Many European metallurgical companies are forced to import iron ore from remote destinations. For these companies
it is necessary to determine the amount of iron ore that will have to be ordered and to create such a delivery
schedule so that the continuous operation of blast-furnace plant is not disrupted and there is no exceedingly large
stock of this raw material. The objective of this article is to design the decision support system for iron ore supply
which would effi ciently reduce uncertainty and risk of that decision-making. The article proposes a hybrid intelligent
system which represents a combination of diff erent artifi cial intelligence methods with dynamic simulation
technique for that purpose.Web of Science511939

Besta, Petr

Feliks, Jerzy

Lenort, Radim

Samolejová, Andrea

DSpace at VSB Technical University of Ostrava

91METALURGIJA 51 (2012) 1, 91-93A. SAMOLEJOVÁ, J. FELIKS, R. LENORT, P. BESTAA HYBRID DECISION SUPPORT SYSTEM FOR IRON ORE SUPPLYReceived – Primljeno: 2010-10-21Accepted – Prihvaćeno: 2011-02-25Preliminary Note – Prethodno priopćenjeISSN 0543-5846METABK 51(1) 91-93 (2012)UDC – UDK 669.1:658.7:004.8:004.94=111Many European metallurgical companies are forced to import iron ore from remote destinations. For these compa-nies it is necessary to determine the amount of iron ore that will have to be ordered and to create such a delivery schedule so that the continuous operation of blast-furnace plant is not disrupted and there is no exceedingly large stock of this raw material. The objective of this article is to design the decision support system for iron ore supply which would effi  ciently reduce uncertainty and risk of that decision-making. The article proposes a hybrid intelli-gent system which represents a combination of diff erent artifi cial intelligence methods with dynamic simulation technique for that purpose.Key words: iron ore supply, hybrid decision support system, neural network, simulation, fuzzy setsHibridni sustav za podršku odlučivanja opskrbe željeznom rudom. Mnoge europske metalurške kompanije su prisiljene na uvoz željezne rudače iz udaljenih destinacija. Za ove tvrtke potrebno je odrediti količinu željezne rude koje će morati biti naručena i stvoriti takav raspored isporuke, tako da je kontinuirani rad visokih peći ne bude poremećen i da ne se ne stvaraju pretjerano velike zalihe te sirovine. Cilj ovog članka je da se dizajnira sustav za podršku kod odlučivanja za opskrbu željezne rude kojim bi se efi kasno smanjila nesigurnost i rizik isporuke iste. Za tu svrhu u članku se predlaže hibridni inteligentni sustav koji predstavlja kombinaciju različitih metoda umjetne inteligencije i dinamičke simulacijske tehnike.Ključne riječi: opskrba željeznom rudačom, hibridni sustav za podršku odlučivanja, neuronske mreže, simulacija, neizraziti skupoviINTRODUCTIONIron ore belongs to the strategic raw materials of met-allurgical companies with full metallurgical cycle, in-cluding pig iron production. More than 80 % of world iron ore production nowadays comes from China, Brazil, Australia, and India [1]. Many European metallurgical companies are forced to import iron ore from remote des-tinations using sea transport and railway service. That is why the basic logistics decisions play key role here. It is necessary to determine the amount of iron ore that will have to be ordered and to do it well in advance. It is also necessary to create such a delivery schedule so that the continuous operation of blast-furnace plant is not disrupt-ed and, at the same time, there is no exceedingly large stock of this raw material (i.e. inventory costs).Iron ore supply is inﬂ uenced by a number of sto-chastic effects which make the aforementioned logistics decisions signiﬁ cantly more difﬁ cult. The most impor-tant of these effects is the ﬂ uctuation in the demand for iron ore caused by variability of demand for metallurgi-cal commodities (and the increasing recent world eco-nomic crisis) and by variability of delivery time caused, for example, by weather-related problems.A. Samolejová, R. Lenort, P. Besta, Faculty of Metallurgy and Materials En-gineering, VŠB – Technical University of Ostrava, Ostrava, Czech RepublicJ. Feliks, Faculty of Management, AGH University of Technology and Science, Cracow, PolandThe objective of this article is to design the decision support system for iron ore supply which would efﬁ ciently reduce uncertainty and risk of logistics decision-making.EXPERIMENTAL WORKIron ore supply process has been assessed in a met-allurgical company situated in Poland which buys iron ore from a supplier from Ukraine. The iron ore is trans-ported by train. The basic starting point for stipulation of the delivery schedule is a quality forecast of demand for iron ore, i.e. proposal of a suitable forecasting mod-el. Demand for iron ore in a period of 44 months was used to serve this purpose. Using the time series analy-sis, seasonal and trend component wasn’t identiﬁ ed. With regards to the fact, suitable time series models had been chosen for demand forecasting.Unfortunately, none of the classical models guaran-teed a forecast with acceptable error rate (the required deviation from reality being 10%). The application of models, resulting from time series of iron ore demand only, was connected with the following problems:1. Time series was disrupted by the world crisis im-pacts which resulted in signiﬁ cant decrease of de-mand for iron ore in the monitored period of time.2. Time series did not affect the cycle component, the pe-riod of which is 5.9 years in metallurgical industry [2].92  METALURGIJA 51 (2012) 1, 91-93A. SAMOLEJOVÁ et al.: A HYBRID DECISION SUPPORT SYSTEM FOR IRON ORE SUPPLYThat is why such models were searched as to make it possible to include not only the historical data con-cerning demand progress but also other factors which have an essential impact on prediction quality. Models using artiﬁ cial neural network for forecasting comply with the above mentioned requirements. These models can be exposed to large amounts of data and discover patterns and relationships within them [3]. A multilayer perceptron (MLP) neural network model described in Figure 1 was designed.The input and output layer consists of the following parameters:Dt, Dt-1, Dt-2 –  historical data regarding demand for iron ore shifted by the equivalent time horizon (t).Pt –  difference between the planned and real production volume in previous periods (t).It – CRU Steel Price Index in previous periods.Dt+1 – demand forecast of iron ore for the next month (t).The variable Pt representing the difference between planned and real production takes into account a change of iron ore stock volume. If the real production is higher than the planned one the stock decreases and converse-ly. CRU Steel Price Index It takes into account the mar-ket development (CRU is a leading independent sup-plier of steel market information). Model is designed in such a way that the output is represented by the demand forecast of iron ore for the next month Dt+1 (t).Levenberg-Maguardt algorithm was used for learn-ing the network. The network structure was learned, tested and selected up to the moment when error rate in the level of 10 % was achieved for the testing set. The model was veriﬁ ed using data of real demand for iron ore in the previous 20 months of the monitored period of time. It is obvious from Figure 2 that the designed model generates sufﬁ ciently exact forecast even for pe-riods of signiﬁ cant decrease of demand. The model used for forecasting iron ore demand was unnecessary static. The decision-making process con-cerning the creation of delivery schedule, i.e. the break-down of the anticipated monthly demands for the indi-vidual deliveries from the point of view of volume and time is, however, of dynamic nature. A suitable dynam-ic model which would be able to cover the essential sto-chastic effects inﬂ uencing the process of iron ore supply was searched for. The classic models in this area include ﬁ xed order-quantity system (continuous system) or ﬁ xed order-peri-od system (periodic system) [4]. However, these models are encumbered by many simplifying conditions which make it impossible to cover all the important aspects of a real process dealing with iron ore demand, which is why they do not provide sufﬁ cient information for efﬁ cient decision-making. Thus the attention was shifted from analytical models to simulation techniques. Computer simulation is a method which uses computer model of company process to conduct experiments with the aim of achieving model parameters that are later applied back on the examined process [5]. With regard to the character of the iron ore supply process a dynamic stochastic simu-lation was used where time plays the key role [6].Using methodology designed for dynamic simula-tion of metallurgical processes [7], a model of iron ore supply from Ukraine to a metallurgical company locat-ed in Poland has been constructed. The model includes a process of supply completion, their transport, unload-ing and storage in a buffer warehouse. The scheme of the model created in the environment of Dosimis 3® simulation software is illustrated in Figure 3.The model is used to analyse the impact of volume and number of supplies on delivery time and the level of stock of iron ore within a company. The acquired out-comes can be used to determine such a delivery sched-ule that will provide the desired level of stock of iron ore in the company buffer warehouse.Weather-related problems represent one of the most important aspects inﬂ uencing iron ore supplies. The analysis concerning iron ore supply identiﬁ ed problems with delayed deliveries and maintenance of sufﬁ cient buffer stock in winter months. These problems are cau-sed by trains bringing in frozen raw material, which makes it necessary to thaw the carriages during the un-Figure 1 Artifi cial neural network forecasting modelFigure 2 Verifi cation of the designed forecasting modelFigure 3 Dynamic model of iron ore supply 93METALURGIJA 51 (2012) 1, 91-93A. SAMOLEJOVÁ et al.: A HYBRID DECISION SUPPORT SYSTEM FOR IRON ORE SUPPLYloading process. This operation can take tens of hours when the temperatures are very low. In order to be able to take into account the time nee-ded for the necessary thawing of train carriages, a fuzzy model was designed. Fuzzy sets and expert knowledge of company specialists were used to create this model. The model structure is clear from Figure 4.The model input and output variables are:Tf –  temperature on the day of train arrival in the com-pany (°C).Tf(-24) –  average temperature from the previous day (°C).ttr – train transport time to company (hours).tdf – thawing time of train (hours).Each variable was assigned an appropriate range of values which have been divided into three categories by means of trapezoidal membership functions. Thawing time of trains tdf (hours) is the outcome variable of the fuzzy model.  The expert knowledge of the company specialists helped to create a knowledge base which makes it pos-sible to determine the thawing time in dependence on the values of the input variables of the model using „if and then“ type of rules.RESULTS AND DISCUSSIONExperimental work proves that efﬁ cient decision support system for iron ore supply must integrate the outcomes of several models, utilising various approach-es and techniques. A hybrid intelligent system which represents a combination of different artiﬁ cial intelli-gence methods with dynamic simulation technique and conventional systems (such as database systems) can meet this objective.The concept of such a system is illustrated in Figure 5. Its most important sub-systems are the sub-system forecasting the demand for iron ore (sub-system A) and the sub-system used for modelling and simulation of iron ore delivery time (sub-system B).The essence of the system is to determine the iron ore demand by means of forecasting model based on artiﬁ cial neural network. This forecast is used as input information for a simulation model which enables us to ﬁ nd a suitable delivery schedule of iron ore into a com-pany. It is convenient to make provision for the case of delayed deliveries caused by frozen train carriages in winter months by revising the unloading time in the simulation model. Fuzzy model determining thawing time of carriages can be used for that purpose.CONCLUSIONThe concept of the designed hybrid intelligent system makes it possible to utilise the advantages and to over-come the limitations of individual artiﬁ cial intelligence and conventional techniques. Mutual exchange of infor-mation from forecasting model, delivery time model, mo-del of thawing trains, and the knowledge of current level of stock of iron ore are therefore sufﬁ cient for efﬁ cient logistics decision-making in the area of iron ore supply.In the end, differences of iron ore supply processes in various European metallurgical companies must be pointed out, especially as for their technological and sup-ply process parameters. Therefore sub-systems included in the hybrid intelligent system concept will have to be adjusted to speciﬁ c conditions of each company.ACKNOWLEDGEMENTThe work was supported by the research plan of Ministry of Education, Youth and Sports of the Czech Republic No. MSM 6198910015.REFERENCES[1] Mineral Commodity Summaries, U.S. Geological Survey, January 2010[2] I. Janík, E. Krausová, Cyklické jevy v obchodě s ocelí. Proceedings of XII. international scientiﬁ c conference Iron and Steelmaking. VŠB – Technical University of Ostrava, Ostrava, 2002, pp. 149-152[3] H. Levenbach, J. P. Cleary, Forecasting: Practice and Pro-cess for Demand Management, Duxbury, 2006[4] S. N. Chary, Production and Operations Management, Tata McGraw-Hill Publishing Company, 2004[5] D. Malindžák, M. Straka, P. Helo, J. Takala, The Methodo-logy for the Logistics System Simulation Model Design, Metalurgija, 49 (2010) 4, 348-352[6] J. A. Sokolowski, C. M. Banks, Principles of Modeling and Simulation: a Multidisciplinary Approach, John Wiley & Sons, 2009[7] R. Lenort, L. Tvrdoň, Methodology for Application of Dy-namic Simulation in Metallurgical Processes, Metallurgi-cal Journal, 63 (2010) 6, 164-167Note:  The responsible translator for English language is Petr Jaroš (Eng-lish Language Tutor at the College of Tourism and Foreign Trade, Goodwill - VOŠ, Frýdek-Místek, the Czech Republic). Revised by John Vlcek (Literacy Tutor at West Suffolk College, Bury St Ed-munds, England)Figure 4  Structure of fuzzy model for thawing of frozen train carriages Figure 5  Concept of hybrid decision support system for iron ore supply

A hybrid decision support system for iron ore supply

Many European metallurgical companies are forced to import iron ore from remote destinations. For these companies it is necessary to determine the amount of iron ore that will have to be ordered and to create such a delivery schedule so that the continuous operation of blast-furnace plant is not disrupted and there is no exceedingly large stock of this raw material. The objective of this article is to design the decision support system for iron ore supply which would effi ciently reduce uncertainty and risk of that decision-making. The article proposes a hybrid intelligent system which represents a combination of diff erent artifi cial intelligence methods with dynamic simulation technique for that purpose

A. Samolejová

J. Feliks

R. Lenort

P. Besta

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91 METALURGIJA 51 (2012) 1, 91-93A. SAMOLEJOVÁ, J. FELIKS, R. LENORT, P. BESTAA HYBRID DECISION SUPPORT SYSTEM FOR IRON ORE SUPPLYReceived – Primljeno: 2010-10-21Accepted – Prihvaćeno: 2011-02-25Preliminary Note – Prethodno priopćenjeISSN 0543-5846METABK 51(1) 91-93 (2012)UDC – UDK 669.1:658.7:004.8:004.94=111Many European metallurgical companies are forced to import iron ore from remote destinations. For these compa-nies it is necessary to determine the amount of iron ore that will have to be ordered and to create such a delivery schedule so that the continuous operation of blast-furnace plant is not disrupted and there is no exceedingly large stock of this raw material. The objective of this article is to design the decision support system for iron ore supply which would effi   ciently reduce uncertainty and risk of that decision-making. The article proposes a hybrid intelli-gent system which represents a combination of diff  erent artifi  cial intelligence methods with dynamic simulation technique for that purpose.Key words: iron ore supply, hybrid decision support system, neural network, simulation, fuzzy setsHibridni sustav za podršku odlučivanja opskrbe željeznom rudom. Mnoge europske metalurške kompanije su prisiljene na uvoz željezne rudače iz udaljenih destinacija. Za ove tvrtke potrebno je odrediti količinu željezne rude koje će morati biti naručena i stvoriti takav raspored isporuke, tako da je kontinuirani rad visokih peći ne bude poremećen i da ne se ne stvaraju pretjerano velike zalihe te sirovine. Cilj ovog članka je da se dizajnira sustav za podršku kod odlučivanja za opskrbu željezne rude kojim bi se efi  kasno smanjila nesigurnost i rizik isporuke iste. Za tu svrhu u članku se predlaže hibridni inteligentni sustav koji predstavlja kombinaciju različitih metoda umjetne inteligencije i dinamičke simulacijske tehnike.Ključne riječi: opskrba željeznom rudačom, hibridni sustav za podršku odlučivanja, neuronske mreže, simulacija, neizraziti skupoviINTRODUCTIONIron ore belongs to the strategic raw materials of met-allurgical companies with full metallurgical cycle, in-cluding pig iron production. More than 80 % of world iron ore production nowadays comes from China, Brazil, Australia, and India [1]. Many European metallurgical companies are forced to import iron ore from remote des-tinations using sea transport and railway service. That is why the basic logistics decisions play key role here. It is necessary to determine the amount of iron ore that will have to be ordered and to do it well in advance. It is also necessary to create such a delivery schedule so that the continuous operation of blast-furnace plant is not disrupt-ed and, at the same time, there is no exceedingly large stock of this raw material (i.e. inventory costs).Iron ore supply is inﬂ  uenced by a number of sto-chastic effects which make the aforementioned logistics decisions signiﬁ  cantly more difﬁ  cult. The most impor-tant of these effects is the ﬂ  uctuation in the demand for iron ore caused by variability of demand for metallurgi-cal commodities (and the increasing recent world eco-nomic crisis) and by variability of delivery time caused, for example, by weather-related problems.A. Samolejová, R. Lenort, P. Besta, Faculty of Metallurgy and Materials En-gineering, VŠB – Technical University of Ostrava, Ostrava, Czech RepublicJ. Feliks, Faculty of Management, AGH University of Technology and Science, Cracow, PolandThe objective of this article is to design the decision support system for iron ore supply which would efﬁ  ciently reduce uncertainty and risk of logistics decision-making.EXPERIMENTAL WORKIron ore supply process has been assessed in a met-allurgical company situated in Poland which buys iron ore from a supplier from Ukraine. The iron ore is trans-ported by train. The basic starting point for stipulation of the delivery schedule is a quality forecast of demand for iron ore, i.e. proposal of a suitable forecasting mod-el. Demand for iron ore in a period of 44 months was used to serve this purpose. Using the time series analy-sis, seasonal and trend component wasn’t identiﬁ  ed. With regards to the fact, suitable time series models had been chosen for demand forecasting.Unfortunately, none of the classical models guaran-teed a forecast with acceptable error rate (the required deviation from reality being 10%). The application of models, resulting from time series of iron ore demand only, was connected with the following problems:1.  Time series was disrupted by the world crisis im-pacts which resulted in signiﬁ  cant decrease of de-mand for iron ore in the monitored period of time.2.  Time series did not affect the cycle component, the pe-riod of which is 5.9 years in metallurgical industry [2].92   METALURGIJA 51 (2012) 1, 91-93A. SAMOLEJOVÁ et al.: A HYBRID DECISION SUPPORT SYSTEM FOR IRON ORE SUPPLYThat is why such models were searched as to make it possible to include not only the historical data con-cerning demand progress but also other factors which have an essential impact on prediction quality. Models using artiﬁ  cial neural network for forecasting comply with the above mentioned requirements. These models can be exposed to large amounts of data and discover patterns and relationships within them [3]. A multilayer perceptron (MLP) neural network model described in Figure 1 was designed.The input and output layer consists of the following parameters:Dt, Dt-1, Dt-2 –  historical data regarding demand for iron ore shifted by the equivalent time horizon (t).Pt –   difference between the planned and real production volume in previous periods (t).It – CRU Steel Price Index in previous periods.Dt+1 – demand forecast of iron ore for the next month (t).The variable Pt representing the difference between planned and real production takes into account a change of iron ore stock volume. If the real production is higher than the planned one the stock decreases and converse-ly. CRU Steel Price Index It takes into account the mar-ket development (CRU is a leading independent sup-plier of steel market information). Model is designed in such a way that the output is represented by the demand forecast of iron ore for the next month Dt+1 (t).Levenberg-Maguardt algorithm was used for learn-ing the network. The network structure was learned, tested and selected up to the moment when error rate in the level of 10 % was achieved for the testing set. The model was veriﬁ  ed using data of real demand for iron ore in the previous 20 months of the monitored period of time. It is obvious from Figure 2 that the designed model generates sufﬁ  ciently exact forecast even for pe-riods of signiﬁ  cant decrease of demand. The model used for forecasting iron ore demand was unnecessary static. The decision-making process con-cerning the creation of delivery schedule, i.e. the break-down of the anticipated monthly demands for the indi-vidual deliveries from the point of view of volume and time is, however, of dynamic nature. A suitable dynam-ic model which would be able to cover the essential sto-chastic effects inﬂ  uencing the process of iron ore supply was searched for. The classic models in this area include ﬁ  xed order-quantity system (continuous system) or ﬁ  xed order-peri-od system (periodic system) [4]. However, these models are encumbered by many simplifying conditions which make it impossible to cover all the important aspects of a real process dealing with iron ore demand, which is why they do not provide sufﬁ  cient information for efﬁ  cient decision-making. Thus the attention was shifted from analytical models to simulation techniques. Computer simulation is a method which uses computer model of company process to conduct experiments with the aim of achieving model parameters that are later applied back on the examined process [5]. With regard to the character of the iron ore supply process a dynamic stochastic simu-lation was used where time plays the key role [6].Using methodology designed for dynamic simula-tion of metallurgical processes [7], a model of iron ore supply from Ukraine to a metallurgical company locat-ed in Poland has been constructed. The model includes a process of supply completion, their transport, unload-ing and storage in a buffer warehouse. The scheme of the model created in the environment of Dosimis 3® simulation software is illustrated in Figure 3.The model is used to analyse the impact of volume and number of supplies on delivery time and the level of stock of iron ore within a company. The acquired out-comes can be used to determine such a delivery sched-ule that will provide the desired level of stock of iron ore in the company buffer warehouse.Weather-related problems represent one of the most important aspects inﬂ   uencing iron ore supplies. The analysis concerning iron ore supply identiﬁ  ed problems with delayed deliveries and maintenance of sufﬁ  cient buffer stock in winter months. These problems are cau-sed by trains bringing in frozen raw material, which makes it necessary to thaw the carriages during the un-Figure 1 Artifi  cial neural network forecasting modelFigure 2 Verifi  cation of the designed forecasting modelFigure 3 Dynamic model of iron ore supply 93 METALURGIJA 51 (2012) 1, 91-93A. SAMOLEJOVÁ et al.: A HYBRID DECISION SUPPORT SYSTEM FOR IRON ORE SUPPLYloading process. This operation can take tens of hours when the temperatures are very low. In order to be able to take into account the time nee-ded for the necessary thawing of train carriages, a fuzzy model was designed. Fuzzy sets and expert knowledge of company specialists were used to create this model. The model structure is clear from Figure 4.The model input and output variables are:Tf –   temperature on the day of train arrival in the com-pany (°C).Tf(-24) –   average temperature from the previous day (°C).ttr – train transport time to company (hours).tdf – thawing time of train (hours).Each variable was assigned an appropriate range of values which have been divided into three categories by means of trapezoidal membership functions. Thawing time of trains tdf (hours) is the outcome variable of the fuzzy model.  The expert knowledge of the company specialists helped to create a knowledge base which makes it pos-sible to determine the thawing time in dependence on the values of the input variables of the model using „if and then“ type of rules.RESULTS AND DISCUSSIONExperimental work proves that efﬁ  cient  decision support system for iron ore supply must integrate the outcomes of several models, utilising various approach-es and techniques. A hybrid intelligent system which represents a combination of different artiﬁ  cial intelli-gence methods with dynamic simulation technique and conventional systems (such as database systems) can meet this objective.The concept of such a system is illustrated in Figure 5. Its most important sub-systems are the sub-system forecasting the demand for iron ore (sub-system A) and the sub-system used for modelling and simulation of iron ore delivery time (sub-system B).The essence of the system is to determine the iron ore demand by means of forecasting model based on artiﬁ  cial neural network. This forecast is used as input information for a simulation model which enables us to ﬁ  nd a suitable delivery schedule of iron ore into a com-pany. It is convenient to make provision for the case of delayed deliveries caused by frozen train carriages in winter months by revising the unloading time in the simulation model. Fuzzy model determining thawing time of carriages can be used for that purpose.CONCLUSIONThe concept of the designed hybrid intelligent system makes it possible to utilise the advantages and to over-come the limitations of individual artiﬁ  cial intelligence and conventional techniques. Mutual exchange of infor-mation from forecasting model, delivery time model, mo-del of thawing trains, and the knowledge of current level of stock of iron ore are therefore sufﬁ  cient for efﬁ  cient logistics decision-making in the area of iron ore supply.In the end, differences of iron ore supply processes in various European metallurgical companies must be pointed out, especially as for their technological and sup-ply process parameters. Therefore sub-systems included in the hybrid intelligent system concept will have to be adjusted to speciﬁ  c conditions of each company.ACKNOWLEDGEMENTThe work was supported by the research plan of Ministry of Education, Youth and Sports of the Czech Republic No. MSM 6198910015.REFERENCES[1]  Mineral Commodity Summaries, U.S. Geological Survey, January 2010[2]  I. Janík, E. Krausová, Cyklické jevy v obchodě s ocelí. Proceedings of XII. international scientiﬁ  c conference Iron and Steelmaking. VŠB – Technical University of Ostrava, Ostrava, 2002, pp. 149-152[3]  H. Levenbach, J. P. Cleary, Forecasting: Practice and Pro-cess for Demand Management, Duxbury, 2006[4]  S. N. Chary, Production and Operations Management, Tata McGraw-Hill Publishing Company, 2004[5]  D. Malindžák, M. Straka, P. Helo, J. Takala, The Methodo-logy for the Logistics System Simulation Model Design, Metalurgija, 49 (2010) 4, 348-352[6]  J. A. Sokolowski, C. M. Banks, Principles of Modeling and Simulation: a Multidisciplinary Approach, John Wiley & Sons, 2009[7]  R. Lenort, L. Tvrdoň, Methodology for Application of Dy-namic Simulation in Metallurgical Processes, Metallurgi-cal Journal, 63 (2010) 6, 164-167Note:   The responsible translator for English language is Petr Jaroš (Eng-lish Language Tutor at the College of Tourism and Foreign Trade, Goodwill - VOŠ, Frýdek-Místek, the Czech Republic). Revised by John Vlcek (Literacy Tutor at West Suffolk College, Bury St Ed-munds, England)Figure 4   Structure of fuzzy model for thawing of frozen train carriages Figure 5   Concept of hybrid decision support system for iron ore supply

A hybrid decision support system for iron ore supply

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