[1]轩　华,耿祝新,李　冰.组合缓冲约束下的多目标混合流水线节能调度[J].郑州大学学报(工学版),2025,46(01):17-25.[doi:10.13705/j.issn.1671-6833.2024.04.009]
　XUAN Hua,GENG Zhuxin,LI Bing.Multi-objective Hybrid Flowline Energy-saving Scheduling with Combined Buffer Constraints[J].Journal of Zhengzhou University (Engineering Science),2025,46(01):17-25.[doi:10.13705/j.issn.1671-6833.2024.04.009]
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组合缓冲约束下的多目标混合流水线节能调度()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年01期
页码:
17-25
栏目:
出版日期:
2024-12-23

文章信息/Info

Title:
Multi-objective Hybrid Flowline Energy-saving Scheduling with Combined Buffer Constraints
文章编号:
1671-6833(2025)01-0017-09
作者:
轩　华;  耿祝新;  李　冰
郑州大学 管理学院,河南 郑州 450001
Author(s):
XUAN Hua;  GENG Zhuxin;  LI Bing
School of Management, Zhengzhou University, Zhengzhou 450001, China
关键词:
混合流水线;  改进多目标模因算法;  组合缓冲约束;  不相关并行机;  多目标优化;  节能调度
Keywords:
hybrid flowline;  improved multi-objective memetic algorithm;  combined buffer constraints;  unrelated parallel machine;  multi-objective optimization;  energy-saving scheduling
分类号:
TB491
DOI:
10.13705/j.issn.1671-6833.2024.04.009
文献标志码:
A
摘要:
为解决生产阶段间带有无限缓冲和阻塞两种中间缓冲约束的混合流水线节能调度问题,考虑不相关并行机和多时间约束建立数学模型,结合问题特征提出一种改进多目标模因算法以同时最小化最大完工时间和机器总能耗。采用基于不相关机器分配的矩阵编码方案,利用基于Tent混沌映射的混合初始化策略生成初始元胞数组,全局优化算子应用基于参数的自适应遗传策略改进的非支配排序遗传算法,局部增强搜索算子应用一种融合自适应选择邻域搜索和多目标模拟退火的搜索策略以提高算法搜索能力。通过24种不同规模问题的算例实验,验证了所提算法求解该问题的有效性和优越性。实验结果表明:改进多目标模因算法在平均运行时间241.26 s内得到的平均IGD值为47.89,平均SP值为857.25,均低于其他3种对比算法。改进多目标模因算法所求解集具有较好的收敛性、多样性和分布性。
Abstract:
To solve the hybrid flowline energy-saving scheduling problem with two intermediate buffer constraints, infinite buffer and blocking, between production stages, a mathematical model was formulated by considering the uncorrelated parallel machines and multiple time constraints. Taking into account the characteristics of the problem, an improved multi-objective memetic algorithm (IMOMA) was proposed to minimize simultaneously makespan and total energy consumption of the machines. The algorithm adopted a matrix encoding method based on uncorrelated machine assignment. Using a hybrid initialization strategy based on Tent chaotic map to generate the initial cell array, an non-dominated sorting genetic algorithm improved by parameter-based adaptive genetic strategy was applied for the global optimization operator, and a search strategy integrating adaptive selection neighborhood search and multi-objective simulated annealing was designed for the locally enhanced search operator to improve the algorithm′s search capability. The effectiveness and superiority of the proposed algorithm were verified through case experiments with 24 problem scales. The experimental results showed that the average IGD value of 47.89 and the average SP value of 857.25 obtained by IMOMA within the average running time of 241.26 s were lower than the other three comparison algorithms. So the solution set obtained by IMOMA had better convergence, diversity and distributivity.

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更新日期/Last Update: 2024-12-30