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A Genetic Algorithm Based on Queen Bee for Scheduling a Flexible Flow Line with Blocking

机译:一种基于女王蜜蜂的遗传算法,用于安排柔性流线阻塞

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This paper presents a comparison between a genetic algorithms (GA) based on queen bee and classical GA for scheduling flexible flow line problem with assuming blocking (FFLB). The proposed heuristics are used to solve a modified version of a mixed-integer mode of the FFLB. The flexible flow line consists of several processing stages in series separated by finite intermediate buffers, in which each stage has one or more identical parallel processors. The objective is to determine a production schedule for all products so as to complete the products in a minimum time (makespan). This paper also uses a novel crossover operator type inspired by the sexual intercourses of honey bees. The method selects a specific chromosome in present population as queen bee with highest fitness. While the selected queen bee is one parent of crossover, all the remaining chromosomes have the chance to be next parent for crossover in each generation once. The model of FFLB is solved by the classical GA (CGA) and queen-bee GA (QGA) and obtained results are compared together. To verify the efficiency of both CGA and QGA, we use a lower bound obtained from the Lingo 8. The results show that the convergence of QGA is faster rather than CGA in same conditions.
机译:本文呈现了基于女王BEE和经典GA的遗传算法(GA)与假设阻塞(FFLB)进行柔性流线问题的比较。拟议的启发式方法用于解决FFLB的混合整数模式的修改版本。柔性流量由有限的中间缓冲器分离的若干处理阶段,其中每个级具有一个或多个相同的并行处理器。目标是确定所有产品的生产计划,以便在最短时间(MakEspan)中完成产品。本文还采用了由蜂蜜蜜蜂的性互际的新型交叉操作员类型。该方法选择当前群体的特定染色体,如同健康最高的女王。虽然所选的女王蜜蜂是一个交叉的父母,但所有剩余的染色体都有机会成为每一代一次交叉的下一个父母。 FFLB的模型由经典GA(CGA)和女王 - 蜂(QGA)求解,并将得到的结果放在一起。为了验证CGA和QGA的效率,我们使用从Lingo 8获得的下限8.结果表明,QGA的收敛性更快而不是CGA在相同条件下。

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