TY - JOUR
T1 - A robust optimization approach to steel grade design problem subject to uncertain yield and demand
AU - Zhang, Qi
AU - Liu, Shixin
AU - Zhou, Meng Chu
N1 - Funding Information:
This work is supported by National Natural Science Foundation of China (62073069) and Liaoning Revitalisation Talents Program (XLYC2002041). The authors thank the editors and reviewers for their valuable comments on the manuscript.
Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Lagrangian relaxation
KW - Steel grade design
KW - column-and-constraint generation
KW - steelmaking continuous casting
KW - two-stage robust optimization
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U2 - 10.1080/00207543.2022.2098872
DO - 10.1080/00207543.2022.2098872
M3 - Article
AN - SCOPUS:85135189685
SN - 0020-7543
JO - International Journal of Production Research
JF - International Journal of Production Research
ER -