Improved NSGA2 Algorithm to Solve Multi-Objective Flexible Job Shop Scheduling Problem

2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT) Pub Date : 2020-11-20 DOI:10.1109/ICCSNT50940.2020.9304984

Xu Liang, Yifan Liu, Ming Huang

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引用次数: 1

Abstract

The NSGA2 algorithm is one of the effective methods to solve multi-objective flexible job shop scheduling problems (MOFJSP). An improved NSGA2 algorithm is proposed to solve the MOFJSP model that aims to minimize the maximum completion time, the total workload of all machines, the total workshop carbon emissions, the total workshop energy consumption, and the delivery time. Firstly, the improved algorithm performs neighborhood search and cross-mutation operation respectively according to the nondominated ranking level and randomly generated probability of individuals to balance their local search and global search ability of the algorithm. Then, in order to further enrich the diversity of the population and improve the solving ability of the improved algorithm, an elite retention combined with random retention is proposed to retain the parent individuals. At last, the experiment proves the effectiveness of the improved NSGA2 algorithm for solving multi-objective flexible job shop scheduling problems.

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求解多目标柔性作业车间调度问题的改进NSGA2算法

NSGA2算法是求解多目标柔性作业车间调度问题(MOFJSP)的有效方法之一。提出了一种改进的NSGA2算法来求解以最大完工时间、所有机器总工作量、车间总碳排放、车间总能耗和交货时间最小为目标的MOFJSP模型。首先，改进算法根据个体的非支配排序水平和随机生成概率分别进行邻域搜索和交叉突变操作，平衡算法的局部搜索能力和全局搜索能力;然后，为了进一步丰富种群的多样性，提高改进算法的求解能力，提出了一种精英保留与随机保留相结合的方法来保留亲本个体。最后，通过实验验证了改进的NSGA2算法对于求解多目标柔性作业车间调度问题的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT)

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