A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 1 Issue 2
Apr.  2014

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Tianmu Ma, Xiaochuan Luo and Tianyou Chai, "Modeling and Hybrid Optimization of Batching Planning System for Steelmaking-continuous Casting Process," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 2, pp. 113-126, 2014.
 Citation: Tianmu Ma, Xiaochuan Luo and Tianyou Chai, "Modeling and Hybrid Optimization of Batching Planning System for Steelmaking-continuous Casting Process," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 2, pp. 113-126, 2014.

# Modeling and Hybrid Optimization of Batching Planning System for Steelmaking-continuous Casting Process

Funds:

This work was supported by National Natural Science Foundation of China (69074091, 61174187, 61104174), National Program on Key Basic Research Project (2009CB320601), Program for New Century Excellent Talents in University (NCET-08-0105), 111 Project (B08015).

• This paper investigates the batching problem for steelmaking and continuous casting production in an iron and steel enterprise. The tasks of this problem are to decide how to select slabs and determine their width, how to group the selected slabs into charges and then group the charges into tundishes, how to determine the sequence of charges in each tundish, and how to group tundishes into casts and determine the sequence of tundishes in each cast. The effective decision on the batching problem can help balance the requirements of the sequential process after steelmaking and continuous casting, reduce production cost, and improve slab quality. We first give the mathematical description of the original problem. Based on the analysis of width, we present a decomposition strategy to divide the model into three sub-models, i.e., charge design model, tundish design model and cast design model, while adding relevant objectives and constraints. According to the characteristics of each sub-model, we present hybrid optimization algorithms separately. Computational experiments show the strategy, models and algorithms can generate satisfactory solutions.

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