A robust optimization approach to steel grade design problem subject to uncertain yield and demand

• Published in: International journal of production research Vol. 61; no. 15; pp. 5176 - 5192
• Main Authors: Zhang, Qi, Liu, Shixin, Zhou, MengChu
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 03.08.2023; Taylor & Francis LLC
• Subjects: Algorithms; column-and-constraint generation; Continuous casting; Design optimization; Lagrangian relaxation; Relaxation method (mathematics); Robustness (mathematics); Steel grade design; Steel making; steelmaking continuous casting; two-stage robust optimization
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2022.2098872

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.