混合多策略MHSSA智能优化风电拉挤板生产排程

doi:10.3969/j.issn.1007-7375.240042

工业工程 ›› 2024, Vol. 27 ›› Issue (3): 114-129.doi: 10.3969/j.issn.1007-7375.240042

• 智能制造系统与车间调度优化 • 上一篇下一篇

混合多策略MHSSA智能优化风电拉挤板生产排程

张志伟¹, 李洛平², 杨晓英^1,3, 杨欣⁴

1. 河南科技大学机电工程学院，河南洛阳 471003;
2. 洛阳双瑞橡塑科技有限公司，河南洛阳 471031;
3. 机械装备先进制造河南省协同创新中心，河南洛阳 471003;
4. 河南科技大学商学院，河南洛阳 471023

收稿日期:2024-01-23 发布日期:2024-07-12
通讯作者: 杨晓英 (1965—)，女，江苏省人，教授，博士，博士生导师，主要研究方向为工业工程与智能制造等。Email: lyyxy111@163.com E-mail:lyyxy111@163.com
作者简介:张志伟 (1998—)，男，浙江省人，硕士研究生，主要研究方向为生产智能排程与智能优化算法
基金资助:
国家重点研发计划资助项目 (2018YFB1701205)；企业委托项目 (HX20221116)

Intelligent Optimization of Wind Turbine Extrusion Plates Production Scheduling Using a Hybrid Multi-strategy Multi-objective Heuristic Sparrow Search Algorithm

ZHANG Zhiwei¹, LI Luoping², YANG Xiaoying^1,3, YANG Xin⁴

1. School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China;
2. Luoyang Sunrui Rubber & Plastic Science and Technology Co., Ltd, Luoyang 471031, China;
3. Henan Collaborative Innovation Center of Advanced Manufacturing of Mechanical Equipment, Luoyang 471003, China;
4. School of Business, Henan University of Science and Technology, Luoyang 471023, China

Received:2024-01-23 Published:2024-07-12

摘要/Abstract

摘要： 针对带序列相关调整时间和顺序齐套约束的风电拉挤板生产排程问题，构建了最小化设备负荷偏差、交货期偏差和最大化设备利用率的多目标优化模型，改进并设计了基于Pareto寻优和拥挤度计算机制的多目标启发式麻雀搜索算法 (multi-objective heuristic sparrow search algorithm, MHSSA)。算法具有“组件-区域”两层编码方式和“倒排-修复-优化”启发式解码算子；采用多规则结合反向学习的改进种群初始化策略，增强了全局搜索能力；利用具有交叉算子和外部存档扰动机制的改进搜索策略，提升了寻优精度和种群多样性。通过数据集测试和实例仿真分析，验证了排程优化模型和智能排程算法的有效性。

关键词: 风电, 拉挤板, 生产排程, 多目标优化, 麻雀搜索算法（SSA）

Abstract: In order to address the production scheduling problem of wind turbine extrusion plates (PSP-WTEP) with sequence-dependent adjustment time and sequential alignment constraints, a multi-objective optimization model is developed to minimize equipment load deviation, delivery time deviation and maximize equipment utilization. A modified multi-objective heuristic sparrow search algorithm (MHSSA) is designed based on the mechanisms of Pareto optimization and crowding distance calculation. A two-layer encoding strategy of "component-region" and a heuristic decoding operator of "inversion-repair-optimization" are incorporated in the algorithm. An improved population initialization strategy, which combines multiple rules with opposition-based learning, is adopted to enhance the global search capability of the algorithm. Additionally, an improved search strategy with crossover operators and an external archive disturbance mechanism is utilized to enhance the optimization accuracy and population diversity of the algorithm. The effectiveness of the proposed optimization model and intelligent scheduling algorithm is verified through dataset testing and instance simulation analysis.

Key words: wind turbine, extrusion plates, production scheduling, multi-objective optimization, sparrow search algorithm (SSA)

中图分类号:

F406.2
TB497

张志伟, 李洛平, 杨晓英, 杨欣. 混合多策略MHSSA智能优化风电拉挤板生产排程[J]. 工业工程, 2024, 27(3): 114-129.

ZHANG Zhiwei, LI Luoping, YANG Xiaoying, YANG Xin. Intelligent Optimization of Wind Turbine Extrusion Plates Production Scheduling Using a Hybrid Multi-strategy Multi-objective Heuristic Sparrow Search Algorithm[J]. Industrial Engineering Journal, 2024, 27(3): 114-129.

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混合多策略MHSSA智能优化风电拉挤板生产排程

Intelligent Optimization of Wind Turbine Extrusion Plates Production Scheduling Using a Hybrid Multi-strategy Multi-objective Heuristic Sparrow Search Algorithm

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