Improving Metallic Yield in a Steel Rolling Plant through Optimization

The objective of this project is to use optimization to improve metallic yield (the percentage of raw material that ends up as usable product) in an ArcelorMital Steel Rolling Plant. The metallic yield of the rolling operations depends upon the length of billets from which the final product is manufactured. Ideally, a single customer order would be filled using billets of precisely the length that would yield the minimum achievable amount of scrap. However, ideal yields for each order in a set of customer orders cannot be aggregated to fulfill them at the ideal yield for the entire set of orders, as this conflicts with the objective of keeping the inventory at a minimum level. In other words, the optimal way of fulfilling a set of customer orders is a tradeoff between yield maximization and inventory minimization. This project will develop efficient and robust models and algorithms for optimal billet ordering. Improving metallic yield is critical in ArcelorMittal, and the proposed project will improve it significantly. Each pound of scrap steel that is reprocessed in the furnace represents an important additional cost, and given the amount of production, even a small percentage of yield improvement is expected to result in significant cost savings.

Faculty Supervisor:

Dr. Vince Thomson

Student:

Onur Hisarciklilar

Partner:

ArcelorMittal

Discipline:

Engineering - mechanical

Sector:

Mining and quarrying

University:

McGill University