![](/develop/static/common/images/pdf.png)
基于三层编码遗传算法求解同种产品存在多个工件的FJSP
Solving FJSP of the Same Product with More Than One Job Based on Three-Layer Coding Genetic Algorithm
以往对于柔性作业车间调度的研究, 未考虑一种产品存在多个相同工件, 这不符合车间的实际情况. 为了克服这一缺陷, 同时考虑柔性作业车间调度过程中机器负载及空载状态的能耗, 建立了低碳柔性作业车间调度的多目标混合整数规划数学模型, 以机器能耗成本和总完工时间成本加权和最小为目标. 根据所建模型的特点, 在二层编码遗传算法的基础上, 提出三层编 码遗传算法, 增加一层用于表示同种产品的不同工件. 同时对于三层编码遗传算法 的交叉和变异算子进行重新设计, 并运用柔性作业车间调度的测试算例进行算法有效 性的验证. 然后, 运用该算法求解同种产品存在多个工件的柔性车间调度问题. 最后通过取不同的权重得到101组不同的解, 并基于数据包络分析得到其中的非支配解, 绘制Pareto前沿线, 验证该算法的有效性.
The studies of flexible job shop scheduling did not consider one product with multiple workpieces in the past. This was inconsistent with the actual situation of workshop. In order to overcome this defect, a multi-objective mixed integer programming mathematical model is established to study the low carbon flexible job shop scheduling, which consider the consumption of machine in load and no-load state. It aims at minimizing the weighted sum of machine energy consumption cost and total completion time cost. According to the characteristics of the model, a three-layer coding genetic algorithm is proposed to solve it, based on the two-layer coding genetic algorithm. The crossover and mutation operators of the three-layer coding genetic algorithm are redesigned. And some test examples are used to verify the validity of the algorithm. Then, the flexible job shop scheduling problem for every product with more than one job was solved by the algorithm. Finally, 101 sets of solutions are obtained by using different weights of objects. And the Pareto front line, which is used to evaluate the performance of three-layer coding genetic algorithm, is plotted based on the non-dominant solutions getting from the data envelopment analysis.
低碳调度 / / 柔性作业车间调度 / / 混合整数线性规划 / / 三层编码遗传算法. {{custom_keyword}} /
/
〈 |
|
〉 |