AN EFFICIENT INVENTORY MODEL-BASED GA FOR FOOD DETERIORATION PRODUCTS IN THE TOURISM INDUSTRY

Al-Salami, Qusay H.; Saleh, Rabeea Kh.; Al-Bazi, Ammar F. J.

doi:10.1590/0101-7438.2022.042.00257447

Qusay H. Al-Salami ^**Corresponding author

Department of Business Administration, College of Administrative and Financial Sciences, Cihan University-Erbil, Kurdistan Region, Iraq - E-mail: qusay.hameed@cihanuniversity.edu.iq

http://orcid.org/0000-0001-8080-1817

Rabeea Kh. Saleh

Department of Oil & Gas Economics, College of Administrative and Financial Sciences, Imam Ja,afar Al-Sadiq University, Baghdad, Iraq - E-mail: rabeea.khalaf.saleh@sadiq.edu.iq

http://orcid.org/0000-0002-6819-6447

Ammar F. J. Al-Bazi

Department of Mechanical, Automotive and Manufacturing Engineering, Faculty of Engineering and Computing, Coventry University, Coventry, UK - E-mail: aa8535@coventry.ac.uk

http://orcid.org/0000-0002-5057-4171

*Corresponding author

Original Chromosome	First Ranking (1^st R)				Second Ranking (2^nd R)
Original Chromosome	Before crossover		After crossover		Before crossover		After crossover
00110111	0011	0111	0111	0011	00	110111	110111	00
10110011	1011	0011	0011	1011	10	110011	110011	10

Original Chromosome	First Ranking (1^st R)		Second Ranking (2^nd R)
Original Chromosome	Before mutation	After mutation	Before mutation	After mutation
00011011	0 0 0 1 1 0 1 1	0 1 0 1 1 0 0 1	0 0 0 1 1 0 1 1	0 0 0 1 1 0 1 1
10010100	1 0 0 1 0 1 0 0	0 0 0 1 0 1 0 1	1 0 0 1 0 1 0 0	1 0 0 0 1 1 0 0

Group No.	Type	Type’s contain	No. of items in each type	Total of items in each group	Average weighted Price (ID/Kg)	Holding Cost (ID/Kg)	Ordering Cost (ID/Kg)
1^st	Dairy products	Butter	5	36	5442	272	174
		Cheese	18
		Yoghurt	4
		Ice cream	6
		Milk	3
2^nd	Canned goods of all kinds	Tomato pastes	9	88	6895	310	290
		Sauces	20
		Jams	26
		Fresh beans	15
		Dried fruit	11
		Seafood	7
3^rd	Appetizers	Chips French fries	9	17	5893	324	259
3^rd	Appetizers	Olive	8	17	5893	324	259
4^th	Biscuit & Chocolate	Biscuit	3	62	6377	332	287
		Corn Flakes	7
		Chocolate	52
5^th	Spices & Condiments	Spices	46	71	9500	390	389
5^th	Spices & Condiments	Condiments	25	71	9500	390	389
6^th	Dry food	Vegetable oils	8	40	2905	143	90
		Pasta	17
		Rice, Sugar & Flour	15
7^th	Meat	Beef	4	28	13816	746	580
		Lamb	4
		Chicken	9
		Fish	11
8^th	Drinks	Tea	12	45	4524	217	190
		Coffee	11
		Soft drinks	7
		Juice	6
		Water	9

	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10	M11	M12
1	00110011	01100110	00111000	11011011	01101010	00011011	01110000	00001100	00000111	01100101	11010100	01111100
2	00111010	11011000	01110110	01101100	00110101	01100110	10101011	00101100	00001100	01100000	11010001	01110111
3	01010111	10001101	11101110	10000011	10111010	10010101	11101010	10111101	11111000	00111000	10010011	11100010
4	01110110	10110011	10111010	01001100	00001100	01111110	00100111	01110000	11101100	11111000	00011100	10001100
5	10010000	00011111	00101100	11010100	00110110	00001100	00001100	10101011	11000111	10110111	00100001	10010010
6	10000111	01111101	10110111	01010000	01011000	00011110	00101100	11101010	01011111	10000101	00110010	00001101
7	01111101	00110111	01111010	01110000	01010111	01110001	10111101	00100111	01110110	01101101	10100000	11111000
8	00110111	11100011	10010101	10101011	11011101	01011111	11101110	00001100	10110000	00011110	10001100	11101100
9	11100011	10110101	01111110	11101010	10101100	01110110	01100010	11111000	10001101	01110001	00111000	11000110
10	10010100	00011011	00001100	00100111	10100011	10110000	00011000	11101100	10000000	01011111	01010101	00011011
11	10110011	01100110	00011110	00001100	10001101	10001101	01101111	11000111	10000111	01110110	01110101	00100101
12	10010110	01010111	01110001	00101100	00110110	00110110	10110011	00011001	01011110	10110000	10010011	10010011
13	00010000	01110110	11001010	01100100	10110101	11011001	10101001	00100101	01010100	00001101	00000110	00000110
14	10110101	10010000	11011001	00101101	11100011	11010100	10100001	01011110	00111001	10110111	11111100	11111100
15	00011011	10000111	10011000	00100010	10110101	01010000	00100111	10000011	01100010	01011000	00000111	00000111

	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10	M11	M12	Total
1	8634500	8009080	8828385	8004285	9325540	10171848	11264428	11609440	10754630	10068820	9656840	8986960	115314756
2	8640370	8017885	8793050	8041135	9332555	10163613	11229562	11603915	10786470	10085380	9659370	8959240	115312545
3	8624040	8016485	8782545	8017090	9310490	10164737	11226582	11567905	10755250	10086105	9644130	8966510	115161869
4	8625430	7996440	8790825	8044385	9348440	10156338	11238273	11598010	10751850	10060955	9681915	8990560	115283421
5	8651870	8006175	8827105	8020050	9320235	10198358	11274983	11560655	10724560	10027570	9687370	8971310	115270241
6	8622540	8014860	8776740	8045640	9340300	10169578	11270108	11557945	10738350	10064220	9663860	8999050	115263191
7	8641000	8002660	8801465	8042390	9308205	10180263	11235377	11570150	10740900	10068745	9676700	8981270	115249125
8	8625490	7997190	8800235	8013785	9308770	10154028	11214922	11609440	10764330	10056215	9671515	8979010	115194930
9	8621000	8006220	8792985	8013185	9327550	10156413	11246953	11576855	10758380	10071160	9687025	8962050	115219776
10	8642860	8014655	8830680	8023530	9311355	10175527	11279218	11572845	10776080	10041195	9667510	8979730	115315185
11	8619800	8009080	8803560	8050385	9324860	10171862	11228188	11544605	10733560	10043740	9663610	8994570	115187820
12	8621280	8000235	8815780	8047135	9320235	10165753	11222222	11606160	10745370	10069030	9644130	8965100	115222430
13	8666570	8002330	8794600	8041190	9314915	10163802	11254462	11596710	10770350	10081295	9665500	8983750	115335474
14	8632370	8023945	8801650	8041735	9302015	10161607	11254547	11565230	10778300	10027570	9652865	8972260	115214094
15	8638980	7998285	8814320	8036880	9314915	10190373	11238273	11566265	10758700	10082655	9659020	8977540	115276206

New row no.	Old row no.		Total	New row no.	Old row no.		Total
1	Crossover Process	3	115161869	9	Mutation Process	5	115270241
2		11	115187820	10		15	115276206
3		8	115194930	11		4	115283421
4		14	115214094	12		2	115312545
5		9	115219776	13		1	115314756
6		12	115222430	14		10	115315185
7		7	115249125	15		13	115335474
8		6	115263191	Total cost for 15 rows			1728821063

Rows No.	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10	M11	M12	Total
21	8609340	8005140	8783940	8026780	9323215	10163443	11217012	11571450	10741080	10031140	9674435	8962770	115109745
19	8631360	8008655	8776940	8011235	9311150	10157403	11245293	11554520	10759800	10049270	9650880	8993710	115150216
1	8624040	8016485	8782545	8017090	9310490	10164737	11226582	11567905	10755250	10086105	9644130	8966510	115161869
18	8632980	8039665	8800170	8009740	9320180	10182808	11254318	11565640	10736960	10046185	9633795	8955770	115178211
2	8619800	8009080	8803560	8050385	9324860	10171862	11228188	11544605	10733560	10043740	9663610	8994570	115187820
17	8628500	8008270	8819290	8012435	9308770	10151497	11214922	11590115	10765150	10061210	9672525	8961980	115194664
3	8625490	7997190	8800235	8013785	9308770	10154028	11214922	11609440	10764330	10056215	9671515	8979010	115194930
16	8634430	8009080	8805340	8034750	9322660	10170012	11207582	11586265	10776320	10043920	9644270	8979010	115213639
4	8632370	8023945	8801650	8041735	9302015	10161607	11254547	11565230	10778300	10027570	9652865	8972260	115214094
5	8621000	8006220	8792985	8013185	9327550	10156413	11246953	11576855	10758380	10071160	9687025	8962050	115219776
6	8621280	8000235	8815780	8047135	9320235	10165753	11222222	11606160	10745370	10069030	9644130	8965100	115222430
22	8655950	7985535	8795795	8045040	9324915	10167177	11231542	11556285	10737710	10082655	9664130	8985890	115232624
7	8641000	8002660	8801465	8042390	9308205	10180263	11235377	11570150	10740900	10068745	9676700	8981270	115249125
25	8625430	8008700	8803745	8032125	9348440	10156338	11253173	11581450	10735950	10044055	9681915	8990560	115261881
8	8622540	8014860	8776740	8045640	9340300	10169578	11270108	11557945	10738350	10064220	9663860	8999050	115263191
9	8651870	8006175	8827105	8020050	9320235	10198358	11274983	11560655	10724560	10027570	9687370	8971310	115270241
10	8638980	7998285	8814320	8036880	9314915	10190373	11238273	11566265	10758700	10082655	9659020	8977540	115276206
27	8625740	8009080	8828385	8004285	9325540	10157772	11264428	11609440	10740580	10058690	9667510	8986960	115278410
11	8625430	7996440	8790825	8044385	9348440	10156338	11238273	11598010	10751850	10060955	9681915	8990560	115283421
26	8655950	8005625	8793050	8028875	9332555	10163613	11244462	11603915	10786470	10068480	9644130	8959240	115286365
20	8657210	8014920	8797955	8016490	9319655	10165883	11242423	11585175	10744910	10065935	9680545	8996760	115287861
12	8640370	8017885	8793050	8041135	9332555	10163613	11229562	11603915	10786470	10085380	9659370	8959240	115312545
13	8634500	8009080	8828385	8004285	9325540	10171848	11264428	11609440	10754630	10068820	9656840	8986960	115314756
14	8642860	8014655	8830680	8023530	9311355	10175527	11279218	11572845	10776080	10041195	9667510	8979730	115315185
28	8651620	8005625	8830680	8011290	9311355	10189603	11279218	11586625	10790130	10031065	9656840	8971240	115315291
29	8666570	8002330	8806570	8041190	9314915	10163802	11254462	11582930	10770350	10071165	9665500	8983750	115323534
23	8651870	8018435	8827105	8007790	9329155	10198358	11274983	11577215	10724560	10044470	9687370	8985890	115327201
15	8666570	8002330	8794600	8041190	9314915	10163802	11254462	11596710	10770350	10081295	9665500	8983750	115335474
24	8654560	8010545	8814320	8049140	9314915	10178133	11253173	11582825	10758700	10082655	9674260	8992120	115365346

Month	Row no.	Gene	Cost (ID)
M₁	1	11100110	8617930
M₂	26	11111111	7981900
M₃	12	11011111	8773250
M₄	0	11110110	7998540
M₅	15	11011111	9290510
M₆	18	11110111	10129917
M₇	15	11010111	11205437
M₈	26	11111111	11531345
M₉	26	11111111	10712730
M₁₀	18	11110111	10019570
M₁₁	1	11111111	9623145
M₁₂	12	11110111	8944540
Total			114828814 ID

\begin{matrix} T h e T o t a l C o s t \\ p e r y e a r (K) \end{matrix} = \overset{A n n u a l O r d e r i n g C o s t}{\overset{⏞}{\frac{D}{Q} \times C_{o}}} + \overset{A n n u a l H o l d i n g C o s t}{\overset{⏞}{\frac{Q}{2} \times C_{h}}} = \sqrt{2 C_{O} D C_{h}}

\underset{\forall j = 1, 2, \dots, 12}{\underset{⏟}{\begin{matrix} C o s t f o r \\ M o n t h (M_{j}) \end{matrix}}} = T C = \sum_{\forall i = 1, 2, \dots, n} [\begin{matrix} D e m a n d o f r a w \\ m a t e r i a l (i) \end{matrix} \times [\begin{matrix} P u r c h a \sin g \\ C o s t (i) \end{matrix} + \{\begin{array}{l} \begin{matrix} H o l d i n g \\ C o s t (i) \end{matrix} \\ \begin{matrix} O r d e r i n g \\ C o s t (i) \end{matrix} \end{array} \begin{array}{l} i f b i n a r y N o . = 0 \\ i f b i n a r y N o . = 1 \end{array}]] T T C = \sum_{\forall i} [D (i) \times [C (i) + \{\begin{matrix} C_{h} (i) i f b i n a r y N o . = 0 \\ C_{o} (i) i f b i n a r y N o . = 1 \end{matrix}\}]]

\underset{\forall j = 1, 2, 3, 4, 5}{\underset{⏟}{A W P f o r G_{1}}} = \frac{\overset{B u t t e r}{\overset{⏞}{[\sum_{k = 1}^{5} Q_{1 k} P_{1 k}]}} + \overset{C h e e s e}{\overset{⏞}{[\sum_{k = 1}^{18} Q_{2 k} P_{2 k}]}} + \dots + \overset{M i l k}{\overset{⏞}{[\sum_{k = 1}^{3} Q_{5 k} P_{5 k}]}}}{\sum_{k = 1}^{5} Q_{1 k} + \sum_{k = 1}^{18} Q_{2 k} + \dots + \sum_{k = 1}^{3} Q_{5 k}} = 5442 I D / K g

\underset{\forall j = 1, 2, \dots, 6}{\underset{⏟}{A W P f o r G 2}} = \frac{\overset{T o m a t o P a s t e s}{\overset{⏞}{[\sum_{k = 1}^{9} Q_{1 k} P_{1 k}]}} + \overset{S a u c e s}{\overset{⏞}{[\sum_{k = 1}^{20} Q_{2 k} P_{2 k}]}} + \dots + \overset{S e a F o o d}{\overset{⏞}{[\sum_{k = 1}^{7} Q_{5 k} P_{5 k}]}}}{\sum_{k = 1}^{9} Q_{1 k} + \sum_{k = 1}^{20} Q_{2 k} + \dots + \sum_{k = 1}^{7} Q_{5 k}} = 6895 I D / K g, a n d s o o n .

\begin{matrix} ∴ C o s t f o r \\ M o n t h (M_{1}) \\ \in R o w_{1} \end{matrix} = \begin{matrix} 150 (5442 + 272) + 300 (6895 + 310) + 70 (5893 + 259) + 160 (6377 + 287) + \\ 70 (9500 + 390) + 170 (2905 + 143) + 130 (13816 + 580) + 220 (4524 + 190) \end{matrix} = 8634500

\begin{matrix} ∴ C o s t f o r \\ M o n t h (M_{12}) \\ \in R o w_{15} \end{matrix} = \begin{matrix} 150 (5442 + 272) + 350 (6895 + 310) + 70 (5893 + 324) + 150 (6377 + 332) + \\ 70 (9500 + 390) + 170 (2905 + 90) + 130 (13816 + 580) + 230 (4524 + 190) \end{matrix} = 8977540

[1] *Corresponding author

Month	M₁								Cost (ID)
Group No.	1	2	3	4	5	6	7	8
Gene	①	①	①	0	0	①	①	0	8617930
	↑	↑	↑			↑	↑
	Re-order points for specific groups

Brasil

Brasil

AN EFFICIENT INVENTORY MODEL-BASED GA FOR FOOD DETERIORATION PRODUCTS IN THE TOURISM INDUSTRY

ABSTRACT

Background:

Purpose:

Methodology:

Results and Conclusion:

Month	M_j Ɐj = 1,2, …,12
Group No.
Gene