能量感知分布式异构混合流水车间调度问题的改进多目标进化算法

IF 3.1 Q2 ENGINEERING, INDUSTRIAL
Yingli Li, Haibing Liu, Biao Zhang
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引用次数: 0

摘要

研究了具有延迟和能耗标准的分布式异构混合流水车间调度问题。提出了一种基于分解的多目标人工蜂群(MOABC/D)算法来解决调度问题。在MOABC/D算法中,设计了一种结合特定领域启发式规则的三层编码方案,以实现全面的解空间探索。一种包含五个基于关键路径的邻域结构的局部搜索框架,以加强子问题的研究。一种自适应优化策略,集成了基于相似性的优先排序、动态邻域关系和相邻子问题之间的协调信息共享。提出了一种解交换策略,帮助算法跳出局部最优,继续在各个方向上寻找解。综合仿真试验验证了该算法在DHHFSP调度效率和节能之间的平衡能力。它为具有异构资源的复杂分布式制造系统的多目标优化提供了广阔的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Improved Multi-Objective Evolution Algorithm for Energy-Aware Distributed Heterogeneous Hybrid Flowshop Scheduling Problem

Improved Multi-Objective Evolution Algorithm for Energy-Aware Distributed Heterogeneous Hybrid Flowshop Scheduling Problem

Improved Multi-Objective Evolution Algorithm for Energy-Aware Distributed Heterogeneous Hybrid Flowshop Scheduling Problem

Improved Multi-Objective Evolution Algorithm for Energy-Aware Distributed Heterogeneous Hybrid Flowshop Scheduling Problem

Improved Multi-Objective Evolution Algorithm for Energy-Aware Distributed Heterogeneous Hybrid Flowshop Scheduling Problem

This study investigates the distributed heterogeneous hybrid flow-shop scheduling problem (DHHFSP) with the tardiness and energy consumption criteria. A decomposition-based multi-objective artificial bee colony (MOABC/D) algorithm is developed to solve the scheduling problem. In the MOABC/D algorithm, a tri-level encoding scheme combined with domain-specific heuristic rules are designed to enable comprehensive solution space exploration. A local search framework incorporating five novel critical path-based neighbourhood structures to intensify subproblem investigation. An adaptive optimisation strategy integrating similarity-based prioritisation, dynamic neighbourhood relationships, and coordinated information sharing among adjacent subproblems. A solution exchange strategy is proposed to assist the algorithm jump out of the local optimum, and continue searching for solutions in various directions. Comprehensive simulation trials validate the algorithm's ability to balance scheduling efficiency and energy conservation in the DHHFSP. It shows great promise for multi-objective optimisation in complex distributed manufacturing systems with heterogeneous resources.

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来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
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