Discuss major algorithms used for a decision support system for the steel industry.

Regardless of the industry and organization, the objective of the management is almost similar. This objective is concerned with increasing efficiency and improving performance at low costs. At present, the steel industry is facing the issue of improving production processes in a time-efficient and cost-effective manner. Following are the support systems for making decisions utilized within the steel industry:

Machine learning algorithms of System of Decision Support in Steel manufacturing industry   

The manufacturing management focuses on the above-explained issue to increase the production output and improve performance in the production of different steel products. In the steel production, algorithms of ELM or Extreme Machine Learning are utilized. It can be said that the main objective of ELM is bias and weight in which functions are performed in different ways. Suppose the sample is based on the following algorithms: 

(x+a)^n=∑_(k=0)^n▒〖(n¦k) x^k a^(n-k) 〗

The equation is abbreviated as:

Hβ = Y

Where H is denoted as the matrix of a hidden layer which is as follow:

The analysis of the algorithms is used within the matrix which are hidden in the output of steel manufacturing. We could calculate the output of the production by using this method.

Genetic Algorithms of System of Decision Support in Steel manufacturing industry   

In general, these are algorithms which are used in the manufacturing industry to evaluate different problems occurring in production processes and resolve these issues to optimize the processes. The genetic algorithms are used to: 

Analyze primitive stages of production

Evaluate different processes of production through the use of artificial intelligence. 

Gi = fm(Gi−1), 1 ≤ i ≤ I max

If the value of G is calculated, the production output is determined in the steel industry.  Other than this, there are different examples in the history of steel industry where a system of decision support is being used as it is the best way of improving the hierarchy of management with different decisions. 

References of algorithms used for a decision support system for the steel industry.

G. F. Porzio, et al., 2013. Reducing the energy consumption and CO2 emissions of energy intensive industries through decision support systems–An example of application to the steel industry.. Applied energy,, Volume 112, pp. 818-833.

M. KARATAŞ & Gecili, H., 2012. The role of decision support systems in steel industry.. Engineering Science & Technology, an International Journal, 15(1).

P. Cowling, 2003. A flexible decision support system for steel hot rolling mill scheduling. Computers & Industrial. Computers & Industrial Engineering,, 45(2), pp. 307-321.

X. Wang, Wong, T. N. & Fan, Z. P., 2013. Ontology-based supply chain decision support for steel manufacturers in China.. Expert Systems with Applications,, 40(18), pp. 7519-7533..