{"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}
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.