基于多目标粒子群优化的茶叶产业绩效提升

D. Roy, R. Dasgupta
{"title":"基于多目标粒子群优化的茶叶产业绩效提升","authors":"D. Roy, R. Dasgupta","doi":"10.1109/ICCECE.2017.8526216","DOIUrl":null,"url":null,"abstract":"In this Paper the Multi-Objective Particle Swarm Optimisation has been used to demonstrate ways to improve the efficiency of Tea Industry after implementation in MAT-LAB. The data for Terai Tea Estate has been extracted from the Financial Statements obtained from Tea Board. The ratio functions have been identified, whose maximisation will improve the performance of the organisation. The regression analysis has been performed to estimate the coefficients of two ratio functions. The Pareto Front has been derived for this dual objective function using Multi-Objective Particle Swarm Optimisation. The results have been presented in this paper.","PeriodicalId":325599,"journal":{"name":"2017 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Performance Improvement of Tea Industry with Multi Objective Particle Swarm Optimisation\",\"authors\":\"D. Roy, R. Dasgupta\",\"doi\":\"10.1109/ICCECE.2017.8526216\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this Paper the Multi-Objective Particle Swarm Optimisation has been used to demonstrate ways to improve the efficiency of Tea Industry after implementation in MAT-LAB. The data for Terai Tea Estate has been extracted from the Financial Statements obtained from Tea Board. The ratio functions have been identified, whose maximisation will improve the performance of the organisation. The regression analysis has been performed to estimate the coefficients of two ratio functions. The Pareto Front has been derived for this dual objective function using Multi-Objective Particle Swarm Optimisation. The results have been presented in this paper.\",\"PeriodicalId\":325599,\"journal\":{\"name\":\"2017 International Conference on Computer, Electrical & Communication Engineering (ICCECE)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Computer, Electrical & Communication Engineering (ICCECE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCECE.2017.8526216\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCECE.2017.8526216","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文运用多目标粒子群优化方法,在MAT-LAB中实现了对茶叶行业效率的提高。寺井茶业的数据是从茶叶委员会获得的财务报表中提取的。比率函数已经确定,其最大化将提高组织的绩效。对两个比值函数的系数进行了回归分析。利用多目标粒子群算法,推导了该双目标函数的Pareto Front。本文给出了实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Improvement of Tea Industry with Multi Objective Particle Swarm Optimisation
In this Paper the Multi-Objective Particle Swarm Optimisation has been used to demonstrate ways to improve the efficiency of Tea Industry after implementation in MAT-LAB. The data for Terai Tea Estate has been extracted from the Financial Statements obtained from Tea Board. The ratio functions have been identified, whose maximisation will improve the performance of the organisation. The regression analysis has been performed to estimate the coefficients of two ratio functions. The Pareto Front has been derived for this dual objective function using Multi-Objective Particle Swarm Optimisation. The results have been presented in this paper.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信