A dynamical teaching-learning-based optimization algorithm for fuzzy energy-efficient parallel batch processing machines scheduling in fabric dyeing process

IF 7.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Soft Computing Pub Date : 2024-11-05 DOI:10.1016/j.asoc.2024.112413

Jing Wang , Debiao Li , Hongtao Tang , Xixing Li , Deming Lei

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引用次数: 0

Abstract

Fabric dyeing is the most time-consuming and energy-intensive process in textile production with some batch processing machines (BPMs) and uncertainty. In this study, a fuzzy energy-efficient parallel BPMs scheduling problem (FEPBSP) with machine eligibility and sequence-dependent setup time (SDST) in fabric dyeing process is investigated, and a dynamical teaching-learning-based optimization algorithm (DTLBO) is proposed to simultaneously optimize the total agreement index, fuzzy makespan, and total fuzzy energy consumption. In DTLBO, multiple classes are constructed by non-dominated sorting. Dynamical class evolution is designed, which incorporates diversified search among students and adaptive self-learning of teachers. The former is implemented using various combinations of the teacher phase and the learner phase, and the latter is achieved through teacher quality and an adaptive threshold. Additionally, a reinforcement local search based on neighborhood structure dynamic selection is also applied. Extensive experiments are conducted, and the computational results demonstrated that the new strategies of DTLBO are effective, and it is highly competitive in solving the considered problem.

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织物染色过程中基于动态教学的模糊节能并行批量处理机调度优化算法

织物染色是纺织生产中最耗时耗能的工序，需要使用一些批量加工机器（BPM），且存在不确定性。本研究研究了织物染色过程中具有机器资格和序列相关设置时间（SDST）的模糊节能并行 BPMs 调度问题（FEPBSP），并提出了一种基于动态教与学的优化算法（DTLBO），以同时优化总协议指数、模糊有效期和总模糊能耗。在 DTLBO 中，通过非优势排序构建多个类。设计了动态班级进化算法，其中包含学生之间的多样化搜索和教师的自适应自学习。前者通过教师阶段和学习者阶段的不同组合来实现，后者则通过教师质量和自适应阈值来实现。此外，还应用了基于邻域结构动态选择的强化局部搜索。实验结果表明，DTLBO 的新策略是有效的，在解决所考虑的问题时具有很强的竞争力。

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来源期刊

Applied Soft Computing 工程技术-计算机：跨学科应用

CiteScore

15.80

自引率

6.90%

发文量

874

审稿时长

10.9 months

期刊介绍： Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.