柔性作业车间机床与搬运机器人联合调度方法

Joint scheduling method of machine and handling robot in flexible manufacturing workshop

针对当前柔性作业车间机床和搬运机器人单独调度存在的不匹配问题,以车间完工时间为目标,提出基于多代竞争强进化遗传算法的机床与机器人联合调度方法。对多工件、多工序、多机床、多机器人的柔性作业车间联合调度问题进行了描述;考虑了机床生产和机器人搬运的时序约束,建立了最小化车间完工时间的优化模型;使用工序链、机床链及机器人链缠绕的染色体编码方式,将联合调度问题转化为算法优化问题;在遗传算法中引入多代竞争机理和强进化算子,其中多代竞争机理增加了优秀染色体的遗传概率,强进化算子具有保留优秀基因片段和强制差基因进化的能力。经生产实验验证,在15个工件44道工序的调度中,该算法的车间完工时间比标准遗传算法缩短了14.75%;另外,在不同规模的工件生产调度中,与克隆选择算法和标准遗传算法相比,该算法的迭代次数最少、车间完工时间最短。上述实验结果充分证明了多代竞争强进化遗传算法在柔性作业车间生产联合调度中的优越性。

Aiming at the individual scheduling mismatch between machine and handling robot in flexible manufacturing workshop,taking workshop production time as target,a multi generation competition strong evolution genetic algorithm for joint scheduling method of machine and robot was proposed.The flexible manufacturing workshop joint scheduling problem with multiple workpieces,multiple processes,multiple machines and multiple robots was described.The optimization model aiming at minimizing the workshop production time was established,and the time sequence constraints of machine production and robot handling were analyzed.Using the chromosome coding method of process chain,machine chain and robot chain entanglement,the joint scheduling problem was transformed into an algorithm optimization problem.Multi generation competition mechanism and strong evolution operator were introduced into genetic algorithm.Multi generation competition mechanism makes excellent chromosomes have greater genetic probability,and strong evolution operator has the ability to retain excellent gene fragments and force poor gene evolution.The production experiment results show that the workshop production time of the proposed algorithm is 14.75%less than that of the standard genetic algorithm in the scheduling of 15 workpieces and 44 processes.In addition,in the production scheduling of different size workpieces,compared with the clonal selection algorithm and standard genetic algorithm,the proposed algorithm has the least iterations and the shortest workshop production time.The above experimental results fully prove the superiority of multi generation competitive strong evolution genetic algorithm in flexible manufacturing workshop production scheduling.