With the rapid development of the industry sector, the assembly line has also experienced development, whose rationality determines the production efficiency of industrial production. In this paper, in order to address the balancing issue of assembly lines for industrial production, a combinatorial optimization method is proposed for the optimization and implementation of the assembly line based on the improved genetic algorithm (GA) and system simulation. By being applied to the assembly line of a company's chiller, our method is proved practical and effective.

Concerning the inverse job-shop scheduling problem (JSP), this paper proposes a hybrid solution based on genetic algorithm (GA) and improved particle swarm optimization (PSO), with the aim to minimize the parameter adjustment. The solution was presented as a block coding plan with decimal mechanism, under which both processes and parameters can be optimized simultaneously. To enhance the local search ability of the proposed algorithm, four neighbourhood structures were designed, and an adaptive selection mechanism was created to select the most suitable neighbourhood. Finally, the proposed algorithm was proved valid through discrete event simulation (DES) and comparison with other algorithms.

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