Pollution sensitive global crude steel production–transportation model under the effect of corruption perception index

Abstract

This article explores global crude steel production transportation problem under the effect of corruption perception index of major steel producing countries. Also, pollution from production process as well as from the transportation system plays vital role towards sustainable development of any country. The development by means of reduction of health hazards, increase of gross domestic product (GDP) in real sense of several country, minimize the number of hungers and also controlling the corruptions in industrial sectors throughout the world. First of all, we have developed functional dependencies among the decision variables like production rate, consumption rate, corruption perception index and pollution indexes of different countries. However, we incorporate pollution due to rail freight transport in a simple production-supply model so as to minimize the average system cost with respect of several constraints for global sustainability in production-consumption-pollutions-corruptions process. In this study we have shown how GDP relates to corruptions, production, and pollution and reduce poverty exclusively. Taking secondary data of 61 countries from world steel annual report 2017, utilizing MATLAB software and LINGO software for data analysis and numerical computation we have come to several decision points. Using fuzzy system, we have shown how human resource development is possible by taking a considerable limit of corruption and pollution indexes. Graphical illustrations and sensitivity analysis are made to show the model validation by means of consumer’s adaptations with such global production set-up.

Appendices

Appendix 1

1-ton iron ore moves 471 miles with 1-gallon fuel (Ref. 3). $Q$ ton iron ore moves 2 $L$ miles with $\frac{2LQ}{{471}}$ gallon fuel.

Appendix 2

By burning 1 gallon of diesel fuel, 22.38 lb of CO₂ are produced (Ref. 16).

1. Pound = 0.000454 MT (approx.). Therefore, by burning $\frac{2LQ}{{471}}$ gallon of diesel fuel, 22.38 × 0.000454 × $\frac{2LQ}{{471}}$ tons of CO₂ are produced.

Appendix 3: Time management of the production process

The following Fig. 17 shows the total system time management process of the proposed supply chain. We see that, for the first cycle, the producer starts its production at time zero and stops at time $T_{1}$, the produced items are transported within the distance L miles reaching at time $T_{2}$ in the Retailer’s counter and finally the inventory exhausts at time $T_{3}$ due to normal demand of the commodities. So, here the production time required at producer’s plant is $T_{1}$; the transportation time required to reach at the Retailer’s counter is $\left( {T_{2} - T_{1} } \right)$ and inventory exhaust time required at the retailer’s shop is $\left( {T_{3} - T_{2} } \right)$. Now, for second cycle time, the produced item is ready for transportation at time $T_{1}$ and to take up for its transportation the rail freight train will have to reach there in time after travelling the 2L distance. Since travelling time for L miles is $\left( {T_{2} - T_{1} } \right)$ so time requires to travel 2 L miles (Up and Down) is $2\left( {T_{2} - T_{1} } \right)$[ see Fig. 

Fig. 16

Time Scheduling of Supply chain (Sc) Model

16].

Thus, we have the time relation

$$ 2\left( {T_{2} - T_{1} } \right) = T_{1} $$

(24)

Again, the retailer’s shop will be empty after the time duration $\left( {T_{3} - T_{2} } \right)$, and that time the items will have to reach there. In this time the rail freight train will have to travel 2L miles to fetch the items. Since, the time requires to travel 2 L miles is $2\left( {T_{2} - T_{1} } \right)$ so we have the second relation on time is

$$ \left( {T_{3} - T_{2} } \right) = 2\left( {T_{2} - T_{1} } \right) $$

(25)

Now, solving Eq. (24) and Eq. (25) we get

$$ \left\{ {\begin{array}{*{20}c} {T_{2} = \frac{3}{2}T_{1} } \\ {T_{3} = \frac{5}{2}T_{1} } \\ \end{array} } \right. $$

(26)

A reverse logistics may be applied over here for more clarity,

The inventory accumulates at the retailer’s shop after time $\left( {T_{3} - T_{2} } \right)$ and it is being ready for transportation. These items are transported to the producer’s plant for manufacturing after travelling L miles. The time requires to travel L miles distance is $\left( {T_{2} - T_{1} } \right)$. After coming back the rail freight train will have to take up the items from retailer’s shop in due time. So, the time relation is

$$ 2\left( {T_{2} - T_{1} } \right) = \left( {T_{3} - T_{2} } \right) $$

(27)

Again, the production will stop after time length $T_{1}$ and will be ready for next production as soon as the transported items are reached there. The time at which the items are transported to begin the production again is $2\left( {T_{2} - T_{1} } \right).$ Thus we have the time relation

$$ 2\left( {T_{2} - T_{1} } \right) = T_{1} $$

(28)

Now, the equations Eq. (27, 28) are same as the equations Eq. (24, 25).

From the above Fig. 16, we see that, A is the point of start time of production, B is the point of end time of production and transportation begin; C is the point of end time of transportation and inventory start time at retailer’s shop and D is the point of no inventory. Now we study the following cases:

• 1. First round: Time requires to hit C (Retailer’s shop) that is the duration of time from production start to transportation stops is $T_{1} + \frac{{T_{1} }}{2} = \frac{{3T_{1} }}{2}$.

• 2. Second round: The rail freight train is at C, Production is half done, Inventory begins to consume. Now, for the next $\frac{{T_{1} }}{2}$ times, the position of the train be at B, production will be completed and ready for transportation, and the inventory exhausts half of the total. Then for another $\frac{{T_{1} }}{2}$ times later, the position of the train be at C, the inventory will be empty. So, to restart the inventory again at the retailer’s shop is $T_{1}$ sharp, and this practice will be continued for the further cycle times. Thus, it is observed that, for uninterrupted production and transportation systems, the inventory process will run with cycle time $T_{1}$ each time without getting any halt or delay except the first / beginning time.

Appendix 4

Here we shall discuss a global poverty eradication mechanism which are the collections ofindividual country’s model graphically (Fig. 

Fig. 17

Poverty Eradication Mechanism

17).

Appendix 5: Data set of highly crude steel produced countries

Country	1Production 2017	2USE 2017	3CPI 2018	4Pollution index 2019
Austria	81.35	46.74	76	21.97
Belgium	78.42	23.98	75	49.89
Bulgaria	6.52	11.02	42	63.98
Croatia	0	9.47	48	31.03
Czech Republic	45.5	81.24	59	40.96
Finland	40.03	20.49	85	11.93
France	155.05	157.71	72	42.7
Germany	432.97	43.33	80	28.01
Greece	13.59	14.45	45	51.86
Hungary	19.01	30.57	46	46.47
Italy	240.68	261.27	52	53.75
Luxembourg	21.72	23.97	81	22.61
Netherlands	67.81	53.67	82	27.45
Poland	103.32	151.06	60	52.38
Slovenia	6.48	11.41	60	24.33
Spain	144.44	146.19	58	39.36
Sweden	47.13	44.98	85	18.01
United Kingdom	74.91	120.4	80	39.43
Bosnia-herzegovina	7.56	7.65	38	63.47
Macedonia	2.73	2.22	37	80.85
Norway	6.03	12.45	84	19.86
Serbia	14.77	10.56	39	58.86
Turkey	375.24	383.56	41	69.15
Byelorussia/Belarus	23.43	21.2	44	41.91
Kazakhstan	44.5	29.04	31	74.25
Moldova	4.55	2.15	33	66.55
Russia	714.91	443.96	28	63.27
Ukraine	213.34	52.33	32	66.63
Uzbekistan	6.8	24.85	23	41.69
Canada	136.14	184.5	81	27.85
Cuba	2.21	1.52	47	73.05
Ei-salvador	0.96	3.38	35	80.14
Guatemala	2.94	10.19	27	67.85
Mexico	199.24	295.01	28	66.02
United states	816.12	1096.64	71	33.95
Argentina	46.24	56.1	40	52.35
Brazil	343.65	212.99	35	57.72
Chile	11.58	31.71	67	65.54
Colombia	12.53	44.84	36	61.74
Ecuador	5.61	20.26	34	56.89
Paraguay	0.24	3.62	29	58.66
Peru	12.07	42.8	35	83.8
Uruguay	0.58	1.85	70	46.63
Venezuela	4.44	6.24	18	73.88
Egypt	68.7	108.9	35	86.72
Libya	4.22	15.39	17	46.99
South Africa	63.01	52.08	43	56.56
Iran	212.36	221.74	28	78.95
Qatar	26.44	16.4	62	66.48
Saudi Arabia	48.31	122.72	49	67.26
United Arab Emirates	33.09	86.2	70	53.11
China	8317.3	7675.3	39	81.91
India	1014.6	1008.92	41	75.81
Japan	1046.6	701	73	37.08
South Korea	710.3	587.52	57	54.19
Pakistan	49.66	90.05	33	75.89
Taiwan, China	224.38	211.82	63	63.47
Thailand	44.71	191.81	36	72.21
Viet Nam	114.73	243.11	33	87.13
Australlia	53.28	60.25	77	23.97
New Zealand	6.57	10.51	87	22.74

1. ^{1, 2}www.worldsteel.org (2018), Error! Hyperlink reference not valid.
2. ⁴www.numbeo.com (2019).