Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem
Table 3
Comparison of solution objective values for the optimal solution, obtained by GLS, TS, and SA, using the Clarke and Wright's algorithm and the nearest neighbour algorithm.
Ins.
Clarke and Wright’s algorithm
Nearest neighbour algorithm
#Veh
#Cont.
Distance
Time
Veh. load
Routes
Obj
Cpt
% gap (%)
Distance
Time
Veh. load
Routes
Obj
Cpt
% gap (%)
GLS
3
5
125.58
73.58
16.10
1.00
77.83
0.19
0.0
125.58
73.58
16.10
1.00
77.83
0.26
0.0
5
10
196.98
82.15
29.51
1.00
87.95
0.24
0.0
196.98
82.15
29.51
1.00
87.95
0.28
0.0
5
15
199.61
85.31
43.06
1.00
92.11
0.28
0.0
199.61
85.31
43.06
1.00
92.11
0.44
0.0
6
10
229.11
82.15
29.51
1.00
87.95
0.42
0.0
229.11
82.15
29.51
1.00
87.95
0.30
0.0
9
15
328.13
85.31
43.06
1.00
92.11
0.63
0.0
328.13
85.31
43.06
1.00
92.11
0.34
0.0
9
20
330.82
88.54
50.77
1.00
95.57
1.03
0.0
330.82
88.54
50.77
1.00
95.57
0.42
0.0
10
25
386.49
116.79
66.51
1.00
124.25
0.93
0.0
386.49
116.79
66.51
1.00
124.25
0.57
0.0
10
35
385.69
154.38
85.08
2.00
162.37
1.72
0.0
385.69
154.38
85.08
2.00
162.37
0.91
0.0
15
50
566.07
178.05
117.55
2.00
186.53
2.04
0.0
566.07
178.05
117.55
2.00
186.53
1.81
0.0
20
60
740.06
194.07
139.19
2.00
202.68
42.52
0.0
740.06
194.07
139.19
2.00
202.68
32.03
0.0
30
60
1061.36
194.07
139.19
2.00
202.67
21.41
0.0
1061.36
194.07
139.19
2.00
202.67
21.00
0.0
30
70
1073.77
208.96
163.67
2.00
217.66
1751.72
0.0
1073.77
208.96
163.67
2.00
217.66
1807.45
0.0
35
75
1235.82
210.64
173.56
2.00
219.50
6528.07
0.0
1235.82
210.64
173.56
2.00
219.50
3962.95
0.0
TS
3
5
125.58
73.58
16.10
1.00
77.83
0.36
0.0
125.58
73.58
16.10
1.00
77.83
0.28
0.0
5
10
196.98
82.15
29.51
1.00
87.95
0.36
0.0
196.98
82.15
29.51
1.00
87.95
0.27
0.0
5
15
199.61
85.31
43.06
1.00
92.11
0.54
0.0
199.61
85.31
43.06
1.00
92.11
0.29
0.0
6
10
229.11
82.15
29.51
1.00
87.95
0.53
0.0
229.11
82.15
29.51
1.00
87.95
0.28
0.0
9
15
328.13
85.31
43.06
1.00
92.11
0.60
0.0
328.13
85.31
43.06
1.00
92.11
0.38
0.0
9
20
330.82
88.54
50.77
1.00
95.57
0.66
0.0
330.82
88.54
50.77
1.00
95.57
0.41
0.0
10
25
386.49
116.79
66.51
1.00
124.25
1.03
0.0
386.49
116.79
66.51
1.00
124.25
0.58
0.0
10
35
385.69
154.38
85.08
2.00
162.37
1.46
0.0
385.69
154.38
85.08
2.00
162.37
0.96
0.0
15
50
566.07
178.05
117.55
2.00
186.53
1.89
0.0
566.07
178.05
117.55
2.00
186.53
1.89
0.0
20
60
740.06
194.07
139.19
2.00
202.68
41.06
0.0
740.06
194.07
139.19
2.00
202.68
35.63
0.0
30
60
1061.36
194.07
139.19
2.00
202.67
21.24
0.0
1061.36
194.07
139.19
2.00
202.67
21.15
0.0
30
70
1073.77
208.96
163.67
2.00
217.66
1430.72
0.0
1073.77
208.96
163.67
2.00
217.66
1573.23
0.0
35
75
1235.82
210.64
173.56
2.00
219.50
4011.22
0.0
1235.82
210.64
173.56
2.00
219.50
4220.71
0.0
SA
3
5
125.58
73.58
16.10
1.00
77.83
0.37
0.0
125.58
73.58
16.10
1.00
77.83
0.24
0.0
5
10
196.98
82.15
29.51
1.00
87.95
0.34
0.0
196.98
82.15
29.51
1.00
87.95
0.24
0.0
5
15
199.61
85.31
43.06
1.00
92.11
0.41
0.0
199.61
85.31
43.06
1.00
92.11
0.30
0.0
6
10
229.11
82.15
29.51
1.00
87.95
0.33
0.0
229.11
82.15
29.51
1.00
87.95
0.25
0.0
9
15
328.13
85.31
43.06
1.00
92.11
0.34
0.0
328.13
85.31
43.06
1.00
92.11
0.35
0.0
9
20
330.82
88.54
50.77
1.00
95.57
0.37
0.0
330.82
88.54
50.77
1.00
95.57
0.36
0.0
10
25
386.49
116.79
66.51
1.00
124.25
0.63
0.0
386.49
116.79
66.51
1.00
124.25
0.54
0.0
10
35
385.69
154.38
85.08
2.00
162.37
0.93
0.0
385.69
154.38
85.08
2.00
162.37
0.99
0.0
15
50
566.07
178.05
117.55
2.00
186.53
1.87
0.0
566.07
178.05
117.55
2.00
186.53
1.82
0.0
20
60
740.06
194.07
139.19
2.00
202.68
34.11
0.0
740.06
194.07
139.19
2.00
202.68
35.59
0.0
30
60
1061.36
194.07
139.19
2.00
202.67
21.48
0.0
1061.36
194.07
139.19
2.00
202.67
21.06
0.0
30
70
1073.77
208.96
163.67
2.00
217.66
1386.93
0.0
1073.77
208.96
163.67
2.00
217.66
1226.45
0.0
35
75
1235.82
210.64
173.56
2.00
219.50
4759.30
0.0
1235.82
210.64
173.56
2.00
219.50
7195.00
0.0
The best computational times are highlighted in bold.
