Multiobjective Quantum Evolutionary Algorithm for the Vehicle Routing Problem with Customer Satisfaction

Table 4

The distance and demand of each client.

CN	Coordinate/km		ST	De/t	TW
1	−29.730	64.136	2	12	[399525]
2	−30.664	5.463	7	8	[121299]
3	51.642	5.469	21	16	[389483]
4	−13.171	69.336	24	5	[204304]
5	−67.413	68.323	1	12	[317458]
6	48.907	6.274	17	5	[160257]
7	5.243	22.260	6	13	[170287]
8	−65.002	77.234	5	20	[215321]
9	−4.175	−1.569	7	13	[80233]
10	23.029	11.639	1	18	[90206]
11	25.482	6.287	4	7	[397525]
12	−42.615	−26.392	10	6	[271 420]
13	−76.672	99.341	2	9	[108 266]
14	−20.673	57.892	16	9	[340 462]
15	−52.039	6.567	23	4	[226 377]
16	−41.376	50.824	18	25	[446 604]
17	−91.943	27.588	3	5	[444 566]
18	−65.118	30.212	15	17	[434 557]
19	18.597	96.716	13	3	[319 460]
20	−40.942	83.209	10	16	[192 312]
21	−37.756	−33.325	4	25	[414 572]
22	23.767	29.083	23	21	[371 462]
23	−43.030	20.453	20	14	[378 472]
24	−35.297	−24.896	10	19	[308 477]
25	−54.755	14.368	4	14	[329 444]
26	−49.329	33.374	2	6	[269 377]
27	57.404	23.822	23	16	[398 494]
28	−22.754	55.408	6	9	[257 416]
29	−56.622	73.340	8	20	[198 294]
30	−38.562	−3.705	10	13	[375 467]
31	−16.779	19.537	7	10	[200 338]
32	−11.560	11.615	1	16	[456 632]
33	−46.545	97.974	21	19	[72  179]
34	16.229	9.320	6	22	[182 282]
35	1.294	7.349	4	14	[159 306]
36	−26.404	29.529	13	10	[321 500]
37	4.352	14.685	9	11	[322 430]
38	−50.665	−23.126	22	15	[443 564]
39	−22.833	−9.814	22	13	[207 348]
40	−71.100	−18.616	18	15	[457 588]
41	−7.849	32.074	10	8	[203 382]
42	11.877	−24.933	25	22	[75 167]
43	−18.927	−23.730	23	24	[459 598]
44	−11.920	11.755	4	3	[174 332]
45	29.840	11.633	9	25	[130 225]
46	12.268	−55.811	17	19	[169 283]
47	−37.933	−21.613	10	21	[115 232]
48	42.883	−2.966	17	10	[414 531]