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Mizux/vrp_passengers_goods.py

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Passengers and Goods VRP for hihae...
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vrp_passengers_goods.py
#!/usr/bin/env python3
#### dial-a-ride problem with time window and maximum riding time
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp
#import pandas as pd
import math
# Create Data Model
def create_data_model():
"""Stores the data for the problem."""
data = {}
data['time_matrix'] = [
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233, 189, 390, 339, 73, 0, 168, 199, 358, 632, 362, 130, 874, 532,
340, 221, 322, 430, 38, 233, 358, 132, 108, 840, 640, 569, 507,
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[
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541
],
[
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300, 274, 343, 390, 338, 209, 121, 406, 426, 746, 547, 475, 413,
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363, 206, 77, 186, 186, 186, 714, 186, 326, 308, 811, 270
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[
275, 248, 432, 312, 80, 130, 91, 239, 330, 605, 335, 0, 917, 574,
384, 273, 374, 474, 92, 275, 364, 63, 149, 882, 683, 611, 549, 628,
381, 327, 398, 168, 780, 721, 649, 1110, 1001, 600, 357, 302, 306,
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],
[
641, 718, 663, 711, 899, 874, 930, 816, 763, 1050, 780, 917, 0,
374, 732, 707, 775, 822, 887, 641, 680, 960, 962, 68, 280, 353,
391, 1074, 839, 798, 1033, 1034, 136, 196, 997, 220, 111, 398, 622,
617, 752, 827, 959, 754, 618, 618, 618, 67, 618, 804, 741, 31, 703
],
[
299, 376, 321, 369, 557, 532, 588, 474, 421, 708, 438, 574, 373, 0,
390, 365, 433, 480, 545, 299, 338, 618, 620, 339, 139, 37, 49, 732,
497, 456, 691, 692, 237, 177, 655, 567, 457, 41, 280, 274, 409,
485, 617, 412, 276, 276, 276, 306, 276, 462, 399, 404, 360
],
[
108, 175, 248, 231, 364, 340, 397, 281, 293, 570, 300, 384, 732,
390, 0, 122, 173, 93, 352, 108, 216, 426, 428, 698, 498, 427, 365,
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271, 293, 478, 284, 114, 114, 114, 666, 119, 270, 24, 763, 118
],
[
83, 53, 222, 205, 246, 221, 290, 163, 268, 544, 274, 273, 707, 365,
122, 0, 101, 212, 234, 83, 191, 308, 309, 673, 473, 402, 339, 568,
333, 292, 527, 395, 570, 511, 387, 900, 791, 391, 147, 90, 246,
174, 453, 259, 88, 88, 88, 640, 94, 151, 131, 738, 28
],
[
151, 154, 290, 273, 347, 322, 391, 264, 336, 613, 343, 374, 775,
433, 173, 101, 0, 121, 335, 151, 259, 408, 410, 741, 541, 470, 408,
636, 401, 361, 595, 495, 639, 579, 290, 969, 859, 459, 216, 158,
314, 275, 521, 327, 157, 157, 157, 708, 162, 252, 149, 806, 72
],
[
198, 265, 338, 321, 454, 430, 487, 372, 383, 660, 390, 474, 822,
480, 93, 212, 121, 0, 443, 198, 306, 516, 518, 788, 589, 517, 455,
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361, 383, 569, 374, 204, 204, 204, 756, 209, 360, 82, 853, 191
],
[
246, 209, 402, 308, 35, 38, 130, 200, 326, 601, 331, 92, 887, 545,
352, 234, 335, 443, 0, 246, 360, 97, 88, 853, 653, 582, 520, 624,
389, 342, 454, 224, 751, 691, 618, 1081, 971, 571, 328, 272, 302,
85, 488, 317, 274, 274, 274, 820, 269, 92, 361, 918, 263
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[
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108, 83, 151, 198, 246, 0, 125, 319, 321, 607, 407, 336, 274, 502,
267, 227, 461, 393, 505, 445, 373, 835, 725, 325, 82, 27, 180, 186,
387, 193, 29, 29, 29, 575, 23, 163, 117, 672, 79
],
[
125, 203, 108, 52, 369, 358, 379, 300, 91, 391, 121, 366, 680, 338,
216, 191, 259, 306, 362, 125, 0, 429, 446, 646, 446, 375, 313, 414,
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300, 82, 103, 103, 103, 613, 102, 289, 225, 711, 187
],
[
319, 283, 476, 375, 62, 132, 60, 274, 393, 668, 398, 63, 960, 618,
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444, 391, 358, 128, 824, 764, 691, 1154, 1044, 644, 401, 346, 369,
175, 431, 384, 348, 348, 348, 894, 342, 166, 434, 991, 336
],
[
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620, 428, 309, 410, 518, 88, 321, 446, 116, 0, 928, 729, 657, 595,
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390, 135, 548, 405, 350, 350, 350, 896, 344, 168, 436, 993, 338
],
[
607, 684, 629, 677, 865, 840, 896, 782, 729, 1016, 746, 882, 68,
340, 698, 673, 741, 788, 853, 607, 646, 926, 928, 0, 246, 319, 357,
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717, 793, 925, 720, 584, 584, 584, 32, 584, 770, 707, 78, 668
],
[
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140, 498, 473, 541, 589, 653, 407, 446, 727, 729, 246, 0, 119, 157,
840, 605, 565, 799, 800, 143, 100, 763, 473, 364, 164, 388, 383,
518, 594, 725, 520, 385, 385, 385, 213, 384, 571, 507, 311, 469
],
[
336, 413, 358, 406, 594, 569, 625, 511, 458, 745, 475, 611, 352,
37, 427, 402, 470, 517, 582, 336, 375, 655, 657, 318, 119, 0, 75,
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446, 522, 654, 449, 313, 313, 313, 286, 313, 499, 436, 383, 397
],
[
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49, 365, 339, 408, 455, 520, 274, 313, 593, 595, 357, 157, 75, 0,
706, 472, 431, 666, 667, 255, 195, 630, 585, 475, 54, 254, 249,
384, 460, 592, 387, 251, 251, 251, 324, 251, 437, 373, 422, 335
],
[
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478, 336, 656, 449, 360, 480, 480, 480, 1008, 480, 620, 602, 1105,
564
],
[
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497, 358, 333, 401, 449, 396, 267, 180, 444, 484, 805, 605, 534,
472, 302, 0, 69, 220, 450, 703, 643, 623, 1033, 923, 523, 225, 243,
87, 420, 146, 136, 245, 245, 245, 772, 244, 383, 367, 870, 329
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[
227, 304, 228, 87, 349, 374, 340, 360, 86, 328, 58, 327, 798, 456,
318, 292, 361, 408, 342, 227, 139, 391, 430, 764, 565, 493, 431,
351, 69, 0, 234, 444, 662, 603, 583, 992, 882, 482, 184, 202, 47,
366, 161, 96, 204, 204, 204, 732, 204, 329, 326, 829, 288
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[
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691, 552, 527, 595, 642, 460, 461, 373, 364, 480, 999, 799, 728,
666, 522, 220, 234, 0, 230, 896, 837, 817, 1226, 1117, 717, 419,
437, 281, 539, 74, 330, 438, 438, 438, 966, 438, 510, 561, 1064,
523
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[
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426, 430, 309, 304, 445, 428, 428, 428, 974, 422, 280, 516, 1072,
429
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237, 596, 570, 639, 686, 751, 505, 544, 824, 826, 102, 143, 217,
255, 937, 703, 662, 896, 898, 0, 60, 861, 330, 220, 262, 485, 480,
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360
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data['distance_matrix'] = [
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10621, 7869, 6828, 6156, 8084, 4971, 3593, 4961, 263, 9262, 847,
657, 13347, 12121, 6523, 358, 2907, 4086, 475, 5074, 4232, 2928,
2928, 2928, 10189, 2861, 832, 3696, 11226, 2572
],
[
3173, 2754, 4959, 4082, 1243, 1702, 0, 2618, 4205, 7727, 4391, 919,
11532, 7073, 4395, 2944, 3966, 5338, 132, 3175, 4713, 61, 179,
11241, 8489, 7448, 6776, 7963, 3791, 3291, 3388, 1056, 9882, 909,
7375, 13967, 12741, 7143, 42, 3527, 3965, 2089, 4135, 4111, 3548,
3548, 3548, 10809, 3481, 1717, 4501, 11847, 3233
],
[
1924, 1464, 371, 3964, 2151, 2018, 2618, 0, 4087, 7609, 4273, 2427,
10283, 5824, 2961, 1654, 2676, 3904, 2033, 1926, 3532, 2776, 2798,
9991, 724, 6199, 5527, 7844, 4731, 4322, 6698, 3675, 8633, 7841,
594, 12718, 11492, 5894, 2951, 2278, 3847, 1919, 5951, 3993, 2299,
2299, 2299, 956, 2232, 1329, 3067, 10597, 1943
],
[
2544, 3518, 159, 686, 4131, 4406, 4285, 4167, 0, 3745, 715, 4135,
9192, 4733, 3511, 3177, 3868, 4455, 4057, 2543, 924, 4775, 491,
8901, 615, 5108, 4436, 398, 1283, 875, 3251, 5341, 7542, 6751,
6491, 11627, 10401, 4804, 1915, 2214, 479, 433, 2504, 94, 2235,
2235, 2235, 847, 2236, 3958, 3617, 9507, 3135
],
[
6127, 71, 5876, 4269, 7639, 7915, 7793, 7675, 3759, 0, 3351, 7643,
13283, 8825, 7093, 676, 7451, 8037, 7566, 6125, 49, 8284, 8418,
12992, 10241, 9199, 8528, 235, 3326, 396, 5545, 7876, 11633, 10842,
10073, 15718, 14492, 8895, 5497, 5796, 3869, 7839, 4798, 3853,
5817, 5817, 5817, 12561, 5819, 7467, 72, 13598, 6718
],
[
2776, 375, 2526, 918, 4288, 4564, 4443, 4324, 715, 3336, 0, 4293,
9933, 5474, 3743, 3409, 41, 4686, 4215, 2775, 1549, 4933, 5068,
9642, 689, 5849, 5177, 3572, 713, 61, 2858, 5499, 8283, 7491, 6723,
12368, 11142, 5544, 2146, 2446, 518, 4488, 2111, 809, 2467, 2467,
2467, 921, 2468, 4116, 3849, 10248, 3367
],
[
3023, 2514, 4381, 3932, 806, 1316, 919, 2427, 4055, 7577, 4241, 0,
11382, 6923, 4245, 2768, 379, 5188, 934, 3025, 4563, 641, 151,
11091, 8339, 7298, 6626, 7813, 3663, 3163, 4034, 1703, 9732, 894,
7224, 13817, 12591, 6993, 3622, 3377, 3815, 1703, 4782, 3961, 3398,
3398, 3398, 10659, 3331, 1331, 4351, 11697, 3057
],
[
8359, 9332, 8173, 9015, 1112, 10912, 11532, 10283, 9192, 13269,
9933, 11382, 0, 4544, 9325, 8992, 9683, 10269, 11002, 8357, 8347,
11745, 11804, 748, 3264, 4335, 4995, 13504, 10391, 9982, 12358,
12588, 165, 2441, 12305, 2468, 1242, 479, 7756, 8028, 9507, 10436,
11611, 9098, 8049, 8049, 8049, 723, 8051, 10169, 9431, 315, 8949
],
[
39, 4873, 3714, 4556, 6661, 6453, 7073, 5824, 4733, 881, 5474,
6923, 4562, 0, 4866, 4533, 5224, 581, 6543, 3898, 3888, 7286, 7345,
427, 1519, 375, 536, 9045, 5932, 5523, 7899, 8129, 2911, 212, 7846,
6997, 5771, 411, 3297, 3569, 5048, 5977, 7152, 4639, 359, 359, 359,
3839, 3592, 571, 4972, 4876, 449
],
[
1221, 1852, 2753, 2825, 3798, 359, 4395, 2961, 3531, 7079, 3743,
4245, 9325, 4866, 0, 1314, 1845, 942, 368, 122, 2574, 4423, 4483,
9034, 6283, 5241, 4569, 7314, 4201, 3792, 6168, 5451, 7675, 6884,
3135, 1176, 10534, 4937, 1993, 1297, 3317, 3115, 5421, 3437, 1276,
1276, 1276, 8603, 1343, 2847, 262, 964, 1272
],
[
888, 538, 2419, 2491, 2492, 2283, 2944, 1654, 3197, 6746, 3409,
2768, 8992, 4533, 1314, 0, 1022, 2258, 2373, 886, 2241, 3116, 3176,
87, 5949, 4908, 4236, 6981, 3867, 3458, 5835, 4, 7341, 655, 4294,
11427, 10201, 4603, 166, 963, 2983, 1808, 5088, 3104, 943, 943,
943, 8269, 101, 154, 142, 9306, 289
],
[
1579, 156, 311, 3182, 3514, 3305, 3966, 2676, 3888, 7436, 41, 379,
9683, 5224, 1845, 1022, 0, 1228, 3395, 1577, 2931, 4138, 4198,
9391, 664, 5599, 4927, 7672, 4558, 4149, 6526, 5022, 8032, 7241,
3148, 12118, 10892, 5294, 2351, 1654, 3674, 283, 5778, 3794, 1634,
1634, 1634, 896, 1701, 2563, 1583, 9997, 733
],
[
2165, 2796, 3696, 3768, 4742, 4533, 5338, 3904, 4475, 8023, 4686,
5188, 10269, 581, 942, 2258, 1228, 0, 4624, 2164, 3518, 5367, 5426,
9978, 7226, 6185, 5513, 8258, 5144, 4736, 7112, 6395, 8619, 7827,
2499, 12704, 11478, 588, 2937, 2241, 426, 4058, 6365, 4381, 222,
222, 222, 9546, 2287, 3791, 838, 10584, 1937
],
[
2643, 212, 4429, 3855, 354, 382, 132, 2033, 3977, 75, 4164, 934,
11002, 6543, 368, 2373, 3395, 4624, 0, 2645, 4486, 979, 896, 10711,
7959, 6918, 6246, 7735, 4622, 3244, 4604, 2272, 9352, 856, 666,
13437, 12211, 6613, 367, 2997, 3737, 858, 4725, 3883, 3018, 3018,
3018, 10279, 2951, 936, 3786, 11317, 2662
],
[
1, 975, 1785, 1857, 2763, 2555, 3175, 1926, 2563, 6111, 2775, 3025,
8357, 3898, 122, 886, 1577, 2164, 2645, 0, 1606, 3388, 3447, 8066,
5315, 4273, 3601, 6346, 3233, 2824, 52, 4231, 6707, 5916, 42,
10792, 9566, 3968, 1025, 353, 2349, 2079, 4453, 2469, 373, 373,
373, 7634, 306, 1812, 1326, 8672, 844
],
[
1607, 2581, 11, 631, 4533, 4161, 4687, 3532, 924, 4885, 1549, 4537,
8347, 3888, 2574, 2241, 2931, 3518, 446, 1606, 0, 5178, 5053, 8056,
5304, 4263, 3591, 5121, 2007, 1598, 3975, 5744, 6697, 5906, 5554,
10782, 9556, 3958, 978, 1277, 1123, 3685, 3227, 83, 1298, 1298,
1298, 7624, 13, 3418, 268, 8662, 2198
],
[
3386, 2863, 5172, 4573, 633, 1336, 61, 2776, 4695, 8218, 4882, 641,
11745, 7286, 4423, 3116, 4138, 5367, 979, 3388, 5204, 0, 118,
11454, 8702, 7661, 6989, 8453, 4303, 3804, 3625, 1294, 10095, 9303,
7403, 1418, 12954, 7356, 4413, 374, 4455, 1775, 4372, 4601, 3761,
3761, 3761, 11022, 3694, 1679, 4529, 1206, 3405
],
[
3446, 2805, 5232, 4707, 866, 1091, 179, 2798, 483, 8352, 5016, 151,
11804, 7345, 4483, 3176, 4198, 5426, 896, 3447, 5053, 118, 0,
11513, 8762, 772, 7049, 8588, 5474, 4097, 4805, 2474, 10154, 9363,
7462, 14239, 13013, 7416, 4472, 38, 459, 1368, 5552, 4736, 3821,
3821, 3821, 11082, 3754, 1702, 4589, 12119, 3465
],
[
8067, 9041, 7881, 8724, 10829, 10621, 11241, 9991, 8901, 12978,
9642, 11091, 748, 4253, 9034, 87, 9391, 9978, 10711, 8066, 8056,
11454, 11513, 0, 2973, 4044, 4704, 13213, 101, 9691, 12067, 12297,
1359, 215, 12014, 2726, 15, 4499, 7465, 7737, 9215, 10145, 1132,
8807, 7758, 7758, 7758, 431, 7759, 9878, 914, 971, 8658
],
[
5316, 6289, 513, 5972, 8078, 7869, 8489, 724, 615, 10227, 689,
8339, 3264, 1501, 6283, 5949, 664, 7226, 7959, 5315, 5304, 8702,
8762, 2973, 0, 1292, 1953, 10462, 7348, 6939, 9316, 9546, 1614,
104, 9262, 5699, 4473, 1748, 4713, 4986, 6464, 7394, 8569, 6056,
5006, 5006, 5006, 2541, 5008, 7126, 6389, 3579, 5907
],
[
4275, 5248, 4089, 4931, 7036, 6828, 7448, 6199, 5108, 9185, 5849,
7298, 4353, 375, 5241, 4908, 5599, 6185, 6918, 4273, 4263, 7661,
772, 4061, 131, 0, 763, 942, 6307, 5898, 8274, 8504, 2703, 1911,
8221, 6788, 5562, 472, 3672, 3944, 5423, 6352, 7527, 5014, 3965,
3965, 3965, 363, 3967, 6085, 5347, 4667, 4865
],
[
3603, 4576, 3417, 4259, 6364, 6156, 6776, 5527, 4436, 8513, 5177,
6626, 4995, 536, 4569, 4236, 4927, 5513, 6246, 3601, 3591, 6989,
7049, 4704, 1953, 763, 0, 8749, 5635, 5226, 7602, 7833, 3345, 2554,
7549, 743, 6204, 55, 3, 3272, 4751, 5681, 6855, 4343, 3293, 3293,
3293, 4273, 3295, 5413, 4676, 531, 4193
],
[
6362, 7335, 6112, 4504, 7874, 815, 8029, 791, 3995, 235, 3586,
7878, 13519, 906, 7329, 6995, 7686, 8272, 7801, 6361, 5135, 8519,
8654, 13228, 10476, 9435, 8763, 0, 3561, 4196, 578, 8111, 11869,
11077, 10308, 15954, 14728, 913, 5732, 6032, 4104, 8074, 5033,
4089, 6052, 6052, 6052, 12796, 6054, 7702, 7435, 13833, 6953
],
[
3234, 4208, 2984, 1376, 3817, 4093, 3791, 4782, 1283, 3312, 713,
3663, 10391, 5932, 4201, 3867, 4558, 5144, 3744, 3233, 2007, 4303,
4597, 101, 7348, 6307, 5635, 3547, 0, 649, 2219, 455, 8741, 7949,
7181, 12826, 116, 6002, 2604, 2904, 884, 4017, 1471, 1377, 2924,
2924, 2924, 9668, 2926, 3645, 4307, 10706, 3825
],
[
2825, 3799, 2575, 967, 3318, 3593, 3291, 4374, 875, 3946, 61, 3163,
9982, 5523, 3792, 3458, 4149, 4736, 3244, 2824, 1598, 3804, 4097,
9691, 6939, 5898, 5226, 4181, 649, 0, 2376, 4347, 8332, 7541, 6772,
12417, 11191, 5593, 2196, 2495, 475, 3518, 1629, 968, 2516, 2516,
2516, 9259, 2517, 3146, 3898, 10297, 3416
],
[
5202, 6175, 4951, 3343, 4323, 5026, 3452, 5845, 3251, 553, 2858,
4099, 12358, 7899, 6168, 5835, 6526, 7112, 4669, 52, 3975, 369,
487, 12067, 9316, 8274, 7602, 5766, 2219, 2376, 0, 2331, 10708,
9917, 9148, 14793, 13567, 797, 4572, 4871, 2852, 5465, 747, 3345,
4892, 4892, 4892, 11636, 4894, 517, 6274, 12673, 5792
],
[
4295, 3875, 6081, 5203, 1991, 2695, 1121, 374, 5326, 8849, 5512,
1768, 12653, 8194, 5516, 4065, 5087, 646, 2337, 4296, 5834, 1359,
2539, 12362, 9611, 8569, 7897, 9084, 455, 4412, 2331, 0, 11003,
10212, 8496, 15088, 13862, 8264, 5321, 4649, 5086, 3134, 3078,
5232, 4669, 4669, 4669, 1193, 4603, 2838, 5622, 12968, 4354
],
[
6708, 7682, 6522, 7365, 947, 9262, 9882, 8633, 7542, 11619, 8283,
9732, 165, 2894, 7675, 7341, 8032, 8619, 9352, 6707, 6697, 10095,
10154, 1359, 1614, 2685, 3345, 11854, 8741, 8332, 10708, 10938, 0,
791, 10655, 4085, 2859, 314, 6106, 6378, 7857, 8786, 9961, 7448,
6399, 6399, 6399, 928, 64, 8519, 7781, 1965, 7299
],
[
5917, 6891, 5731, 6573, 8679, 847, 909, 7841, 6751, 10828, 7491,
894, 2441, 2102, 6884, 655, 7241, 7827, 856, 5916, 5906, 9303,
9363, 215, 104, 1893, 2554, 11063, 7949, 7541, 9917, 10147, 791, 0,
9863, 4876, 365, 2349, 5315, 5587, 7065, 7995, 917, 6657, 5607,
5607, 5607, 1719, 5609, 7728, 699, 2756, 6508
],
[
4201, 4832, 5732, 5805, 6778, 657, 7375, 594, 6511, 10059, 6723,
7224, 12305, 7846, 3135, 4294, 3148, 2499, 666, 42, 5554, 7403,
7462, 12014, 9262, 8221, 7549, 10294, 7181, 6772, 9148, 8431,
10655, 9863, 0, 1474, 13514, 7916, 4973, 4277, 6296, 6094, 8401,
6417, 4256, 4256, 4256, 11582, 4323, 5827, 2874, 1262, 3857
],
[
10794, 11767, 10608, 1145, 13555, 13347, 13967, 12718, 11627,
15704, 12368, 13817, 2468, 6979, 1176, 11427, 12118, 12704, 13437,
10792, 10782, 1418, 14239, 2726, 5699, 677, 743, 15939, 12826,
12417, 14793, 15023, 4085, 4876, 1474, 0, 1226, 7225, 10191, 10463,
11942, 12871, 14046, 11533, 10484, 10484, 10484, 3158, 10486,
12604, 11866, 2154, 11384
],
[
9568, 10541, 9382, 10224, 12329, 12121, 12741, 11492, 10401, 14478,
11142, 12591, 1242, 5753, 10534, 10201, 10892, 11478, 12211, 9566,
9556, 12954, 13013, 15, 4473, 5544, 6204, 14713, 116, 11191, 13567,
13797, 2859, 365, 13514, 1226, 0, 5999, 8965, 9237, 10716, 11645,
1282, 10307, 9258, 9258, 9258, 1932, 926, 11378, 1064, 928, 10158
],
[
397, 4943, 3784, 4626, 6732, 6523, 7143, 5894, 4804, 8881, 5544,
6993, 4808, 411, 4937, 4603, 5294, 588, 6613, 3968, 3958, 7356,
7416, 4517, 1765, 472, 55, 9116, 6002, 5593, 797, 82, 3158, 2366,
7916, 7243, 6017, 0, 3367, 364, 5118, 6048, 7222, 471, 366, 366,
366, 4085, 3662, 578, 5043, 5123, 4561
],
[
1027, 2, 759, 1228, 3788, 358, 42, 2951, 1935, 5483, 2146, 3622,
7756, 3297, 1993, 166, 2351, 2937, 367, 1025, 978, 4413, 4472,
7465, 4713, 3672, 3, 5718, 2604, 2196, 4572, 5256, 6106, 5315,
4973, 10191, 8965, 3367, 0, 696, 172, 3104, 3825, 1841, 717, 717,
717, 7033, 719, 2837, 2099, 8071, 1617
],
[
354, 1328, 1456, 1528, 3116, 2907, 3527, 2278, 2234, 5782, 2446,
3377, 8028, 3569, 1297, 963, 1654, 2241, 2997, 353, 1277, 374, 38,
7737, 4986, 3944, 3272, 6017, 2904, 2495, 4871, 4584, 6378, 5587,
4277, 10463, 9237, 364, 696, 0, 202, 2432, 4124, 214, 21, 21, 21,
7306, 46, 2165, 1403, 8343, 921
],
[
235, 3323, 21, 492, 3862, 4138, 4017, 3898, 479, 3855, 518, 3866,
9507, 5048, 3317, 2983, 3674, 426, 3789, 2349, 1123, 4507, 4642,
9215, 6464, 5423, 4751, 409, 884, 475, 2852, 5073, 7857, 7065,
6296, 11942, 10716, 5118, 172, 202, 0, 4062, 2104, 521, 204, 204,
204, 8784, 2042, 369, 3423, 9821, 2941
],
[
2078, 1437, 3864, 3936, 115, 475, 2089, 1919, 425, 7773, 4437,
1703, 10436, 5977, 3115, 1808, 283, 4058, 858, 2079, 3685, 1775,
1368, 10145, 7394, 6352, 5681, 8008, 4895, 3518, 54, 3069, 8786,
7995, 6094, 12871, 11645, 6048, 3104, 2432, 4011, 0, 4998, 4157,
2453, 2453, 2453, 9714, 2386, 59, 3221, 10751, 2097
],
[
4454, 5428, 4204, 2596, 4798, 5074, 42, 5098, 2504, 4783, 2111,
4643, 11611, 7152, 5421, 5088, 5778, 6365, 4725, 4453, 3227, 4437,
5617, 1132, 8569, 7527, 6855, 5019, 1471, 1629, 747, 3078, 9961,
917, 8401, 14046, 1282, 7222, 3825, 4124, 2104, 4998, 0, 2598,
4145, 4145, 4145, 10888, 4147, 4626, 5527, 11926, 5045
],
[
245, 3424, 1496, 592, 4037, 4312, 4191, 4073, 94, 3839, 809, 4041,
9098, 4639, 3417, 3083, 3774, 4361, 3963, 2449, 83, 4681, 4816,
8807, 6056, 5014, 4343, 4074, 1377, 968, 3345, 5247, 7448, 6657,
6397, 11533, 10307, 471, 1821, 212, 521, 4237, 2598, 0, 2141, 2141,
2141, 8376, 2142, 3864, 3523, 9413, 3041
],
[
375, 1348, 1476, 1548, 3136, 2928, 3548, 2299, 2255, 5803, 2467,
3398, 8049, 359, 1276, 943, 1634, 222, 3018, 373, 1298, 3761, 3821,
7758, 5006, 3965, 3293, 6038, 2924, 2516, 4892, 4605, 6399, 5607,
4256, 10484, 9258, 366, 717, 21, 204, 2453, 4145, 2161, 0, 0, 0,
7326, 67, 2185, 1382, 8364, 9
],
[
375, 1348, 1476, 1548, 3136, 2928, 3548, 2299, 2255, 5803, 2467,
3398, 8049, 359, 1276, 943, 1634, 222, 3018, 373, 1298, 3761, 3821,
7758, 5006, 3965, 3293, 6038, 2924, 2516, 4892, 4605, 6399, 5607,
4256, 10484, 9258, 366, 717, 21, 204, 2453, 4145, 2161, 0, 0, 0,
7326, 67, 2185, 1382, 8364, 9
],
[
375, 1348, 1476, 1548, 3136, 2928, 3548, 2299, 2255, 5803, 2467,
3398, 8049, 359, 1276, 943, 1634, 222, 3018, 373, 1298, 3761, 3821,
7758, 5006, 3965, 3293, 6038, 2924, 2516, 4892, 4605, 6399, 5607,
4256, 10484, 9258, 366, 717, 21, 204, 2453, 4145, 2161, 0, 0, 0,
7326, 67, 2185, 1382, 8364, 9
],
[
7636, 8609, 745, 8292, 10398, 10189, 10809, 956, 847, 12547, 921,
10659, 723, 3821, 8603, 8269, 896, 9546, 10279, 7634, 7624, 11022,
11082, 431, 2541, 3612, 4273, 12782, 9668, 9259, 11636, 11866, 928,
1719, 11582, 3158, 1932, 4067, 7033, 7306, 8784, 9714, 10888, 8376,
7326, 7326, 7326, 0, 7328, 9446, 8709, 1037, 8227
],
[
308, 1281, 1478, 155, 307, 2861, 3481, 2232, 2256, 5805, 2468,
3331, 8051, 3592, 1343, 101, 1701, 2287, 2951, 306, 13, 3694, 3754,
7759, 5008, 3967, 3295, 604, 2926, 2517, 4894, 4538, 64, 5609,
4323, 10486, 926, 3662, 719, 46, 2042, 2386, 4147, 2163, 67, 67,
67, 7328, 0, 2118, 1449, 8365, 967
],
[
181, 1277, 3596, 3756, 1055, 832, 1717, 1329, 3878, 7401, 4065,
1331, 10169, 571, 2847, 154, 2563, 3791, 936, 1812, 3418, 1679,
1702, 9878, 7126, 6085, 5413, 7636, 4523, 3146, 5105, 2774, 8519,
7728, 5827, 12604, 11378, 578, 2837, 2165, 3638, 59, 4626, 3785,
2185, 2185, 2185, 9446, 2118, 0, 2953, 10484, 1829
],
[
1328, 1958, 2859, 2931, 3904, 3696, 4501, 3067, 3637, 7185, 3849,
4351, 9431, 4972, 262, 142, 1583, 838, 3786, 1326, 268, 4529, 4589,
914, 6389, 5347, 4676, 742, 4307, 3898, 6274, 5557, 7781, 699,
2874, 11866, 1064, 5043, 2099, 1403, 3423, 3221, 5527, 3543, 1382,
1382, 1382, 8709, 1449, 2953, 0, 9746, 1378
],
[
8673, 9647, 8487, 933, 11435, 11226, 11847, 10597, 9507, 13584,
10248, 11697, 315, 4859, 964, 9306, 9997, 10584, 11317, 8672, 8662,
1206, 12119, 971, 3579, 465, 531, 13819, 10706, 10297, 12673,
12903, 1965, 2756, 1262, 2154, 928, 5105, 8071, 8343, 9821, 10751,
11926, 9413, 8364, 8364, 8364, 1037, 8365, 10484, 9746, 0, 9264
],
[
846, 827, 2377, 2449, 278, 2572, 3233, 1943, 3155, 6703, 3367,
3057, 8949, 449, 1272, 289, 733, 1937, 2662, 844, 2198, 3405, 3465,
8658, 5907, 4865, 4193, 6938, 3825, 3416, 5792, 4289, 7299, 6508,
3857, 11384, 10158, 4561, 1617, 921, 2941, 2097, 5045, 3061, 9, 9,
9, 8227, 967, 1829, 1378, 9264, 0
]
]
# [0] depot
# [1-20] Delivery Passengers
# [21,40] Pickup Passengers
# [41, 46] Delivery Goods
# [47, 52] Pickup Goods
data['Pickups_Deliveries_Passengers'] = [
[21, 1, 2], # Passengers
[22, 2, 1],
[23, 3, 2],
[24, 4, 1],
[25, 5, 2],
[26, 6, 2],
[27, 7, 2],
[28, 8, 2],
[29, 9, 2],
[30, 10, 2],
[31, 11, 1],
[32, 12, 2],
[33, 13, 2],
[34, 14, 2],
[35, 15, 2],
[36, 16, 2],
[37, 17, 2],
[38, 18, 2],
[39, 19, 3],
[40, 20, 2],
[47, 41, -1], # Goods
[48, 42, -1],
[49, 43, -1],
[50, 44, -1],
[51, 45, -1],
[52, 46, -1],
]
#data['Pickups_Deliveries_Passengers'] = [[21,1,2], [22,2,1.15], [23,3,2], [24,4,1.15], [25,5,2], [26,6,2], [27,7,2], [28,8,2], [29,9,2], [30,10,2], [31,11,1.15],[32,12,2],[33,13,2],[34,14,1.15], [35,15,2], [36,16,2],[37,17,2], [38,18,2], [39,19,1.15],[40,20,2],[47, 41,-1], [48, 42,-1], [49, 43,-1], [50, 44,-1], [51, 45, -1], [52, 46, -1]]
#data['pickups_deliveries_Goods'] = [[47, 41,-1], [48, 42,-1], [49, 43,-1], [50, 44,-1], [51, 45, -1], [52, 46, -1]]
data['vehicle_capacities_Passengers'] = [6, 6, 6, 6, 6, 6, 6]
data['vehicle_capacities_Goods'] = [4, 4, 4, 4, 4, 4, 4]
data['num_vehicles'] = 7
data['depot'] = 0
data['demands_Passengers'] = [
0, # 0 depot
-1, # 1 Delivery Passengers
-1,
-1,
-1,
-1, # 5
-1,
-1,
-1,
-1,
-1, # 10
-1,
-1,
-1,
-1,
-1, # 15
-1,
-1,
-1,
-1,
-1, # 20
1, # 21 Pickup Passengers
1,
1,
1,
1, # 25
1,
1,
1,
1,
1, # 30
1,
1,
1,
1,
1, # 35
1,
1,
1,
1,
1, # 40
0, #41 Delivery Goods
0,
0,
0,
0, # 45
0,
0, # 47 Pickup Goods
0,
0,
0, # 50
0,
0, # 52
]
data['demands_Goods'] = [
0, # 0 depot
0, # 1 Delivery Passengers
0,
0,
0,
0, # 5
0,
0,
0,
0,
0, # 10
0,
0,
0,
0,
0, # 15
0,
0,
0,
0,
0, # 20
0, # 21 Pickup Passengers
0,
0,
0,
0, # 25
0,
0,
0,
0,
0, # 30
0,
0,
0,
0,
0, # 35
0,
0,
0,
0,
0, # 40
-1, # 41 Delivery Goods
-1,
-1,
-1,
-1,
-1,
1, # 47 Pickup Goods
1,
1,
1,
1,
1, # 52
]
data['time_windows'] = [
(0, 100_000), # 0 depot
(0, 100_000), # 1 Delivery Passengers
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000), # 5
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000), # 10
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000), # 15
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000), # 20
(0, 100_000), # 21 Pickup Passengers
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000), #25
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000), #30
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000), #35
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000), #40
(0, 100_000), # 41 Delivery Goods
(0, 100_000),
(0, 100_000),
(0, 100_000),
(0, 100_000), # 45
(0, 100_000),
(0, 100_000), # 47 Pickup Goods
(0, 100_000),
(0, 100_000),
(0, 100_000), # 50
(0, 100_000),
(0, 100_000), #52
]
assert len(data['time_matrix']) == len(data['distance_matrix'])
assert len(data['vehicle_capacities_Passengers']) == data['num_vehicles']
assert len(data['vehicle_capacities_Goods']) == data['num_vehicles']
assert len(data['demands_Passengers']) == len(data['demands_Goods'])
assert len(data['demands_Passengers']) == len(data['time_matrix'])
assert len(data['time_windows']) == len(data['time_matrix'])
return data
### Start the printing section
def print_solution(data, manager, routing, solution):
"""Prints solution on console."""
print(f'Objective: {solution.ObjectiveValue()}')
# Display dropped nodes.
dropped_nodes = 'Dropped nodes:'
for node in range(routing.Size()):
if routing.IsStart(node) or routing.IsEnd(node):
continue
if solution.Value(routing.NextVar(node)) == node:
dropped_nodes += ' {}'.format(manager.IndexToNode(node))
print(dropped_nodes)
# Print routes
time_dimension = routing.GetDimensionOrDie('Time')
distance_dimension = routing.GetDimensionOrDie('Distance')
capacity_dimension_p = routing.GetDimensionOrDie('Capacity_Passengers')
capacity_dimension_g = routing.GetDimensionOrDie('Capacity_Goods')
total_time = 0
total_distance = 0
total_load_passengers = 0
total_load_goods = 0
for vehicle_id in range(manager.GetNumberOfVehicles()):
index = routing.Start(vehicle_id)
plan_output = f'Route for vehicle {vehicle_id}:\n'
while not routing.IsEnd(index):
node_index = manager.IndexToNode(index)
time_var = time_dimension.CumulVar(index)
distance_var = distance_dimension.CumulVar(index)
capacity_var_p = capacity_dimension_p.CumulVar(index)
capacity_var_g = capacity_dimension_g.CumulVar(index)
plan_output += '{0} Time({1},{2}) Distance:{3} Passenger_Load:{4}, Goods_Load:{5} -> '.format(
node_index,
solution.Min(time_var), solution.Max(time_var),
solution.Value(distance_var),
solution.Value(capacity_var_p), solution.Value(capacity_var_g))
index = solution.Value(routing.NextVar(index))
node_index = manager.IndexToNode(index)
time_var = time_dimension.CumulVar(index)
distance_var = distance_dimension.CumulVar(index)
capacity_var_p = capacity_dimension_p.CumulVar(index)
capacity_var_g = capacity_dimension_g.CumulVar(index)
plan_output += '{0} Time({1},{2}) Distance:{3} Passenger_Load:{4}, Goods_Load:{5})\n'.format(
manager.IndexToNode(index),
solution.Min(time_var), solution.Max(time_var),
solution.Value(distance_var),
solution.Value(capacity_var_p), solution.Value(capacity_var_g))
plan_output += 'Time of the route: {}sec\n'.format(
solution.Min(time_var))
plan_output += 'Distance of the route: {}m\n'.format(
solution.Value(distance_var))
plan_output += 'Load of the passenger: {}\n'.format(
solution.Value(capacity_var_p))
plan_output += 'Load of the goods: {}\n'.format(
solution.Value(capacity_var_g))
print(plan_output)
total_time += solution.Min(time_var)
total_distance += solution.Value(distance_var)
total_load_passengers += solution.Value(capacity_var_p)
total_load_goods += solution.Value(capacity_var_g)
print('Total time of all routes: {}sec'.format(total_time))
print('Total distance of all routes: {}m'.format(total_distance))
print(
'Total load of all routes_passenger: {}'.format(total_load_passengers))
print('Total load of all routes_goods: {}'.format(total_load_goods))
### Start the main funciton
def main():
"""Solve the CVRP problem."""
# Instantiate the data problem.
data = create_data_model()
# Create the routing index manager.
manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),
data['num_vehicles'], data['depot'])
# Create Routing Model.
routing = pywrapcp.RoutingModel(manager)
# Create and register a transit callback for distance.
def distance_callback(from_index, to_index):
"""Returns the distance between the two nodes."""
# Convert from routing variable Index to distance matrix NodeIndex.
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
return data['distance_matrix'][from_node][to_node]
transit_callback_index = routing.RegisterTransitCallback(distance_callback)
# Define cost of each arc.
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
# Add Distance constraint.
dimension_name = 'Distance'
routing.AddDimension(
transit_callback_index,
0, # no slack
#100_000, # vehicle maximum travel distance
50_000, # if one vehicle aka tsp distance ~=67000 -> force solver to split demands since one vehicle can't visit all locations
True, # start cumul to zero
dimension_name)
distance_dimension = routing.GetDimensionOrDie(dimension_name)
distance_dimension.SetGlobalSpanCostCoefficient(100)
# Add Passenger Seats Capacity constraint.
def demand_callback_p(from_index):
"""Returns the demand of the node."""
# Convert from routing variable Index to demands NodeIndex.
from_node = manager.IndexToNode(from_index)
return data['demands_Passengers'][from_node]
demand_callback_index_p = routing.RegisterUnaryTransitCallback(demand_callback_p)
routing.AddDimensionWithVehicleCapacity(
demand_callback_index_p,
0, # null capacity slack
data['vehicle_capacities_Passengers'], # vehicle maximum capacities
True, # start cumul to zero
'Capacity_Passengers')
# Add Goods Capacity constraint.
def demand_callback_g(from_index):
"""Returns the demand of the node."""
# Convert from routing variable Index to demands NodeIndex.
from_node = manager.IndexToNode(from_index)
return data['demands_Goods'][from_node]
demand_callback_index_g = routing.RegisterUnaryTransitCallback(demand_callback_g)
routing.AddDimensionWithVehicleCapacity(
demand_callback_index_g,
0, # null capacity slack
data['vehicle_capacities_Goods'], # vehicle maximum capacities
True, # start cumul to zero
'Capacity_Goods')
## Add Time window constraint
# Create and register a transit callback for time.
def transit_time_callback(from_index, to_index):
"""Returns the travel time between the two nodes."""
# Convert from routing variable Index to time matrix NodeIndex.
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
return data['time_matrix'][from_node][to_node]
transit_time_callback_index = routing.RegisterTransitCallback(transit_time_callback)
# Define cost of each arc.
routing.SetArcCostEvaluatorOfAllVehicles(transit_time_callback_index)
# Add Time Windows constraint.
def time_callback(from_index, to_index):
"""Retruns the travel time between the two nodes"""
# convert from routing variable Index to the time matrix NodeIndex
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
return data['time_matrix'][from_node][to_node]
time_callback_index = routing.RegisterTransitCallback(time_callback)
time = 'Time'
routing.AddDimension(
transit_time_callback_index,
0, # allow waiting time
100_000, # maximum time per vehicle
False, # Don't force start cumul to zero.
time)
time_dimension = routing.GetDimensionOrDie(time)
# Add time window constraints for each location except depot.
for location_idx, time_window in enumerate(data['time_windows']):
if location_idx == data['depot']:
continue
index = manager.NodeToIndex(location_idx)
time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])
# Add time window constraints for each vehicle start node.
depot_idx = data['depot']
for vehicle_id in range(manager.GetNumberOfVehicles()):
index = routing.Start(vehicle_id)
time_dimension.CumulVar(index).SetRange(
data['time_windows'][depot_idx][0],
data['time_windows'][depot_idx][1])
# Instantiate route start and end times to produce feasible times.
for i in range(manager.GetNumberOfVehicles()):
routing.AddVariableMinimizedByFinalizer(
time_dimension.CumulVar(routing.Start(i)))
routing.AddVariableMinimizedByFinalizer(
time_dimension.CumulVar(routing.End(i)))
# Define Transportation Requests for Passengers.
for pickup, delivery, _ in data['Pickups_Deliveries_Passengers']:
passenger_pickup_index = manager.NodeToIndex(pickup)
passenger_delivery_index = manager.NodeToIndex(delivery)
routing.AddPickupAndDelivery(passenger_pickup_index, passenger_delivery_index)
routing.solver().Add(
routing.VehicleVar(passenger_pickup_index) ==
routing.VehicleVar(passenger_delivery_index))
routing.solver().Add(
distance_dimension.CumulVar(passenger_pickup_index) <=
distance_dimension.CumulVar(passenger_delivery_index))
# Give the maximum travel time for individual
for pickup, delivery, tardiness_coef in data['Pickups_Deliveries_Passengers']:
if tardiness_coef == -1: # nothing to do if no max delay
continue
p_index = manager.NodeToIndex(pickup)
d_index = manager.NodeToIndex(delivery)
transit_time = transit_time_callback(p_index, d_index) # transit time for the fastest path.
tardiness = int(math.floor(tardiness_coef * transit_time)) # round the value to integer
routing.solver().Add(
time_dimension.CumulVar(d_index) <=
time_dimension.CumulVar(p_index) + tardiness)
# Setting first solution heuristic.
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (
#routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
routing_enums_pb2.FirstSolutionStrategy.PARALLEL_CHEAPEST_INSERTION)
search_parameters.local_search_metaheuristic = (
routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
search_parameters.log_search = True
search_parameters.time_limit.FromSeconds(200)
# Solve the problem.
solution = routing.SolveWithParameters(search_parameters)
# Print solution on console.
if solution:
print_solution(data, manager, routing, solution)
else:
print('No Solution was found !')
if __name__ == '__main__':
main()
Raw
zz_diff.md

a diff with the original code

0a1
> #!/usr/bin/env python3
5c6
< import pandas as pd
---
> #import pandas as pd
21,24c22,26
<             77, 0, 234, 217, 221, 189, 272, 144, 280, 556, 286, 248, 718, 376,
<             175, 53, 154, 265, 209, 77, 203, 283, 277, 684, 485, 413, 351, 579,
<             345, 304, 538, 376, 582, 522, 440, 912, 802, 402, 159, 104, 257,
<             142, 465, 270, 106, 106, 106, 652, 100, 126, 184, 749, 82
---
>             77, 0, 234, 217, 221, 189, 272, 144, 280, 556, 286,
>             248, 718, 376, 175, 53, 154, 265, 209, 77, 203, 283, 277, 684, 485,
>             413, 351, 579, 345, 304, 538, 376, 582, 522, 440, 912, 802, 402,
>             159, 104, 257, 142, 465, 270, 106, 106, 106, 652, 100, 126, 184,
>             749, 82
338c340
<         ],
---
>         ]
349,353c351,355
<             973, 0, 2759, 2832, 2238, 1913, 2754, 1464, 3538, 7086, 375, 2514,
<             9332, 4873, 1852, 538, 156, 2796, 212, 975, 2581, 2863, 2805, 9041,
<             6289, 5248, 4576, 7321, 4208, 3799, 6175, 3811, 7682, 6891, 4832,
<             11767, 10541, 4943, 2, 1328, 3323, 1437, 5428, 3444, 1348, 1348,
<             1348, 8609, 1281, 1277, 1958, 9647, 827
---
>             973, 0, 2759, 2832, 2238, 1913, 2754, 1464, 3538, 7086, 375,
>             2514, 9332, 4873, 1852, 538, 156, 2796, 212, 975, 2581, 2863, 2805,
>             9041, 6289, 5248, 4576, 7321, 4208, 3799, 6175, 3811, 7682, 6891,
>             4832, 11767, 10541, 4943, 2, 1328, 3323, 1437, 5428, 3444, 1348,
>             1348, 1348, 8609, 1281, 1277, 1958, 9647, 827
712c714
<         ],
---
>         ]
714a717,721
>     # [0] depot
>     # [1-20] Delivery Passengers
>     # [21,40] Pickup Passengers
>     # [41, 46] Delivery Goods
>     # [47, 52] Pickup Goods
716c723
<         [21, 1, 2],
---
>         [21, 1, 2], # Passengers
736c743
<         [47, 41, -1],
---
>         [47, 41, -1], # Goods
746c753
<     data['vehicle_capacities_Passengers'] = [6, 6, 6, 6, 6, 6]
---
>     data['vehicle_capacities_Passengers'] = [6, 6, 6, 6, 6, 6, 6]
751,753c758,810
<         -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
<         -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
<         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
---
>         0, # 0 depot
>         -1, # 1 Delivery Passengers
>         -1,
>         -1,
>         -1,
>         -1, # 5
>         -1,
>         -1,
>         -1,
>         -1,
>         -1, # 10
>         -1,
>         -1,
>         -1,
>         -1,
>         -1, # 15
>         -1,
>         -1,
>         -1,
>         -1,
>         -1, # 20
>         1, # 21 Pickup Passengers
>         1,
>         1,
>         1,
>         1, # 25
>         1,
>         1,
>         1,
>         1,
>         1, # 30
>         1,
>         1,
>         1,
>         1,
>         1, # 35
>         1,
>         1,
>         1,
>         1,
>         1, # 40
>         0, #41 Delivery Goods
>         0,
>         0,
>         0,
>         0, # 45
>         0,
>         0, # 47 Pickup Goods
>         0,
>         0,
>         0, # 50
>         0,
>         0, # 52
756,758c813,865
<         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, -1, -1, -1, -1,
<         -1, 1, 1, 1, 1, 1, 1
---
>         0, # 0 depot
>         0, # 1 Delivery Passengers
>         0,
>         0,
>         0,
>         0, # 5
>         0,
>         0,
>         0,
>         0,
>         0, # 10
>         0,
>         0,
>         0,
>         0,
>         0, # 15
>         0,
>         0,
>         0,
>         0,
>         0, # 20
>         0, # 21 Pickup Passengers
>         0,
>         0,
>         0,
>         0, # 25
>         0,
>         0,
>         0,
>         0,
>         0, # 30
>         0,
>         0,
>         0,
>         0,
>         0, # 35
>         0,
>         0,
>         0,
>         0,
>         0, # 40
>         -1, # 41 Delivery Goods
>         -1,
>         -1,
>         -1,
>         -1,
>         -1,
>         1, # 47 Pickup Goods
>         1,
>         1,
>         1,
>         1,
>         1, # 52
761,804c868,920
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 100000),
<         (0, 10000),
<         (0, 10000),
<         (0, 100000),
---
>         (0, 100_000), # 0 depot
>         (0, 100_000), # 1 Delivery Passengers
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000), # 5
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000), # 10
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000), # 15
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000), # 20
>         (0, 100_000), # 21 Pickup Passengers
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000), #25
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000), #30
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000), #35
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000), #40
>         (0, 100_000), # 41 Delivery Goods
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000), # 45
>         (0, 100_000),
>         (0, 100_000), # 47 Pickup Goods
>         (0, 100_000),
>         (0, 100_000),
>         (0, 100_000), # 50
>         (0, 100_000),
>         (0, 100_000), #52
805a922,927
>     assert len(data['time_matrix']) == len(data['distance_matrix'])
>     assert len(data['vehicle_capacities_Passengers']) == data['num_vehicles']
>     assert len(data['vehicle_capacities_Goods']) == data['num_vehicles']
>     assert len(data['demands_Passengers']) == len(data['demands_Goods'])
>     assert len(data['demands_Passengers']) == len(data['time_matrix'])
>     assert len(data['time_windows']) == len(data['time_matrix'])
808,809d929
<     ### Start the printing section
< 
810a931
> ### Start the printing section
825c946
<     capacity_dimension_p = routing.GetDimensionOrDie('Capacity_passengers')
---
>     capacity_dimension_p = routing.GetDimensionOrDie('Capacity_Passengers')
840,843c961,965
<             plan_output += '{0}node Time({1},{2}) Distance:{3} Passenger_Load:{4}, Goods_Load{5}  -> '.format(
<                 node_index, solution.Min(time_var), solution.Max(time_var),
<                 solution.Value(distance_var), solution.Value(capacity_var_p),
<                 solution.Value(capacity_var_g))
---
>             plan_output += '{0} Time({1},{2}) Distance:{3} Passenger_Load:{4}, Goods_Load:{5} -> '.format(
>                 node_index,
>                 solution.Min(time_var), solution.Max(time_var),
>                 solution.Value(distance_var),
>                 solution.Value(capacity_var_p), solution.Value(capacity_var_g))
848,853c970,975
<         load_var_p = capacity_dimension_p.CumulVar(index)
<         load_var_g = capacity_dimension_g.CumulVar(index)
< 
<         plan_output += '{0}node Time({1},{2}) Distance:{3} Passenger_Load:{4}, Goods_Load{5})\n'.format(
<             manager.IndexToNode(index), solution.Min(time_var),
<             solution.Max(time_var), solution.Value(distance_var),
---
>         capacity_var_p = capacity_dimension_p.CumulVar(index)
>         capacity_var_g = capacity_dimension_g.CumulVar(index)
>         plan_output += '{0} Time({1},{2}) Distance:{3} Passenger_Load:{4}, Goods_Load:{5})\n'.format(
>             manager.IndexToNode(index),
>             solution.Min(time_var), solution.Max(time_var),
>             solution.Value(distance_var),
874,875d995
<     ### Start the main funciton
< 
876a997
> ### Start the main funciton
907c1028,1029
<         1000000,  # vehicle maximum travel distance
---
>         #100_000,  # vehicle maximum travel distance
>         50_000,  # if one vehicle aka tsp distance ~=67000 -> force solver to split demands since one vehicle can't visit all locations
920,921c1042,1043
<     demand_callback_index_p = routing.RegisterUnaryTransitCallback(
<         demand_callback_p)
---
>     demand_callback_index_p = routing.RegisterUnaryTransitCallback(demand_callback_p)
> 
927c1049
<         'Capacity_passengers')
---
>         'Capacity_Passengers')
936,937c1058,1059
<     demand_callback_index_g = routing.RegisterUnaryTransitCallback(
<         demand_callback_g)
---
>     demand_callback_index_g = routing.RegisterUnaryTransitCallback(demand_callback_g)
> 
954,955c1076
<     transit_time_callback_index = routing.RegisterTransitCallback(
<         transit_time_callback)
---
>     transit_time_callback_index = routing.RegisterTransitCallback(transit_time_callback)
974c1095
<         1000000000,  # maximum time per vehicle
---
>         100_000,  # maximum time per vehicle
988c1109
<     for vehicle_id in range(data['num_vehicles']):
---
>     for vehicle_id in range(manager.GetNumberOfVehicles()):
995c1116
<     for i in range(data['num_vehicles']):
---
>     for i in range(manager.GetNumberOfVehicles()):
1002,1006c1123,1126
<     for request in data['Pickups_Deliveries_Passengers']:
<         passenger_pickup_index = manager.NodeToIndex(request[0])
<         passenger_delivery_index = manager.NodeToIndex(request[1])
<         routing.AddPickupAndDelivery(passenger_pickup_index,
<                                      passenger_delivery_index)
---
>     for pickup, delivery, _ in data['Pickups_Deliveries_Passengers']:
>         passenger_pickup_index = manager.NodeToIndex(pickup)
>         passenger_delivery_index = manager.NodeToIndex(delivery)
>         routing.AddPickupAndDelivery(passenger_pickup_index, passenger_delivery_index)
1008,1009c1128,1129
<             routing.VehicleVar(passenger_pickup_index) == routing.VehicleVar(
<                 passenger_delivery_index))
---
>             routing.VehicleVar(passenger_pickup_index) ==
>             routing.VehicleVar(passenger_delivery_index))
1015,1017c1135,1136
<     for pickup, delivery, tardiness_coef in data[
<             'Pickups_Deliveries_Passengers']:
<         if coef == -1:  # nothing to do if no max delay
---
>     for pickup, delivery, tardiness_coef in data['Pickups_Deliveries_Passengers']:
>         if tardiness_coef == -1:  # nothing to do if no max delay
1019,1027c1138,1144
<     p_index = manager.NodeToIndex(pickup)
<     d_index = manager.NodeToIndex(delivery)
<     transit_time = transit_time_callback(
<         p_index, d_index)  # transit time for the fastest path.
<     tardiness = int(math.floor(tardiness_coef *
<                                transit_time))  # round the value to integer
<     routing.solver().Add(
<         time_dimension.CumulVar(d_index) <= time_dimension.CumulVar(p_index) +
<         tardiness)
---
>         p_index = manager.NodeToIndex(pickup)
>         d_index = manager.NodeToIndex(delivery)
>         transit_time = transit_time_callback(p_index, d_index)  # transit time for the fastest path.
>         tardiness = int(math.floor(tardiness_coef * transit_time))  # round the value to integer
>         routing.solver().Add(
>             time_dimension.CumulVar(d_index) <=
>             time_dimension.CumulVar(p_index) + tardiness)
1032c1149,1150
<         routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
---
>         #routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
>         routing_enums_pb2.FirstSolutionStrategy.PARALLEL_CHEAPEST_INSERTION)
1035c1153,1154
<     search_parameters.time_limit.FromSeconds = (3600)
---
>     search_parameters.log_search = True
>     search_parameters.time_limit.FromSeconds(200)
1047c1166,1167
< if __name__ == '__main__': main()
---
> if __name__ == '__main__':
>     main()
Raw
zz_output.md

possible output:

$ ./code.py
Objective: 1399983
Dropped nodes:
Route for vehicle 0:
0 Time(0,0) Distance:0 Passenger_Load:0, Goods_Load:0 -> 48 Time(23,23) Distance:308 Passenger_Load:0, Goods_Load:0 -> 32 Time(505,505) Distance:372 Passenger_Load:0, Goods_Load:1 -> 12 Time(641,641) Distance:537 Passenger_Load:1, Goods_Load:1 -> 47 Time(708,708) Distance:1260 Passenger_Load:0, Goods_Load:1 -> 51 Time(806,806) Distance:2297 Passenger_Load:0, Goods_Load:2 -> 36 Time(886,886) Distance:3225 Passenger_Load:0, Goods_Load:3 -> 35 Time(996,996) Distance:4451 Passenger_Load:1, Goods_Load:3 -> 25 Time(1543,1543) Distance:5128 Passenger_Load:2, Goods_Load:3 -> 37 Time(1590,1590) Distance:5600 Passenger_Load:3, Goods_Load:3 -> 45 Time(1892,1892) Distance:5966 Passenger_Load:4, Goods_Load:3 -> 17 Time(2096,2096) Distance:6188 Passenger_Load:4, Goods_Load:2 -> 16 Time(2217,2217) Distance:7416 Passenger_Load:3, Goods_Load:2 -> 15 Time(2318,2318) Distance:8438 Passenger_Load:2, Goods_Load:2 -> 49 Time(2469,2469) Distance:8592 Passenger_Load:1, Goods_Load:2 -> 5 Time(2551,2551) Distance:9424 Passenger_Load:1, Goods_Load:3 -> 41 Time(2598,2598) Distance:9899 Passenger_Load:0, Goods_Load:3 -> 30 Time(3131,3131) Distance:9953 Passenger_Load:0, Goods_Load:2 -> 42 Time(3205,3205) Distance:10700 Passenger_Load:1, Goods_Load:2 -> 10 Time(3411,3411) Distance:12811 Passenger_Load:1, Goods_Load:1 -> 43 Time(3488,3488) Distance:13620 Passenger_Load:0, Goods_Load:1 -> 0 Time(3680,3680) Distance:13865 Passenger_Load:0, Goods_Load:0)
Time of the route: 3680sec
Distance of the route: 13865m
Load of the passenger: 0
Load of the goods: 0

Route for vehicle 1:
0 Time(0,0) Distance:0 Passenger_Load:0, Goods_Load:0 -> 0 Time(0,0) Distance:0 Passenger_Load:0, Goods_Load:0)
Time of the route: 0sec
Distance of the route: 0m
Load of the passenger: 0
Load of the goods: 0

Route for vehicle 2:
0 Time(0,0) Distance:0 Passenger_Load:0, Goods_Load:0 -> 50 Time(117,117) Distance:1328 Passenger_Load:0, Goods_Load:0 -> 23 Time(824,824) Distance:2242 Passenger_Load:0, Goods_Load:1 -> 33 Time(986,986) Distance:2457 Passenger_Load:1, Goods_Load:1 -> 13 Time(1164,1164) Distance:4559 Passenger_Load:2, Goods_Load:1 -> 3 Time(1533,1533) Distance:9115 Passenger_Load:1, Goods_Load:1 -> 40 Time(1573,1573) Distance:9607 Passenger_Load:0, Goods_Load:1 -> 20 Time(1665,1665) Distance:10730 Passenger_Load:1, Goods_Load:1 -> 44 Time(1768,1768) Distance:12028 Passenger_Load:0, Goods_Load:1 -> 39 Time(1770,1770) Distance:12049 Passenger_Load:0, Goods_Load:0 -> 19 Time(1797,1797) Distance:12402 Passenger_Load:1, Goods_Load:0 -> 0 Time(1797,1797) Distance:12403 Passenger_Load:0, Goods_Load:0)
Time of the route: 1797sec
Distance of the route: 12403m
Load of the passenger: 0
Load of the goods: 0

Route for vehicle 3:
0 Time(0,0) Distance:0 Passenger_Load:0, Goods_Load:0 -> 31 Time(393,393) Distance:423 Passenger_Load:0, Goods_Load:0 -> 11 Time(567,567) Distance:2191 Passenger_Load:1, Goods_Load:0 -> 22 Time(716,716) Distance:2342 Passenger_Load:0, Goods_Load:0 -> 2 Time(1194,1194) Distance:7574 Passenger_Load:1, Goods_Load:0 -> 38 Time(1269,1269) Distance:8333 Passenger_Load:0, Goods_Load:0 -> 18 Time(1597,1597) Distance:8700 Passenger_Load:1, Goods_Load:0 -> 21 Time(1694,1694) Distance:9679 Passenger_Load:0, Goods_Load:0 -> 1 Time(1977,1977) Distance:12542 Passenger_Load:1, Goods_Load:0 -> 0 Time(2054,2054) Distance:13515 Passenger_Load:0, Goods_Load:0)
Time of the route: 2054sec
Distance of the route: 13515m
Load of the passenger: 0
Load of the goods: 0

Route for vehicle 4:
0 Time(0,0) Distance:0 Passenger_Load:0, Goods_Load:0 -> 26 Time(274,274) Distance:3603 Passenger_Load:0, Goods_Load:0 -> 6 Time(837,837) Distance:10379 Passenger_Load:1, Goods_Load:0 -> 0 Time(1126,1126) Distance:13552 Passenger_Load:0, Goods_Load:0)
Time of the route: 1126sec
Distance of the route: 13552m
Load of the passenger: 0
Load of the goods: 0

Route for vehicle 5:
0 Time(0,0) Distance:0 Passenger_Load:0, Goods_Load:0 -> 29 Time(227,227) Distance:2825 Passenger_Load:0, Goods_Load:0 -> 9 Time(555,555) Distance:6771 Passenger_Load:1, Goods_Load:0 -> 27 Time(578,578) Distance:7006 Passenger_Load:0, Goods_Load:0 -> 7 Time(1213,1213) Distance:7797 Passenger_Load:1, Goods_Load:0 -> 34 Time(1760,1760) Distance:8391 Passenger_Load:0, Goods_Load:0 -> 14 Time(2041,2041) Distance:11526 Passenger_Load:1, Goods_Load:0 -> 0 Time(2149,2149) Distance:12747 Passenger_Load:0, Goods_Load:0)
Time of the route: 2149sec
Distance of the route: 12747m
Load of the passenger: 0
Load of the goods: 0

Route for vehicle 6:
0 Time(0,0) Distance:0 Passenger_Load:0, Goods_Load:0 -> 28 Time(268,268) Distance:3234 Passenger_Load:0, Goods_Load:0 -> 8 Time(395,395) Distance:4517 Passenger_Load:1, Goods_Load:0 -> 24 Time(925,925) Distance:5132 Passenger_Load:0, Goods_Load:0 -> 4 Time(1590,1590) Distance:13210 Passenger_Load:1, Goods_Load:0 -> 52 Time(1864,1864) Distance:13488 Passenger_Load:0, Goods_Load:0 -> 46 Time(1948,1948) Distance:13497 Passenger_Load:0, Goods_Load:1 -> 0 Time(1977,1977) Distance:13872 Passenger_Load:0, Goods_Load:0)
Time of the route: 1977sec
Distance of the route: 13872m
Load of the passenger: 0
Load of the goods: 0

Total time of all routes: 12783sec
Total distance of all routes: 79954m
Total load of all routes_passenger: 0
Total load of all routes_goods: 0
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