Auction Based Approach to Resolve Scheduling Problem in Steel Making Process

Steel production is an extremely complex process and finding its coherent schedules for the wide variety of production steps in a dynamic environment, where disturbances frequently occur, is a challenging task. In the steel production process, the blast furnace continuously produces liquid iron, which is transformed into liquid steel in the melt shop. The majority of the molten steel passes through a continuous caster to form large steel slabs, which are rolled into coils in the hot strip mill. The scheduling system of these processes has very different objectives and constraints, and operates in an environment where there is a substantial quantity of real-time information concerning production failures and customer requests. The steel making process that includes steel making followed by continuous casting is generally the main bottleneck in steel production. Therefore, comprehensive scheduling of this process is critical to improve the quality and productivity of the entire production system. This paper addresses the scheduling problem in steel making process. The methodology of winner determination using combinatorial auction process is employed to solve the aforementioned problem. Combinatorial auction based approaches are capable of minimizing large search space, discontinuity, and noise in order to obtain near optimal solutions. Hence in the proposed work the authors have adopted its splendid characteristics to enhance the quality of the solution in such complex scenarios. In the combinatorial auction, allowing bidding on a combination of assets, offers a way to enhance the efficiency of allocating the assets. In this paper, scheduling problem in steel making has been formulated as a linear integer program to determine the scheduling sequence for different charges. Then bids are obtained for sequencing the charges. Next, a heuristic approach is used to evaluate the bids. The computational results show that our algorithm can obtain optimal or near-optimal solutions for combinatorial problems. The proposed algorithm has been verified by a case study.