Fuzzy rule-based system for the economic analysis of RFID investments

被引:38
作者
Ustundag, Alp [1 ]
Kilinc, Mehmet Serdar [1 ]
Cevikcan, Emre [1 ]
机构
[1] ITU Management Fac, Dept Ind Engn, TR-34367 Istanbul, Turkey
关键词
Fuzzy rule-based system; RFID implementation; SUPPLY CHAIN; TECHNOLOGY; SUPPORT; MODEL; IDENTIFICATION; MANAGEMENT; INACCURACY; INFERENCE; IMPACT; POWER;
D O I
10.1016/j.eswa.2010.01.009
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Radio frequency identification (RFID) technology introduces the opportunity for increased visibility by facilitating easy tracking and identifying of goods, assets and even living things. The number of RFID applications and users in various fields are growing. However, high investment cost and inadequate technical capability still remain as challenges for RFID system implementations. That being the case, fair evaluation of savings associated with increasing performance and investment costs has a great role in the success of RFID projects. In this study, a systematic framework for the economic analysis for RFID investment is proposed. In this method, the elements of cost and benefits are determined in order to measure the value of an RFID investment. The expected increase of customer order is determined in terms of delivery accuracy and delivery time via a fuzzy rule-based system. The Monte-Carlo simulation method is used to determine the expected net present value (NPV) of RFID investment. A case study is constructed on the basis of expert conception to illustrate the proposed method. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:5300 / 5306
页数:7
相关论文
共 50 条
[31]   Fuzzy rule-based models via space partition and information granulation [J].
Pang, Yunhui ;
Wang, Lidong ;
Liu, Yifei ;
Guo, Jiayi .
NEURAL COMPUTING & APPLICATIONS, 2022, 34 (19) :16199-16211
[32]   Fuzzy Rule-Based Prediction of Gold Prices using News Affect [J].
Hajek, Petr ;
Novotny, Josef .
EXPERT SYSTEMS WITH APPLICATIONS, 2022, 193
[33]   An new interval type-2 hybrid fuzzy rule-based AHP system for supplier selection [J].
Ozturk, Muslum ;
Paksoy, Turan .
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, 2020, 35 (03) :1519-1535
[34]   An improved fuzzy rule-based system using evidential reasoning and subtractive clustering for environmental investment prediction [J].
Yang, Long-Hao ;
Ye, Fei-Fei ;
Liu, Jun ;
Wang, Ying-Ming ;
Hu, Haibo .
FUZZY SETS AND SYSTEMS, 2021, 421 (421) :44-61
[35]   Risk analysis of petroleum transportation using fuzzy rule-based Bayesian reasoning [J].
Alghanmi, Ayman ;
Yang, Zaili ;
Blanco-Davis, Eduardo .
INTERNATIONAL JOURNAL OF SHIPPING AND TRANSPORT LOGISTICS, 2020, 12 (1-2) :39-64
[36]   A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters [J].
Aznarte, Jose Luis ;
Molina, Daniel ;
Sanchez, Ana M. ;
Benitez, Jose M. .
APPLIED INTELLIGENCE, 2011, 34 (03) :386-393
[37]   Fuzzy rule-based models with randomized development mechanisms [J].
Hu, Xingchen ;
Pedrycz, Witold ;
Wang, Dianhui .
FUZZY SETS AND SYSTEMS, 2019, 361 :71-87
[38]   Fuzzy rule-based model for hydropower reservoirs operation [J].
Moeini, R. ;
Afshar, A. ;
Afshar, M. H. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2011, 33 (02) :171-178
[39]   Development of a Reinforcement Learning-based Evolutionary Fuzzy Rule-Based System for diabetes diagnosis [J].
Mansourypoor, Fatemeh ;
Asadi, Shahrokh .
COMPUTERS IN BIOLOGY AND MEDICINE, 2017, 91 :337-352
[40]   Activation and Defuzzification Methods for Fuzzy Rule-Based Systems [J].
Boubekeur Mendil ;
K. Benmahammed .
Journal of Intelligent and Robotic Systems, 2001, 32 :437-444