A Bee Evolutionary Algorithm for Multiobjective Vehicle Routing Problem with Simultaneous Pickup and Delivery
Table 4
Comparisons of the optimization results of test example 1 with 12 customers.
Algorithms
Vehicle path
Fuel consumption/kg
Waiting time/min
Distance/km
Average computing time/min
T-NSGA-II
0-3-1-8-10-0 0-4-12-5-7-0 0-2-11-6-9-0
546.39
0.74
489.70
1.2
0-3-1-8-10-0 0-4-12-5-7-0 0-11-2-6-9-0
548.06
0.74
481.77
0-3-8-1-10-0 0-4-12-5-7-0 0-2-11-6-9-0
546.33
31.28
493.11
0-3-8-1-10-0 0-4-12-5-7-0 0-11-2-6-9-0
548.00
31.28
485.18
0-3-10-1-8-0 0-4-12-5-7-0 0-2-11-6-9-0
539.77
50.13
475.41
W-BEG-NSGA-II
0-4-5-7-10-0 0-2-6-11-1-8-0 0-3-9-12-0
512.69
95.5
476.94
0.9
0-4-5-7-10-0 0-6-2-11-1-8-0 0-9-3-12-0
506.21
147.4
458.14
0-4-5-7-10-0 0-6-2-11-1-8-0 0-3-9-12-0
525.85
73.8
488.82
0-4-5-7-10-0 0-6-2-11-1-8-0 0-9-3-12-0
519.36
125.6
470.02
0-5-7-4-10-0 0-2-6-11-1-8-0 0-9-3-12-0
519.20
180.9
452.91
BEG-NSGA-II
0-2-6-11-1-8-0 0-4-12-5-7-0 0-3-9-10-0
418.99
95.5
371.69
0.5
0-6-2-11-1-8-0 0-4-12-5-7-0 0-3-9-10-0
432.14
73.7
383.57
0-6-2-1-8-11-0 0-4-12-5-7-0 0-3-9-10-0
449.21
63.0
409.26
0-6-2-1-9-10-0 0-4-12-5-7-0 0-3-8-1-11-0
475.03
31.3
444.04
0-6-2-1-10-9-0 0-4-12-5-7-0 0-3-8-1-11-0
477.91
31.3
441.32
0-1-6-2-11-8-0 0-4-12-5-7-0 0-3-9-10-0
479.98
0
427.85
0-1-2-6-11-8-0 0-4-12-5-7-0 0-3-9-10-0
484.08
0
424.94
Note: (a) “”meant that the marked result dominates the results of T-NSGA-II. (b) “” meant that the marked result dominates the results of W-BEG-NSGA-II. (c) “” meant that the marked result dominates the results of T-NSGA-II and W-BEG-NSGA-II simultaneously.
