Multi-Objective Constrained Optimization Model and Molten Iron Allocation Application Based on Hybrid Archimedes Optimization Algorithm

The challenge of distributing molten iron involves the optimal allocation of blast furnace output to various steelmaking furnaces, considering the blast furnace’s production capacity and the steelmaking converter’s consumption capacity. The primary objective is to prioritize the distribution from th...

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Bibliographic Details
Published inMathematics (Basel) Vol. 12; no. 16; p. 2437
Main Authors Hu, Huijuan, Shi, Shichao, Xu, He
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2024
Subjects
Algorithms
Constraints
Continuous casting
Decomposition
Dynamic programming
Efficiency
Furnaces
Genetic algorithms
Hot blast
Integer programming
iron and steel balance
Iron and steel making
Lagrange multiplier
Linear programming
Logistics
molten iron allocation
Multiple objective analysis
Nonlinear programming
optimization algorithm
Optimization algorithms
Optimization models
Planning
Production capacity
quadratic programming
Scheduling
Steel converters
Steel industry
Steel production
Weighting methods
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Summary:The challenge of distributing molten iron involves the optimal allocation of blast furnace output to various steelmaking furnaces, considering the blast furnace’s production capacity and the steelmaking converter’s consumption capacity. The primary objective is to prioritize the distribution from the blast furnace to achieve a balance between iron and steel production while ensuring that the volume of hot metal within the system remains within a safe range. To address this, a constrained multi-objective nonlinear programming model is abstracted. A linear weighting method combines multiple objectives into a single objective function, while the Lagrange multiplier method addresses constraints. The proposed hybrid Archimedes optimization algorithm effectively solves this problem, demonstrating significant improvements in time efficiency and precision compared to existing methods.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12162437