Abstract
The existing production scheduling mode ignores ladle dispatching resulting in the increase of energy consumption in ladle heating and instability in production. Hence, we study the energy-efficient integration optimization of production scheduling and ladle dispatching in this paper. Specifically, a mixed integer linear programming model is formulated to coordinate the time-dependent correlations between them and quantify the energy consumption of them. Moreover, an enhanced migrating birds optimization algorithm (EMBO) is proposed to tackle this NP-hard integration optimization problem. In this proposed algorithm, a three-level rule-based heuristic decoding is designed to achieve the optimal solutions at the given production sequence; well-designed neighborhood structures are appended to intensify exploration; a simulated annealing-based acceptance criterion is hired to escape from local optima. Additionally, a novel competitive mechanism for birds regrouping is developed to increase the population diversity by information exchange between the left and right lines of V-formation. Mass experimental results demonstrate that the proposed EMBO observably outperforms all the compared algorithms, and the proposed integration optimization decreases the energy-consumption by 1.21% in the context of constant production efficiency.
Highlights
The iron and steel sector accounts for no less than 18% of the total industrial energy consumption on a global scale and is regarded as one of the most energy-intensive manufacturing processes [1], [2]
The steelmaking and continuous-casting (SCC) process is a key phase in the whole steel manufacturing process, and the production management of SCC plays a determinant role in energy saving
It should be noted that inspired by this special formation, the migrating birds optimization algorithm (MBO) algorithm does not employ the concepts of the constant angle and depth, but has a hypothetical V-shaped population formed by a leader solution and other solutions in left and right lines following the leader and introduces a benefit mechanism corresponding to the WTS
Summary
The iron and steel sector accounts for no less than 18% of the total industrial energy consumption on a global scale and is regarded as one of the most energy-intensive manufacturing processes [1], [2]. Among the metaheuristics, migrating birds optimization algorithm (MBO), as a new meta-heuristic algorithm inspired by the V-shaped flight formation of migrating birds, has been proved to be effective on energy conservation This algorithm is unique where the benefit mechanism is utilized to replace the poor-quality solution and accelerate the evolution process greatly [29]–[31]. To tackle the integration optimization of production scheduling and ladle dispatching effectively and efficiently, it is recommended to combine the known expertise of heuristic algorithms and optimization abilities of meta-heuristics together. This is the main contribution behind this paper.
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