{"title":"Rating and Control Mechanisms Design in the Program \"Research of Dynamic Systems\"","authors":"A. Alekseev, A. Salamatina, T. Kataeva","doi":"10.1109/CBI.2019.10103","DOIUrl":null,"url":null,"abstract":"Development a computer-based platform for creating automated rating and control systems of any complex objects is particular importance in the business informatics domain. The RDS-program (Research of Dynamic Systems) has been selected as a platform for software prototyping of rating and control systems. To carry out operative prototyping of rating and control systems for complex evaluation of complex objects, the following blocks and libraries were created in the RDS program: a library of data input blocks about the status of individual criteria and a library of information aggregation blocks. A combination of these blocks allows for creating a rating and control system for a complex object of any form, degree and source of uncertainty. To convert quantitative parameters from the phase space to the criterial one, three blocks are implemented for selecting the function type such as: increasing (linear, power, S-shaped, logarithmic, and exponential), decreasing (similar functions, but Z-shaped ones instead of the S-shaped function) and mixed (trapezoidal and bell-shaped). To estimate qualitative parameters, four blocks are implemented for assigning accurate estimates (point values), intervals, fuzzy values without constraints on the membership function or those with special constraints, as well as F-Fuzzy values. The defined matrix mechanisms of complex evaluation are as follows: the tree fuzzy rating mechanism employing an additive-multiplicative approach and two max-min approaches to set theoretic operations, as along with two continuous complex evaluation mechanisms. Six model examples studied in the paper demonstrate procedures of aggregating two criteria with accurate real values and with different uncertainty such as interval values, fuzzy values, and F-Fuzzy values.","PeriodicalId":193238,"journal":{"name":"2019 IEEE 21st Conference on Business Informatics (CBI)","volume":"15 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 21st Conference on Business Informatics (CBI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBI.2019.10103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Development a computer-based platform for creating automated rating and control systems of any complex objects is particular importance in the business informatics domain. The RDS-program (Research of Dynamic Systems) has been selected as a platform for software prototyping of rating and control systems. To carry out operative prototyping of rating and control systems for complex evaluation of complex objects, the following blocks and libraries were created in the RDS program: a library of data input blocks about the status of individual criteria and a library of information aggregation blocks. A combination of these blocks allows for creating a rating and control system for a complex object of any form, degree and source of uncertainty. To convert quantitative parameters from the phase space to the criterial one, three blocks are implemented for selecting the function type such as: increasing (linear, power, S-shaped, logarithmic, and exponential), decreasing (similar functions, but Z-shaped ones instead of the S-shaped function) and mixed (trapezoidal and bell-shaped). To estimate qualitative parameters, four blocks are implemented for assigning accurate estimates (point values), intervals, fuzzy values without constraints on the membership function or those with special constraints, as well as F-Fuzzy values. The defined matrix mechanisms of complex evaluation are as follows: the tree fuzzy rating mechanism employing an additive-multiplicative approach and two max-min approaches to set theoretic operations, as along with two continuous complex evaluation mechanisms. Six model examples studied in the paper demonstrate procedures of aggregating two criteria with accurate real values and with different uncertainty such as interval values, fuzzy values, and F-Fuzzy values.