结果完全一致，参见：https://blog.csdn.net/baidu/article/details/124723962
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