A robust optimization approach to steel grade design problem subject to uncertain yield and demand
研究了钢铁连续铸造生产中考虑不确定产量和需求的钢种设计问题，提出一种增强列与约束生成算法，通过拉格朗日松弛分解子问题，有效求解大规模实例并优于标准算法。
This work formulates and investigates a steel grade design problem (SGDP) arising from a production process of steelmaking continuous casting. For the first time, we consider uncertain yield and demand in SGDP and construct a two-stage robust optimisation model accordingly. Then, we propose an enhanced column-and-constraint generation algorithm to obtain high-quality solutions. By exploiting the problem characteristics, we first use a Lagrangian relaxation method to decompose SGDP into multiple subproblems and then apply a standard column-and-constraint generation algorithm to solve the latter. At last, we test the proposed algorithm by extensive instances constructed based on actual production rules of a steelmaking shop. Numerical results show that it can effectively solve large-scale SGDPs. The obtained plan is better than those obtained by a commonly-used and standard column-and-constraint generation algorithm.