Optimization of CLIA Operation Scheduling Based on Improved Genetic Algorithm
An improved genetic algorithm is proposed to optimize the scheduling of the chemical luminescence immunity analyzer with the objective of minimizing the maximum completion time.Building on the traditional genetic algorithm,a coding method based on job sequencing is introduced.The roulette wheel selection strategy is employed to preserve population diversity.The POX crossover operator is used to optimize the crossover results.The decoding algorithm is improved by incorporating an adaptive module waiting algorithm to address job blocking issues during equipment operation.Experimental results demonstrate that the improved genetic algorithm can arrange the detection sequence of multiple jobs more reasonably,effectively improving the operating efficiency of the CLIA,reducing the total testing time,and exhibiting a high degree of automation.
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