Solving the Steel Continuous Casting Problem using an Artificial Intelligence Model

Over the past decade, the steel continuous casting problem has revolutionized in important and remarkable ways. In this paper, we consider a multiple parallel device for the steel continuous casting problem (SCC) known as one of the hardest scheduling problem. The SCC problem is an important NP-hard combinatorial optimization problem and can be seen as three stages hybrid flowshop problem. We have proposed to solve it a recurrent neural network (RNN) with LSTM cells that we will executed in the cloud. For our problem, we consider several machines at each stage that are the converter stage, the refining stage and the continuous casting stage. We formulate the mathematical model and implemented a RNN with LSTM cells to approximately solve the problem. The proposed neural network has been trained on a big dataSet, which contains 10 000 real use cases and others generated randomly. The performances of the proposed model are very interesting such that the success rate is 93% and able to resolve large instances while the traditional approaches are limited and fail to resolve very large instances. We analyzed the results taking into account the quality of the solution and the prediction time to highlight the performance of the approach.