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A genetic regulatory network based method for multi-objective sequencing problem in mixed-model assembly lines

College of Mechanical Engineering, Donghua University, 2999 North Renmin Road, Shanghai, China

Special Issues: Optimization methods in Intelligent Manufacturing

This research proposes a genetic regulatory network based sequencing method that minimizes multiple objectives including utility work costs, production rate variation costs and setup costs in mixed-model assembly lines. After constructing mathematical model of this multi-objective sequencing problem, the proposed method generates a set of genes to represent the decision variables and develops a gene regulation equation to describe decision variable interactions composed of production constraints and some validated sequencing rules. Moreover, a gene expression procedure that determines each gene’s expression state based on the gene regulation equation is designed. This enables the generation of a series of problem solutions by indicating decision variable values with related gene expression states, and realizes the minimization of weighted sum of multiple objectives by applying a regulatory parameter optimization mechanism in regulation equations. The proposed genetic regulatory network based sequencing method is validated through a series of comparative experiments, and the results demonstrate its effectiveness over other methods in terms of solution quality, especially for industrial instances collected from a diesel engine assembly line.
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Keywords genetic regulatory network; multiple objectives; sequencing problem; mixed-model assembly line; differential equation; gene regulation

Citation: Youlong Lv, Jie Zhang. A genetic regulatory network based method for multi-objective sequencing problem in mixed-model assembly lines. Mathematical Biosciences and Engineering, 2019, 16(3): 1228-1243. doi: 10.3934/mbe.2019059


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