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A Compound Optimization Greedy Strategy with Reverse Correction Mechanism Cover

A Compound Optimization Greedy Strategy with Reverse Correction Mechanism

By: Han Shen and  Zhongsheng Wang  
Open Access
|May 2023

Figures & Tables

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

DRMN-OSimpleOptimizationSR/%
0%52212902174301.7443
102192052166051.1861
152185652162251.0706
202182602160701.0034
252180002159700.9312
302178952160250.8582
SR-average/%1.1323
10%54087804051200.8953
104048154023200.6163
154036804013650.5735
204031054006650.6053
254027554003650.5934
304025353996250.7229
SR-average/%0.6678
20%56187106110151.2437
106119706061500.9510
156097106043000.8873
206089756033350.9261
256082306027800.8960
306074656024400.8272
SR-average/%0.9552
30%58269508165901.2528
108170458102750.8286
158139808078000.7592
208123308069200.6660
258113058059550.6594
308106608052600.6661
SR-average/%0.8054
40%5103077510191501.1278
10101804010114850.6439
15101443010088250.5525
20101281510076200.5129
25101150010064200.5022
30101106010059250.5079
SR-average/%0.6412
SR-average-total/%0.8404

Test of parameter change of preferential scheme

SchemeContainer BoatsOperator HandsSimpleOptimizationSR/%
1f(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.$4031054006650.6053
2f(x)={ 200x,x5100+180x,5<x10300+160x,10<x15600+140x,x>15$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.$3970553899301.7945
3f(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.$3942653795803.7247
4f(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.$4142254133550.2100
5f(x) = 200xg(x) = 100x4342954336150.1566
6f(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.$4187004178800.1958
7f(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.$4261934255780.1443
8f(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.$3760653658352.7203
9f(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.$3490253290505.7231
SR-average %1.6972

Number of vascular robots used in Weeks 1-104

Week 1-81154716657
Week 9-161365712546
Week 17-249551129211720
Week 25-32271391016657
Week 33-4011556127710
Week 41-48151091115101016
Week 49-562621233650454549
Week 57-645743404452434245
Week 65-725241394148353435
Week 73-804234364355485465
Week 81-8880707485101898890
Week 89-96100878889104898990
Week 97-1041069694991099996102

The output of the simple greedy strategy model

The output of the simple greedy strategy model
The purchase program of Container boat
Week 1-800050001
Week 9-1600020000
Week 17-24002190007
Week 25-3200000000
Week 33-4000000004
Week 41-48000500512
Week 49-56021518041813
Week 57-640011200611
Week 65-720021100010
Week 73-800111160101621
Week 81-88091824061119
Week 89-96071024041025
Week 97-10418142017160
Amount484Cost91600
The purchase program of operator hands
Week 1-81400360001
Week 9-1600080000
Week 17-24001398440010
Week 25-3200000000
Week 33-4000000000
Week 41-480000001868
Week 49-56260681175003266
Week 57-6400036100957
Week 65-722003400016
Week 73-801004789371989126
Week 81-884111901384503183
Week 89-96240359936023104
Week 97-1046004398400390
Amount2290Cost311505
Total Cost403105

Original Purchase Parameter Settings

The number of container boats
Week. N2715
Week. N+s271527152715
The number of operator hands
Week. N103050
Week. N+s103050103050103050

Comparison of the Effect Before and After Optimization

OP-NOP-CCB-NCB-CTOTAL
Simple229031150548491600403105
Optimization 1229031117548491600402775
Optimization 2229031052548490140400665
SR total0.6053%  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 1-800050001
Week 9-1600020000
Week 17-24002190007
Week 25-3200000000
Week 33-4000000004
Week 41-48000500512
Week 49-5602151804813
Week 57-640011200611
Week 65-720021100010
Week 73-800111160101621
Week 81-88091824061119
Week 89-96071024041025
Week 97-10418142017160
Amount484Cost91600
The purchase program of operator hands
Week 1-81400360001
Week 9-1600080000
Week 17-24001396410015
Week 25-3200000000
Week 33-4000000000
Week 41-480000001866
Week 49-56251701144813662
Week 57-6400037130955
Week 65-721003700016
Week 73-80904887372189122
Week 81-884412901344703380
Week 89-96220409634028101
Week 97-1045704995380440
Amount2290Cost311175
Total Cost402775

The output of the second optimization

The output of the second optimization
The purchase program of Container boats
Week 1-800050001
Week 9-1600020000
Week 17-24002190007
Week 25-3200000000
Week 33-4000000004
Week 41-48000500512
Week 49-5602151804813
Week 57-640011200611
Week 65-720021100010
Week 73-800111160101621
Week 81-88091824061119
Week 89-96071024041025
Week 97-10418142017160
Amount484Cost90140
The purchase program of operator hands
Week 1-81400360001
Week 9-1600080000
Week 17-24001396410015
Week 25-3200000000
Week 33-4000000000
Week 41-480000001866
Week 49-56251701144813662
Week 57-6400037130955
Week 65-721003700016
Week 73-80904887372189122
Week 81-884412901344703380
Week 89-96220409634028101
Week 97-1045704995380440
Amount2290Cost310525
Total Cost400665

Retest results of changing global parameters

OP-NOP-CCB-NCB-CTOTAL
Simple4101383245939146280529525
Optimization 14080381415939146280527695
Optimization 24080368045939137530505575
  SR total   4.5229%    SR 1   0.3456%    SR 2   4.1918%  
Language: English
Page range: 18 - 39
Published on: May 31, 2023
Published by: Xi’an Technological University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Han Shen, Zhongsheng Wang, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.