融合蚁群算法框架下区域资源的动态调度模型  

A dynamic scheduling model for regional resources under the framework of fusion ant colony algorithm

作　　者：吴炜[1,2] 尹秋旎 朱俊 胡振 龙晨 WU Wei;YIN Qiuni;ZHU Jun;HU Zhen;LONG Chen(School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,China;EHV Company,State Grid Hubei Electric Power Co.,Ltd.,Wuhan 430050,China)

机构地区：[1]武汉大学电气与自动化学院,湖北武汉430072 [2]国网湖北省电力有限公司超高压公司,湖北武汉430050

出　　处：《电子设计工程》2024年第20期44-49,共6页Electronic Design Engineering

基　　金：国家自然科学基金项目(72072101)。

摘　　要：针对目前电力作业人力资源调度主要依赖于人工经验的现状,文中提出了基于改进蚁群算法的区域资源动态调度模型。该模型以花费总时间与总成本最小为优化目标,考虑了任务的资源需求、先后顺序等方面的约束条件。同时,结合区域资源动态调度模型的特性,对传统蚁群算法进行了改进设计,提出了优化的编码解码方法、蚁群动态更新机制及2-opt局部搜索方法等策略。算例分析结果表明,所提改进蚁群算法相比于传统蚁群算法具有更快的收敛速度和更高的计算准确度。在实际电力作业调度中,对比传统蚁群算法,所设计模型能够减少总时间约12%,降低总成本约10%。In response to the current situation that human resource scheduling in power operations mainly relies on manual experience,a regional resource dynamic scheduling model is proposed based on improved ant colony algorithm.The model aims to minimize the total time spent and total cost,taking into account constraints such as resource requirements and sequencing of tasks.At the same time,based on the characteristics of the regional resource dynamic scheduling model,traditional ant colony algorithms have been improved and designed,and strategies such as optimized encoding and decoding methods,ant colony dynamic update mechanism,and 2-opt local search method have been proposed.Through example analysis,it is shown that the proposed improved ant colony algorithm has faster convergence speed and higher computational accuracy compared to traditional ant colony algorithms.In actual power job scheduling,compared to traditional ant colony algorithm,the designed model can reduce the total time by about 12%and reduce the total cost by about 10%.

关 键 词：蚁群算法 人力资源调度 编解码方法 动态更新机制 局部搜索 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置] TN99[自动化与计算机技术—控制科学与工程]