Applications of Mathematical Programming Models for Product Mix Optimization in World Steel Industry: Challenges and Directions

The world steel demand by construction industry, mechanical engineering industry, and metal goods industry is more than 78 %. More than 46 % steel is supplied by China, while India’s steel supply share is approximately 5 %. The production of world crude steel increased from 851 megatons (Mt) in 2001 to 1,548 Mt in the year 2012. Also, world average steel use per capita has increased from 150 kg in 2001 to 215 kg in 2011. Mathematical programming models for product mix optimization have been applied to various industrial sectors to achieve improved performance. Process industry, especially the steel sector, has been home to extensive mathematical programming applications. In this paper we present a survey of more than 20 reported publications in the field of mathematical modeling to determine optimum product mix in an integrated steel plant. It suggests a classification of models based on the functional areas they are applied in. The study is an attempt to review the mathematical programming application in product mix optimization in integrated steel plants. The study summarizes the various applications of genetic algorithm, revenue management, energy modeling, and modeling uncertainty in the model parameters. The study concludes that there is a growing need of modeling uncertainty in demand and prices of finished goods of steel. The future researchers should also focus on modeling multiple objective mathematical models, genetic algorithm, and revenue management application. The alternative interesting extensions are to explore if the similar applications exist in other metal processing industries, viz., aluminum, copper, etc. The mathematical applications in one industry have significant scope to replicate in other similar process industries. The study states that the current paper has reviewed reported literature from product mix optimization real-world applications. This is an interesting extension of this research to review the reported literature from multiple areas including cutting stock, financial planning, blending, implementation of decision support systems, production and capacity planning, etc.