A Compound Optimization Greedy Strategy with Reverse Correction Mechanism

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

Data update flow chart of Container Boats

Data update flow chart of the Operator Hands

TABLE II.

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

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

Update flow chart of the Operator Hands after the first optimization

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

TABLE III.

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

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

TABLE IV.

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

Simulation of the combined purchase cost of Container Boats

Simulation of the combined purchase cost of Operator Hands

The purchase of Operator Hands before and after optimization

The purchase of Container Boats before and after optimization

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

TABLE V.

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

TABLE VI.

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%

TABLE VII.

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

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

TABLE VIII.

Test of parameter change of preferential scheme

Scheme	Container Boats	Operator Hands	Simple	Optimization	SR/%
1	$f (x) = {\begin{array}{l} 200 x, & x \leq 5 \\ 100 + 180 x, & 5 < x \leq 10 \\ 300 + 160 x, & x > 10 \end{array}$	$g (x) = {\begin{array}{l} 100 x, & x \leq 20 \\ 200 + 90 x, & 20 < x \leq 40 \\ 600 + 80 x, & x > 40 \end{array}$	403105	400665	0.6053
2	$f (x) = {\begin{array}{l} 200 x, & x \leq 5 \\ 100 + 180 x, & 5 < x \leq 10 \\ 300 + 160 x, & 10 < x \leq 15 \\ 600 + 140 x, & x > 15 \end{array}$	$g (x) = {\begin{array}{l} 100 x, & x \leq 20 \\ 200 + 90 x, & 20 < x \leq 40 \\ 600 + 80 x, & 40 < x \leq 60 \\ 1200 + 70 x, & x > 60 \end{array}$	397055	389930	1.7945
3	$f (x) = {\begin{array}{l} 200 x, & x \leq 5 \\ 100 + 180 x, & 5 < x \leq 10 \\ 300 + 160 x, & 10 < x \leq 15 \\ 600 + 140 x, & 15 < x \leq 20 \\ 1000 + 120 x & x > 20 \end{array}$	$g (x) = {\begin{array}{l} 100 x, & x \leq 20 \\ 200 + 90 x, & 20 < x \leq 40 \\ 600 + 80 x, & 40 < x \leq 60 \\ 1200 + 70 x, & 60 < x \leq 80 \\ 2000 + 60 x & x > 80 \end{array}$	394265	379580	3.7247
4	$f (x) = {\begin{array}{l} 200 x, & x \leq 5 \\ 100 + 180 x, & 5 < x \end{array}$	$g (x) = {\begin{array}{l} 100 x, & x \leq 20 \\ 200 + 90 x, & 20 < x \end{array}$	414225	413355	0.2100
5	f(x) = 200x	g(x) = 100x	434295	433615	0.1566
6	$f (x) = {\begin{array}{l} 200 x, & x \leq 5 \\ 50 + 190 x, & 5 < x \leq 10 \\ 150 + 180 x, & x > 10 \end{array}$	$g (x) = {\begin{array}{l} 100 x, & x \leq 20 \\ 100 + 95 x, & 20 < x \leq 40 \\ 300 + 90 x, & x > 40 \end{array}$	418700	417880	0.1958
7	$f (x) = {\begin{array}{l} 200 x, & x \leq 5 \\ 25 + 195 x, & 5 < x \leq 10 \\ 75 + 190 x, & x > 10 \end{array}$	$g (x) = {\begin{array}{l} 100 x, & x \leq 20 \\ 60 + 97 x, & 20 < x \leq 40 \\ 140 + 95 x, & x > 40 \end{array}$	426193	425578	0.1443
8	$f (x) = {\begin{array}{l} 200 x, & x \leq 5 \\ 150 + 170 x, & 5 < x \leq 10 \\ 450 + 140 x, & x > 10 \end{array}$	$g (x) = {\begin{array}{l} 100 x, & x \leq 20 \\ 400 + 80 x, & 20 < x \leq 40 \\ 1200 + 60 x, & x > 40 \end{array}$	376065	365835	2.7203
9	$f (x) = {\begin{array}{l} 200 x, & x \leq 5 \\ 200 + 160 x, & 5 < x \leq 10 \\ 600 + 120 x, & x > 10 \end{array}$	$g (x) = {\begin{array}{l} 100 x, & x \leq 20 \\ 600 + 70 x, & 20 < x \leq 40 \\ 1800 + 40 x, & x > 40 \end{array}$	349025	329050	5.7231
SR-average %			1.6972

Relationship between discount intensity and SR

Test results after changing the requirement matrix

TABLE IX.

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%

References

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DOI: https://doi.org/10.2478/ijanmc-2023-0043 | Journal eISSN: 2470-8038

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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

Keywords:

Greedy Strategy,

Lowest Operating Cost,

Related subjects:

Computer sciences, other

© 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.

Volume 8 (2023): Issue 1 (January 2023)

A Compound Optimization Greedy Strategy with Reverse Correction Mechanism

Figures & Tables

TABLE I.

Figure. 1.

Figure. 2.

TABLE II.

Figure. 3.

Figure. 4.

Figure. 5.

TABLE III.

Figure. 6.

TABLE IV.

Figure. 7.

Figure. 8.

Figure. 9.

Figure. 10.

Figure. 11.

Figure. 12.

Figure. 13.

TABLE V.

TABLE VI.

TABLE VII.

Figure. 14.

TABLE VIII.

Figure. 15.

Figure. 16.

Figure. 17.

TABLE IX.

Paradigm

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