哪个分类器适合sklearn的调度问题？

Question

使用sklearn，我正在尝试对皮卡上下车路线问题进行建模。如果您可以推荐一种分类器，将不胜感激。为简单起见，这里只有一辆车，有5位客户。训练数据具有20个功能和10个输出。

功能包括5位客户的x-y线。每个客户都有接送地点。

c1p_x, c1p_y,c2p_x, c2p_y,c3p_x, c3p_y,c4p_x, c4p_y,c5p_x, c5p_y,
c1d_x, c1d_y,c2d_x, c2d_y,c3d_x, c3d_y,c4d_x, c4d_y,c5d_x, c5d_y,

c1 p _x，c1 p _y：客户1 提货 x-y线。

c1 d _x：c1 d _y：客户1 下车 x-y线。

例如，

123,106,332,418,106,477,178,363,381,349,54,214,297,34,5,122,3,441,455,322

输出包括最佳访问顺序。例如5,10,2,7,1,6,4,9,3,8

客户5（pkup）=> 10（drop）=> 2（pkup）=> 7（drop）... => 8（drop）

请注意，每次接送后都会立即下车。

这是我尝试过的代码。

import numpy as np
import pandas as pd
from sklearn.neural_network import MLPClassifier

train = pd.read_csv('ML_DARP_train.txt',header=None,sep=',')
print (train.head())

x = train[range(0,19)]
y = train[range(20,30)]

classifier = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(15,), random_state=1)
MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto',
              beta_1=0.9, beta_2=0.999, early_stopping=False,
              epsilon=1e-08, hidden_layer_sizes=(15,),
              learning_rate='constant', learning_rate_init=0.001,
              max_iter=200, momentum=0.9, n_iter_no_change=10,
              nesterovs_momentum=True, power_t=0.5,  random_state=1,
              shuffle=True, solver='lbfgs', tol=0.0001,
              validation_fraction=0.1, verbose=False, warm_start=False)
classifier.fit(x, y)
print(classifier.score(x, y))
test = pd.read_csv('ML_DARP_test.txt',header=None,sep=',')
test = test[range(0,19)]
print (classifier.predict(test))


import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.multioutput import MultiOutputClassifier
import pandas as pd

train = pd.read_csv('ML_DARP_train.txt',header=None,sep=',')
print (train.head())

x = train[range(0,19)]
y = train[range(20,30)]
print (y)
forest = RandomForestClassifier(n_estimators=100, random_state=0)
classifier = MultiOutputClassifier(forest, n_jobs=-1)
classifier.fit(x, y)
print(classifier.score(x, y))

test = pd.read_csv('ML_DARP_test.txt',header=None,sep=',')
test = test[range(0,19)]

print (classifier.predict(test))

这里是训练数据。

123,106,332,418,106,477,178,363,381,349,54,214,297,34,5,122,3,441,455,322,5,10,2,7,1,6,4,9,3,8
154,129,466,95,135,191,243,13,289,227,300,40,171,286,219,403,232,113,378,428,5,10,2,7,1,6,4,9,3,8
215,182,163,321,259,500,434,304,355,276,77,414,93,83,42,292,101,459,488,237,5,10,4,9,3,8,2,7,1,6
277,220,313,29,304,229,500,454,263,154,339,255,484,351,287,87,330,147,411,343,1,6,3,8,2,7,4,9,5,10
308,258,464,223,349,460,64,120,188,62,100,96,374,118,16,368,73,352,365,480,2,7,1,6,5,10,3,8,4,9
369,296,97,385,363,174,161,317,128,472,346,423,217,338,246,163,349,87,335,132,2,7,4,9,1,6,5,10,3,8
400,318,263,94,471,467,321,45,146,475,107,264,139,136,53,36,155,370,382,380,3,8,2,7,4,9,5,10,1,6
477,387,461,350,62,244,417,242,102,399,401,137,76,451,330,364,431,90,368,47,3,8,1,6,4,9,2,7,5,10
38,441,95,12,45,412,452,361,496,276,162,479,420,155,12,112,128,263,290,138,4,9,1,6,3,8,2,7,5,10
69,447,245,205,106,157,79,89,467,216,393,289,311,422,273,440,435,30,291,323,2,7,4,9,3,8,1,6,5,10
115,0,427,430,214,451,207,302,439,172,185,178,232,220,64,282,210,266,292,22,2,7,5,10,1,6,3,8,4,9
192,53,92,123,259,180,273,468,363,81,447,19,122,488,310,77,454,471,246,159,3,8,1,6,5,10,2,7,4,9
223,91,227,317,304,411,385,180,319,5,208,361,498,239,54,389,245,222,231,328,5,10,2,7,4,9,1,6,3,8
269,113,424,57,396,188,12,378,322,493,470,218,435,52,331,231,20,474,263,59,4,9,5,10,1,6,2,7,3,8
315,151,42,204,410,387,78,43,215,355,215,28,278,273,44,11,264,178,170,149,5,10,2,7,3,8,4,9,1,6
393,236,239,444,487,148,191,240,202,326,23,417,200,71,321,338,39,414,203,365,4,9,2,7,3,8,1,6,5,10
454,274,390,153,62,410,303,453,173,266,286,259,106,354,96,165,331,165,203,48,4,9,2,7,3,8,5,10,1,6
15,327,86,378,154,187,447,181,160,237,62,131,27,152,389,23,137,448,220,264,3,8,4,9,2,7,1,6,5,10
61,365,237,71,184,417,43,379,131,178,324,474,403,388,133,334,413,167,205,417,5,10,3,8,1,6,4,9,2,7
123,418,403,280,261,178,124,59,56,86,101,331,309,170,394,145,172,404,175,70,3,8,2,7,5,10,4,9,1,6
169,441,36,458,275,378,190,194,466,465,332,141,167,406,108,426,385,76,97,176,3,8,2,7,1,6,5,10,4,9
215,494,249,213,398,186,365,470,500,483,124,29,120,236,431,315,238,391,161,439,3,8,5,10,4,9,1,6,2,7
246,0,337,345,396,370,399,88,377,329,355,324,449,441,113,48,420,32,68,28,2,7,3,8,1,6,5,10,4,9
339,100,49,84,489,131,496,286,317,253,163,213,370,238,390,375,195,268,37,181,5,10,2,7,3,8,4,9,1,6
385,122,215,294,80,424,139,29,320,240,425,70,292,36,181,233,17,50,70,413,2,7,4,9,1,6,5,10,3,8
416,144,366,2,125,154,236,211,291,180,170,396,182,304,427,44,277,286,86,112,5,10,3,8,2,7,4,9,1,6
477,198,15,180,170,384,348,424,231,105,448,253,41,39,171,356,68,22,56,265,1,6,4,9,2,7,5,10,3,8
38,251,181,390,231,130,414,58,171,13,209,95,448,322,417,151,281,211,10,387,2,7,5,10,1,6,3,8,4,9
69,258,347,98,276,360,495,239,111,439,455,437,354,89,161,447,40,447,480,55,3,8,1,6,4,9,5,10,2,7
131,327,28,323,384,153,169,500,130,426,248,309,275,388,469,321,379,229,27,286,1,6,5,10,2,7,4,9,3,8
161,334,116,454,351,305,172,102,492,272,463,88,87,76,120,37,60,387,419,361,1,6,5,10,3,8,4,9,2,7
238,403,313,194,428,82,300,331,479,227,255,462,9,375,412,396,367,154,420,45,2,7,1,6,4,9,5,10,3,8
285,456,10,419,35,375,460,74,498,230,47,350,447,189,219,254,189,437,484,308,2,7,5,10,4,9,1,6,3,8
346,494,144,112,95,120,71,287,469,170,294,176,322,441,480,81,480,188,469,476,3,8,2,7,4,9,5,10,1,6
393,47,310,322,156,367,168,453,378,63,71,33,227,223,240,393,208,377,423,113,5,10,1,6,3,8,4,9,2,7
470,100,22,77,264,159,312,197,380,34,364,406,180,52,47,266,30,159,440,329,4,9,2,7,5,10,1,6,3,8
15,138,157,239,294,358,362,332,289,429,109,248,23,273,246,14,243,348,393,466,5,10,4,9,3,8,2,7,1,6
61,176,323,449,355,119,490,59,261,384,371,89,430,39,22,357,49,99,394,134,2,7,4,9,1,6,3,8,5,10
92,198,427,110,353,303,39,210,201,293,117,400,274,260,220,121,278,304,348,272,1,6,4,9,5,10,3,8,2,7
185,267,154,367,476,111,183,439,172,249,410,288,226,89,27,496,84,70,365,488,5,10,4,9,1,6,3,8,2,7
231,321,305,59,36,357,264,119,112,157,187,145,117,357,288,291,345,291,319,124,4,9,1,6,5,10,2,7,3,8
262,327,440,238,66,71,345,270,36,66,417,456,477,92,17,86,72,480,273,262,1,6,2,7,4,9,3,8,5,10
323,381,89,431,96,287,411,436,477,476,179,298,367,359,231,367,317,200,242,415,1,6,3,8,2,7,5,10,4,9
369,418,286,171,219,94,85,195,10,494,456,170,304,173,54,256,154,499,321,192,1,6,2,7,3,8,4,9,5,10
416,456,437,365,249,325,166,377,452,403,217,12,179,425,299,51,415,218,275,330,3,8,2,7,4,9,5,10,1,6
477,9,86,42,294,55,248,58,360,296,479,354,53,176,28,347,158,423,214,452,3,8,2,7,4,9,1,6,5,10
7,31,237,251,355,301,360,255,332,236,240,179,460,459,289,158,434,158,214,151,2,7,3,8,4,9,5,10,1,6
84,84,418,476,447,78,473,468,319,207,17,52,381,241,65,0,225,426,231,335,5,10,4,9,2,7,3,8,1,6
146,154,83,169,477,277,22,102,180,37,295,410,256,493,279,265,438,83,107,395,4,9,5,10,2,7,1,6,3,8
177,176,234,363,21,7,134,299,183,24,40,236,131,244,24,76,213,334,155,125,4,9,2,7,1,6,3,8,5,10
238,214,416,87,144,332,309,74,201,27,318,108,68,58,347,466,66,148,202,372,1,6,2,7,4,9,5,10,3,8
316,298,128,344,236,108,438,287,173,468,126,498,5,372,154,340,357,400,188,40,4,9,1,6,5,10,3,8,2,7
346,305,215,475,219,276,441,391,50,330,341,292,334,76,337,57,38,41,126,162,3,8,1,6,4,9,5,10,2,7
408,358,397,199,296,37,68,103,37,285,118,133,239,359,97,384,330,309,127,346,3,8,5,10,1,6,4,9,2,7
439,381,47,377,357,284,180,316,494,225,380,491,114,110,358,211,120,44,112,30,1,6,3,8,5,10,2,7,4,9
15,450,228,117,449,60,309,28,465,165,156,348,51,425,150,69,412,311,97,199,3,8,4,9,5,10,2,7,1,6
77,2,363,295,463,260,358,163,358,43,434,205,411,160,348,318,124,469,35,305,5,10,1,6,3,8,2,7,4,9
123,40,59,19,23,21,487,392,345,500,211,62,317,428,124,160,416,236,36,4,5,10,3,8,1,6,2,7,4,9
169,62,194,197,83,267,83,72,317,455,441,389,207,194,385,472,175,472,37,189,2,7,1,6,5,10,4,9,3,8
231,131,376,438,160,28,164,254,241,348,234,261,129,493,145,298,435,176,492,326,3,8,5,10,2,7,4,9,1,6
277,154,25,115,221,274,276,452,213,304,480,87,3,244,406,109,210,428,493,10,3,8,2,7,4,9,5,10,1,6
323,191,176,309,266,489,358,132,153,213,241,429,394,11,135,405,470,147,463,179,5,10,1,6,3,8,2,7,4,9
354,214,311,487,296,203,423,283,46,90,488,255,253,247,349,185,198,336,401,300,3,8,1,6,4,9,5,10,2,7
447,298,7,211,372,481,66,11,64,93,296,143,175,45,141,43,4,103,449,31,3,8,4,9,2,7,5,10,1,6
493,336,157,420,449,242,178,224,35,33,41,470,65,312,417,370,296,370,434,200,5,10,3,8,1,6,2,7,4,9
7,343,323,129,40,19,291,421,477,458,287,311,488,110,193,197,71,90,404,369,2,7,5,10,4,9,3,8,1,6
69,396,474,307,54,218,372,102,432,382,64,168,346,346,407,477,331,326,389,21,1,6,3,8,5,10,4,9,2,7
146,465,155,31,115,465,469,284,357,291,342,25,252,113,167,304,90,30,343,158,3,8,1,6,5,10,4,9,2,7
192,2,305,240,191,226,96,11,375,278,103,368,158,396,444,146,397,313,375,390,3,8,1,6,4,9,5,10,2,7
238,24,471,450,268,488,209,209,315,202,365,209,64,178,220,474,156,33,360,58,3,8,2,7,4,9,1,6,5,10
285,78,105,111,282,202,274,359,224,80,126,50,424,415,434,238,385,222,298,179,5,10,3,8,1,6,2,7,4,9
346,116,286,336,358,464,387,71,195,35,388,393,330,197,210,80,176,474,299,364,1,6,3,8,5,10,2,7,4,9
424,185,484,92,466,256,46,316,229,38,196,281,283,26,17,454,499,272,347,110,3,8,4,9,2,7,5,10,1,6
470,223,133,270,11,471,111,482,138,432,442,122,157,278,231,218,242,476,301,248,3,8,2,7,4,9,5,10,1,6
15,261,284,464,56,201,208,163,94,357,203,465,32,29,477,29,1,196,271,401,4,9,1,6,5,10,2,7,3,8
46,267,418,141,70,400,258,313,2,250,434,275,392,265,190,310,230,401,209,6,4,9,3,8,5,10,1,6,2,7
107,336,130,381,193,224,417,72,5,237,242,178,345,79,12,183,68,183,257,253,2,7,4,9,1,6,3,8,5,10
154,358,234,43,176,392,452,176,415,114,473,474,188,284,195,417,250,341,179,359,5,10,3,8,1,6,4,9,2,7
200,412,400,252,252,153,79,405,370,54,250,331,94,66,472,259,56,92,165,27,1,6,4,9,5,10,3,8,2,7
277,465,81,462,298,383,129,38,295,448,26,188,485,334,201,54,269,281,134,196,3,8,4,9,1,6,2,7,5,10
339,18,262,186,405,176,304,314,313,451,304,60,406,131,8,429,122,94,182,428,4,9,1,6,2,7,3,8,5,10
370,56,429,411,498,453,432,26,269,375,65,403,328,430,300,271,398,331,152,80,1,6,5,10,4,9,2,7,3,8
431,94,78,88,11,152,466,161,162,252,327,244,187,166,499,19,110,3,74,186,5,10,3,8,1,6,4,9,2,7
462,116,228,282,71,414,94,390,180,239,72,70,93,449,274,362,417,286,122,433,2,7,4,9,1,6,5,10,3,8
38,185,410,6,164,190,237,102,136,179,366,459,500,231,66,220,208,22,107,101,5,10,4,9,2,7,3,8,1,6
100,238,75,215,225,421,319,284,92,104,142,300,390,499,311,15,468,258,77,238,3,8,5,10,1,6,2,7,4,9
131,245,179,362,192,73,337,387,454,451,358,95,233,203,478,249,149,400,485,329,1,6,3,8,5,10,4,9,2,7
177,298,392,118,331,397,27,178,3,469,150,484,186,32,317,154,18,229,47,91,3,8,2,7,1,6,5,10,4,9
254,352,57,311,392,143,108,344,460,409,428,341,76,299,61,450,262,450,48,275,3,8,4,9,1,6,5,10,2,7
285,390,191,489,406,358,174,9,369,302,173,167,436,35,291,229,6,138,2,413,5,10,2,7,3,8,1,6,4,9
362,443,373,229,482,119,318,253,387,289,451,23,357,334,67,71,329,437,34,143,4,9,1,6,3,8,2,7,5,10
408,481,54,439,105,428,462,466,358,245,227,397,279,131,375,446,119,188,35,328,5,10,3,8,1,6,4,9,2,7
470,34,204,131,134,142,42,147,283,138,489,238,154,383,104,241,364,393,475,450,5,10,1,6,2,7,3,8,4,9
15,71,355,325,164,357,92,282,176,15,250,64,28,134,318,5,76,65,413,71,3,8,5,10,2,7,4,9,1,6
61,94,4,18,225,102,204,495,178,488,12,406,435,417,78,317,368,333,429,286,1,6,5,10,3,8,2,7,4,9
123,163,202,259,348,411,364,254,181,475,289,279,357,215,402,206,205,115,462,487,2,7,4,9,1,6,5,10,3,8
185,201,336,437,362,125,429,405,90,352,50,120,231,452,115,487,434,320,400,107,1,6,5,10,3,8,2,7,4,9
231,238,17,145,407,356,41,117,77,323,312,463,121,218,360,298,225,70,432,323,1,6,4,9,2,7,5,10,3,8
262,276,167,355,484,101,138,298,1,216,73,320,27,0,120,108,485,291,370,461,1,6,4,9,3,8,2,7,5,10
292,283,302,32,44,347,203,449,442,141,304,130,403,252,366,420,213,480,356,129,4,9,3,8,1,6,2,7,5,10

Answer 1

Sklearn是为通用算法而构建的，TSP / VRP对此过于具体。您愿意尝试比Sklearn更具体的库吗？

强化学习的最新进展似乎以一种挑战传统组合优化方法的方式解决了TSP和VRP问题。

首先，您可以查看this tutorial。

recent paper显示了一种VRP方法。他们还在Github上分享了他们的代码。

more recent paper声称培训时间较短。

一般而言，这些论文中提出的体系结构从整体上看待了VRP的工作，并且比贪婪的方法要好：

1. 来回包含未来的培训阶段 奖励
2. 解决方案体系结构包括（至少）两个NN。 编码器和解码器。在解码器开始产生输出之前，编码器会遍历整个输入

总而言之，如果您想要一种快速而强大的解决方案，则可以使用现有的开放库，例如Jsprit。如果您有时间进行研究，有用于训练NN的资源并且可能冒失败的风险，请继续学习强化学习。

Answer 2

根据您的评论，仅使用ML来生成传统MIP /约束/启发式求解器的起点比使用ML来解决整个问题更好，但我认为这仍然不是一个好主意。我认为，使用ML很难获得有用的初始解决方案。在几行代码中，您可能可以组合试探法，以贪婪地增加搜索起点的路线；要让ML执行质量几乎相同的工作将需要做很多工作，甚至可能做不到。

如果您真的想尝试此操作（我再次强调这是个坏主意），那么功能的选择可能比分类器的选择重要得多。例如，目前您要让分类器学习（a）毕达哥拉斯，然后（b）什么是好的途径。它必须学习毕达哥拉斯，因为您直接传递了座标。当这些功能经过精心设计以简化学习任务时，ML效果最好。传递标准化距离矩阵而不是原始坐标可能会更成功，因为这样分类器就不必学习毕达哥拉斯。但是，要素的缩放比例为n ^ 2，这可能会导致过度拟合以及与此相关的问题...

或者，您可以使用ML将路线从空驶入，以决定每次添加的下一站。因此，ML分类器选择第一个停靠点，然后再次分类以选择第二个停靠点，然后再次分类以选择第三个停靠点，依此类推。这也将更加简单，尽管ML只会学习“最接近停靠点的位置”。我知道有些公司在一次安排/分派食物外卖/按需交付时使用这种“ ML选择下一站或工作”方法，即类似于Uber Eats交付热食品的问题从餐馆。这与您的情况有些不同，因为它是动态/实时路线优化问题，但是仍然有一些公司实际上在实际的车辆路线优化中使用ML。在我的选择中，这仍然是一个不好的方法-例如我们在这段视频https://www.youtube.com/watch?v=EMhnXAH5dvM中进行了一项研究，我们研究了这种一次一次的调度/调度（您可以使用ML进行调度）与适当的路由优化以及一次一次时间调度/调度的效果明显变差。