Robust operation optimization in cold rolling production process

Abstract

In the cold rolling production process in iron and steel industry, the operation optimization problem (OOP) is to determine the optimal setting for the control variables so as to maximize the product quality or minimize the total operation cost. Traditional OOP generally assumes that the production process is stable, however, the practical production process is dynamic and the dimensions of input coils such as crown and thickness often change during production. To tackle the dynamic change of dimensions of a coil and obtain a satisfactory setting for control variables that can guarantee a good product quality, this paper investigate the robust operation optimization problem (ROOP) in cold rolling production process. A mathematical model is constructed for the ROOP so as to minimize the total power of rollers and an improved particle swarm optimization (PSO) algorithm is developed. Computational results based on practical production data show that the proposed ROOP model and the PSO algorithm are very effective and they can help to reduce the energy consumption and at the same time obtain a better power allocation on rollers.