Large-scale FJSP based on improved multi-group NSGA-Ⅱ algorithm

CHEN Zihao; CHEN Songhang; CHEN Hao

doi:10.13873/J.1000-9787(2021)02-0051-04

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Large-scale FJSP based on improved multi-group NSGA-Ⅱ algorithm

更新时间：2025-09-11

- Large-scale FJSP based on improved multi-group NSGA-Ⅱ algorithm
- Issue 2, Pages: 51-54(2021)
- 作者机构：
  
  1. 福建省复杂动态系统智能辨识与控制实验室泉州装备制造研究所
  2. 中北大学电气与控制工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2021)02-0051-04
  CLC： TH165;TP18
- Published：2021
- 稿件说明：
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CHEN Zihao, CHEN Songhang, CHEN Hao. Large-scale FJSP based on improved multi-group NSGA-Ⅱ algorithm[J]. 2021, (2): 51-54.
DOI：

CHEN Zihao, CHEN Songhang, CHEN Hao. Large-scale FJSP based on improved multi-group NSGA-Ⅱ algorithm[J]. 2021, (2): 51-54. DOI： 10.13873/J.1000-9787(2021)02-0051-04.

摘要

在研究柔性作业车间调度问题(FJSP)中为模拟实际大型生产车间的柔性作业车间调度情景

设计了基于传统基准实例的大规模FJSP数据集。针对大规模FJSP数据集

提出了以最大完工时间、机器最大负荷、机器总负荷为优化指标构建多目标柔性作业车间调度模型

在求解时为避免算法陷入局部最优

提出了一种多种群NSGA-Ⅱ改进算法(IMNSGA-Ⅱ)

该算法优化了种群初始化方法与交叉策略

对不同种群中机器部分的基因采取不同的交叉方式。在生成的大规模FJSP数据集中验证了算法的有效性和可行性。

Abstract

In the study of flexible job-shop scheduling problem（ FJSP）

a large-scale FJSP data set based on the traditional benchmark instance is designed to simulate the flexible job-shop scheduling scenario of the actual largescale production workshop. Aiming at the large-scale FJSP dataset

a multi-objective flexible job-shop scheduling model with the optimization index of maximum completion time

maximum machine load and total machine load is proposed. In order to avoid the algorithm falling into local optimum

an improved multi-group NSGA-Ⅱ algorithm（ IMNSGA-Ⅱ） is proposed. The algorithm optimized the population initialization method and cross-strategy

and adopting different crossover methods for the genes of the machine parts in different populations. Effectiveness and feasibility of the algorithm are verified in the large-scale FJSP dataset.

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