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A Compound Optimization Greedy Strategy with Reverse Correction Mechanism


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Figure. 1.

Data update flow chart of Container Boats
Data update flow chart of Container Boats

Figure. 2.

Data update flow chart of the Operator Hands
Data update flow chart of the Operator Hands

Figure. 3.

The purchase and idle situation of the Operator Hands in the Simple Mode
The purchase and idle situation of the Operator Hands in the Simple Mode

Figure. 4.

Update flow chart of the Operator Hands after the first optimization
Update flow chart of the Operator Hands after the first optimization

Figure. 5.

The purchase and idle situation of the Operator Hands in the First Optimization Mode
The purchase and idle situation of the Operator Hands in the First Optimization Mode

Figure. 6.

Comparison of purchasing plans of Operator Hands before and after the first optimization
Comparison of purchasing plans of Operator Hands before and after the first optimization

Figure. 7.

Simulation of the combined purchase cost of Container Boats
Simulation of the combined purchase cost of Container Boats

Figure. 8.

Simulation of the combined purchase cost of Operator Hands
Simulation of the combined purchase cost of Operator Hands

Figure. 9.

Flow chart of merge purchase algorithm
Flow chart of merge purchase algorithm

Figure. 10.

Flowchart of the rollback algorithm
Flowchart of the rollback algorithm

Figure. 11.

The purchase of Operator Hands before and after optimization
The purchase of Operator Hands before and after optimization

Figure. 12.

The purchase of Container Boats before and after optimization
The purchase of Container Boats before and after optimization

Figure. 13.

The relationship between the MN-O and the SR with different DR
The relationship between the MN-O and the SR with different DR

Figure. 14.

The relationship between the DR and the SR with different MN-O
The relationship between the DR and the SR with different MN-O

Figure. 15.

Number of discount segments and SR
Number of discount segments and SR

Figure. 16.

Relationship between discount intensity and SR
Relationship between discount intensity and SR

Figure. 17.

Test results after changing the requirement matrix
Test results after changing the requirement matrix

Test results of loss parameter changes

DR MN-O Simple Optimization SR/%
0% 5 221290 217430 1.7443
10 219205 216605 1.1861
15 218565 216225 1.0706
20 218260 216070 1.0034
25 218000 215970 0.9312
30 217895 216025 0.8582
SR-average/% 1.1323
10% 5 408780 405120 0.8953
10 404815 402320 0.6163
15 403680 401365 0.5735
20 403105 400665 0.6053
25 402755 400365 0.5934
30 402535 399625 0.7229
SR-average/% 0.6678
20% 5 618710 611015 1.2437
10 611970 606150 0.9510
15 609710 604300 0.8873
20 608975 603335 0.9261
25 608230 602780 0.8960
30 607465 602440 0.8272
SR-average/% 0.9552
30% 5 826950 816590 1.2528
10 817045 810275 0.8286
15 813980 807800 0.7592
20 812330 806920 0.6660
25 811305 805955 0.6594
30 810660 805260 0.6661
SR-average/% 0.8054
40% 5 1030775 1019150 1.1278
10 1018040 1011485 0.6439
15 1014430 1008825 0.5525
20 1012815 1007620 0.5129
25 1011500 1006420 0.5022
30 1011060 1005925 0.5079
SR-average/% 0.6412
SR-average-total/% 0.8404

Test of parameter change of preferential scheme

Scheme Container Boats Operator Hands Simple Optimization SR/%
1 f(x)={ 200x,x5100+180x,5<x10300+160x,x>10 $$f(x) = \left\{ {\matrix{ {200x,} & {x \le 5} \cr {100 + 180x,} & {5 < x \le 10} \cr {300 + 160x,} & {x > 10} \cr } } \right.$$ g(x)={ 100x,x20200+90x,20<x40600+80x,x>40 $$g(x) = \left\{ {\matrix{ {100x,} & {x \le 20} \cr {200 + 90x,} & {20 < x \le 40} \cr {600 + 80x,} & {x > 40} \cr } } \right.$$ 403105 400665 0.6053
2 f(x)={ 200x,x5100+180x,5<x10300+160x,10<x15600+140x,x>15$$f(x) = \left\{ {\matrix{ {200x,} & {x \le 5} \cr {100 + 180x,} & {5 < x \le 10} \cr {300 + 160x,} & {10 < x \le 15} \cr {600 + 140x,} & {x > 15} \cr } } \right.$$ g(x)={ 100x,x20200+90x,20<x40600+80x,40<x601200+70x,x>60$$g(x) = \left\{ {\matrix{ {100x,} & {x \le 20} \cr {200 + 90x,} & {20 < x \le 40} \cr {600 + 80x,} & {40 < x \le 60} \cr {1200 + 70x,} & {x > 60} \cr } } \right.$$ 397055 389930 1.7945
3 f(x)={ 200x,x5100+180x,5<x10300+160x,10<x15600+140x,15<x201000+120xx>20 $$f(x) = \left\{ {\matrix{ {200x,} & {x \le 5} \cr {100 + 180x,} & {5 < x \le 10} \cr {300 + 160x,} & {10 < x \le 15} \cr {600 + 140x,} & {15 < x \le 20} \cr {1000 + 120x} & {x > 20} \cr } } \right.$$ g(x)={ 100x,x20200+90x,20<x40600+80x,40<x601200+70x,60<x802000+60xx>80 $$g(x) = \left\{ {\matrix{ {100x,} & {x \le 20} \cr {200 + 90x,} & {20 < x \le 40} \cr {600 + 80x,} & {40 < x \le 60} \cr {1200 + 70x,} & {60 < x \le 80} \cr {2000 + 60x} & {x > 80} \cr } } \right.$$ 394265 379580 3.7247
4 f(x)={ 200x,x5100+180x,5<x $$f(x) = \left\{ {\matrix{ {200x,} & {x \le 5} \cr {100 + 180x,} & {5 < x} \cr } } \right.$$ g(x)={ 100x,x20200+90x,20<x$$g(x) = \left\{ {\matrix{ {100x,} & {x \le 20} \cr {200 + 90x,} & {20 < x} \cr } } \right.$$ 414225 413355 0.2100
5 f(x) = 200x g(x) = 100x 434295 433615 0.1566
6 f(x)={ 200x,x550+190x,5<x10150+180x,x>10 $$f(x) = \left\{ {\matrix{ {200x,} & {x \le 5} \cr {50 + 190x,} & {5 < x \le 10} \cr {150 + 180x,} & {x > 10} \cr } } \right.$$ g(x)={ 100x,x20100+95x,20<x40300+90x,x>40 $$g(x) = \left\{ {\matrix{ {100x,} & {x \le 20} \cr {100 + 95x,} & {20 < x \le 40} \cr {300 + 90x,} & {x > 40} \cr } } \right.$$ 418700 417880 0.1958
7 f(x)={ 200x,x525+195x,5<x1075+190x,x>10 $$f(x) = \left\{ {\matrix{ {200x,} & {x \le 5} \cr {25 + 195x,} & {5 < x \le 10} \cr {75 + 190x,} & {x > 10} \cr } } \right.$$ g(x)={ 100x,x2060+97x,20<x40140+95x,x>40 $$g(x) = \left\{ {\matrix{ {100x,} & {x \le 20} \cr {60 + 97x,} & {20 < x \le 40} \cr {140 + 95x,} & {x > 40} \cr } } \right.$$ 426193 425578 0.1443
8 f(x)={ 200x,x5150+170x,5<x10450+140x,x>10 $$f(x) = \left\{ {\matrix{ {200x,} & {x \le 5} \cr {150 + 170x,} & {5 < x \le 10} \cr {450 + 140x,} & {x > 10} \cr } } \right.$$ g(x)={ 100x,x20400+80x,20<x401200+60x,x>40 $$g(x) = \left\{ {\matrix{ {100x,} & {x \le 20} \cr {400 + 80x,} & {20 < x \le 40} \cr {1200 + 60x,} & {x > 40} \cr } } \right.$$ 376065 365835 2.7203
9 f(x)={ 200x,x5200+160x,5<x10600+120x,x>10 $$f(x) = \left\{ {\matrix{ {200x,} & {x \le 5} \cr {200 + 160x,} & {5 < x \le 10} \cr {600 + 120x,} & {x > 10} \cr } } \right.$$ g(x)={ 100x,x20600+70x,20<x401800+40x,x>40 $$g(x) = \left\{ {\matrix{ {100x,} & {x \le 20} \cr {600 + 70x,} & {20 < x \le 40} \cr {1800 + 40x,} & {x > 40} \cr } } \right.$$ 349025 329050 5.7231
SR-average % 1.6972

Number of vascular robots used in Weeks 1-104

Week 1-8 11 5 4 7 16 6 5 7
Week 9-16 13 6 5 7 12 5 4 6
Week 17-24 9 5 5 11 29 21 17 20
Week 25-32 27 13 9 10 16 6 5 7
Week 33-40 11 5 5 6 12 7 7 10
Week 41-48 15 10 9 11 15 10 10 16
Week 49-56 26 21 23 36 50 45 45 49
Week 57-64 57 43 40 44 52 43 42 45
Week 65-72 52 41 39 41 48 35 34 35
Week 73-80 42 34 36 43 55 48 54 65
Week 81-88 80 70 74 85 101 89 88 90
Week 89-96 100 87 88 89 104 89 89 90
Week 97-104 106 96 94 99 109 99 96 102

The output of the simple greedy strategy model

The output of the simple greedy strategy model
The purchase program of Container boat
Week 1-8 0 0 0 5 0 0 0 1
Week 9-16 0 0 0 2 0 0 0 0
Week 17-24 0 0 2 19 0 0 0 7
Week 25-32 0 0 0 0 0 0 0 0
Week 33-40 0 0 0 0 0 0 0 4
Week 41-48 0 0 0 5 0 0 5 12
Week 49-56 0 2 15 18 0 4 18 13
Week 57-64 0 0 1 12 0 0 6 11
Week 65-72 0 0 2 11 0 0 0 10
Week 73-80 0 1 11 16 0 10 16 21
Week 81-88 0 9 18 24 0 6 11 19
Week 89-96 0 7 10 24 0 4 10 25
Week 97-104 1 8 14 20 1 7 16 0
Amount 484 Cost 91600
The purchase program of operator hands
Week 1-8 14 0 0 36 0 0 0 1
Week 9-16 0 0 0 8 0 0 0 0
Week 17-24 0 0 13 98 44 0 0 10
Week 25-32 0 0 0 0 0 0 0 0
Week 33-40 0 0 0 0 0 0 0 0
Week 41-48 0 0 0 0 0 0 18 68
Week 49-56 26 0 68 117 50 0 32 66
Week 57-64 0 0 0 36 10 0 9 57
Week 65-72 2 0 0 34 0 0 0 16
Week 73-80 10 0 47 89 37 19 89 126
Week 81-88 41 11 90 138 45 0 31 83
Week 89-96 24 0 35 99 36 0 23 104
Week 97-104 60 0 43 98 40 0 39 0
Amount 2290 Cost 311505
Total Cost 403105

Original Purchase Parameter Settings

The number of container boats
Week. N 2 7 15
Week. N+s 2 7 15 2 7 15 2 7 15
The number of operator hands
Week. N 10 30 50
Week. N+s 10 30 50 10 30 50 10 30 50

Comparison of the Effect Before and After Optimization

OP-N OP-C CB-N CB-C TOTAL
Simple 2290 311505 484 91600 403105
Optimization 1 2290 311175 484 91600 402775
Optimization 2 2290 310525 484 90140 400665
SR total 0.6053%   SR 1   0.08187%     SR 2   0.5239%  

The output of the first optimization

The output of the first optimization
The purchase program of Container boat
Week 1-8 0 0 0 5 0 0 0 1
Week 9-16 0 0 0 2 0 0 0 0
Week 17-24 0 0 2 19 0 0 0 7
Week 25-32 0 0 0 0 0 0 0 0
Week 33-40 0 0 0 0 0 0 0 4
Week 41-48 0 0 0 5 0 0 5 12
Week 49-56 0 2 15 18 0 4 8 13
Week 57-64 0 0 1 12 0 0 6 11
Week 65-72 0 0 2 11 0 0 0 10
Week 73-80 0 1 11 16 0 10 16 21
Week 81-88 0 9 18 24 0 6 11 19
Week 89-96 0 7 10 24 0 4 10 25
Week 97-104 1 8 14 20 1 7 16 0
Amount 484 Cost 91600
The purchase program of operator hands
Week 1-8 14 0 0 36 0 0 0 1
Week 9-16 0 0 0 8 0 0 0 0
Week 17-24 0 0 13 96 41 0 0 15
Week 25-32 0 0 0 0 0 0 0 0
Week 33-40 0 0 0 0 0 0 0 0
Week 41-48 0 0 0 0 0 0 18 66
Week 49-56 25 1 70 114 48 1 36 62
Week 57-64 0 0 0 37 13 0 9 55
Week 65-72 1 0 0 37 0 0 0 16
Week 73-80 9 0 48 87 37 21 89 122
Week 81-88 44 12 90 134 47 0 33 80
Week 89-96 22 0 40 96 34 0 28 101
Week 97-104 57 0 49 95 38 0 44 0
Amount 2290 Cost 311175
Total Cost 402775

The output of the second optimization

The output of the second optimization
The purchase program of Container boats
Week 1-8 0 0 0 5 0 0 0 1
Week 9-16 0 0 0 2 0 0 0 0
Week 17-24 0 0 2 19 0 0 0 7
Week 25-32 0 0 0 0 0 0 0 0
Week 33-40 0 0 0 0 0 0 0 4
Week 41-48 0 0 0 5 0 0 5 12
Week 49-56 0 2 15 18 0 4 8 13
Week 57-64 0 0 1 12 0 0 6 11
Week 65-72 0 0 2 11 0 0 0 10
Week 73-80 0 1 11 16 0 10 16 21
Week 81-88 0 9 18 24 0 6 11 19
Week 89-96 0 7 10 24 0 4 10 25
Week 97-104 1 8 14 20 1 7 16 0
Amount 484 Cost 90140
The purchase program of operator hands
Week 1-8 14 0 0 36 0 0 0 1
Week 9-16 0 0 0 8 0 0 0 0
Week 17-24 0 0 13 96 41 0 0 15
Week 25-32 0 0 0 0 0 0 0 0
Week 33-40 0 0 0 0 0 0 0 0
Week 41-48 0 0 0 0 0 0 18 66
Week 49-56 25 1 70 114 48 1 36 62
Week 57-64 0 0 0 37 13 0 9 55
Week 65-72 1 0 0 37 0 0 0 16
Week 73-80 9 0 48 87 37 21 89 122
Week 81-88 44 12 90 134 47 0 33 80
Week 89-96 22 0 40 96 34 0 28 101
Week 97-104 57 0 49 95 38 0 44 0
Amount 2290 Cost 310525
Total Cost 400665

Retest results of changing global parameters

OP-N OP-C CB-N CB-C TOTAL
Simple 4101 383245 939 146280 529525
Optimization 1 4080 381415 939 146280 527695
Optimization 2 4080 368045 939 137530 505575
  SR total   4.5229%     SR 1   0.3456%     SR 2   4.1918%  
eISSN:
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Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, other