基于改进遗传算法的CLIA运行调度优化

Optimization of CLIA Operation Scheduling Based on Improved Genetic Algorithm

针对化学发光免疫分析仪的运行调度优化问题,以最小化最长完工时间为优化目标,提出了一种改进的遗传算法。在传统遗传算法的基础上,引进了一种基于工件排列的编码方法;采用轮盘赌选择策略保留了种群多样性;利用POX交叉算子优化了交叉结果;对于解码算法的改进,通过加入自适应模块等待算法,解决了设备运行时出现的工件阻塞问题。结果表明,改进的遗传算法可以更合理安排多种工件的检测顺序,有效提高CLIA的运行效率,缩短检测的总时长,具有自动化程度高的优点。

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.