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Research on the Application of Intelligent Algorithm in the Balance Optimization of Manufacturing Production Line

Author(s)

Lihui Ma

Affiliation(s)

Business School, Shandong University of Technology, Zibo, Shandong, China

Abstract

Against the backdrop of the manufacturing industry’s transformation toward flexibility and intelligence, production line balancing optimization has become a core objective for improving production efficiency and reducing operational costs. Traditional balancing methods rely heavily on static analysis and empirical decision-making, leaving them unable to accommodate production scenarios featuring multiple product variants, small batch sizes, and short delivery cycles. This paper systematically reviews the core optimization objectives and constraints of production line balancing problems, analyzes the underlying mechanisms and implementation pathways of mainstream intelligent optimization algorithms—including genetic algorithms, simulated annealing, particle swarm optimization, and ant colony algorithm in production line balancing, and discusses the advantages and limitations of different algorithms in combination with specific scenarios such as bottleneck station identification, cycle time optimization, and operation element allocation. Through case comparison and application effect analysis, the remarkable results of intelligent algorithms in reducing production cycle time, improving linebalance rate, and enhancing flexible response ability are revealed, providing theoretical basis and practical reference for manufacturing enterprises to achieve dynamic balance and intelligent scheduling of production lines.

Keywords

Intelligent Algorithm; Production Line Balance; Manufacturing; Optimize Scheduling; Genetic Algorithms; Industrial Intelligence

References

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