Case Retrieval Mechanism of the Intelligent RCM Analysis System

2010 ◽  
Vol 40-41 ◽  
pp. 686-691
Author(s):  
Zhong Hua Cheng ◽  
Lu Chao Wang ◽  
Li Bo Lv
Keyword(s):  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Analytic Hierarchy ◽  
Retrieval Method ◽  
Analysis Process ◽  
Reliability Centered Maintenance ◽  
Retrieval Mechanism ◽  
Analysis System ◽  
Hierarchy Process

To improve Reliability Centered Maintenance (RCM) analysis efficiency, the Artificial Intelligence (AI) technology, such as case-based reasoning (CBR) is successfully introduced into RCM analysis process and an intelligent RCM analysis (IRCMA) was studied, and an intelligent RCM analysis system (IRCMAS) was developed. The idea for IRCMAS is based on the fact that the historical records of RCM analysis on similar items can be referenced and used for the current RCM analysis of a new item. Case retrieval is the key part of the IRCMAS, of which mechanism has an importance effect on reasoning efficiency of system. In this paper, the IRCMAS is introduced, retrieval mechanism and process of cases are presented, and nearest neighbor retrieval method based on analytic hierarchy process (AHP) is in detail discussed by an example. Design of case retrieval mechanism lays steady foundation for development and realization of intelligent RCM analysis system.

Heterogeneous multi-attribute case retrieval method based on group decision making using incomplete weight information

2021 ◽  
pp. 1-13
Author(s):  
Kai Zhang ◽  
Jing Zheng ◽  
Ying-Ming Wang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Interval Numbers ◽  
Retrieval Method ◽  
Incomplete Weight ◽  
Incomplete Weight Information ◽  
Evaluation Information

Case-based reasoning (CBR) is one of the most popular methods used in emergency decision making (EDM). Case retrieval plays a key role in EDM processes based on CBR and usually functions by retrieving similar historical cases using similarity measurements. Decision makers (DMs), thus, choose the most appropriate historical cases. Although uncertainty and fuzziness are present in the EDM process, in-depth research on these issues is still lacking. In this study, a heterogeneous multi-attribute case retrieval method based on group decision making (GDM) with incomplete weight information is developed. First, the case similarities between historical and target cases are calculated, and a set of similar historical cases is constructed. Six formats of case attributes are considered, namely crisp numbers, interval numbers, linguistic variables, intuitionistic fuzzy numbers, single-valued neutrosophic numbers (NNs) and interval-valued NNs. Next, the evaluation information from the DMs is expressed using single-valued NNs. Additionally, the evaluation utilities of similar historical cases are obtained by aggregating the evaluation information. The comprehensive utilities of similar historical cases are obtained using case similarities and evaluation utilities. In this process, the weights of incomplete information are determined by constructing optimization models. Furthermore, the most appropriate similar historical case is selected according to the comprehensive utilities. Finally, the proposed method is demonstrated using two examples; its performance is then compared with those of other similar methods to demonstrate its validity and efficacy.

Case Representation and Organization Studies on Case-Based Reasoning Maintenance Resources Requirement Analysis

2013 ◽  
Vol 389 ◽  
pp. 698-702
Author(s):  
Xiao Chen ◽  
Ling Chen ◽  
Wo Ye Liu ◽  
Fei Han
Keyword(s):  
Prototype System ◽  
Case Based Reasoning ◽  
Requirement Analysis ◽  
Organization Studies ◽  
Case Representation ◽  
Relation Database ◽  
Analysis Process ◽  
System Perfection ◽  
Analysis System ◽  
Case Based

To improve the efficiency of planning maintenance resources requirement, the artificial intelligent (AI) technology, especially Case-Based Reasoning (CBR) is applied into maintenance resources requirement analysis process, the process is introduced, and the critical techniques of which, such as case representation and organization etc, are discussed in detail, according to the case characteristics, analyzed the cases main ingredient, cases representation and organization which is based on Relation Database and Object Oriented are detailed discussed, the development of case-based maintenance resources requirement analysis prototype system proved the validity of the technique, formed the foundation for the case-based maintenance resources requirement analysis system perfection.

scholarly journals HYBRID FORECASTING SYSTEM BASED ON CASE-BASED REASONING AND ANALYTIC HIERARCHY PROCESS FOR COST ESTIMATION

2013 ◽  
Vol 19 (1) ◽  
pp. 86-96 ◽  
Author(s):  
Sangyong Kim
Keyword(s):  
Analytic Hierarchy Process ◽  
Cost Estimation ◽  
Prediction Models ◽  
Early Stage ◽  
Case Based Reasoning ◽  
Cost Estimating ◽  
Analytic Hierarchy ◽  
Highway Projects ◽  
Hierarchy Process ◽  
Case Based

Cost estimating of highway projects with high accuracy at the early stage of project development is crucial for planning and feasibility studies. Various research have been attempted to develop cost prediction models in the early stage of a construction life cycle. This study uses the hybrid estimating tool to provide an effective cost data management for highway projects and accordingly develops a realistic cost estimating system. This study focused on the development of a more accurate estimate technique for highway projects in South Korea at the early stage using hybrid analytic hierarchy process (AHP) and case-based reasoning (CBR). Real case studies are used to demonstrate and validate the benefits of the proposed approach. It is expected that the developed CBR system is to provide decision-makers with accurate cost information to asses and compare multiple alternatives for obtaining the optimal solution and controlling cost.

A Case-Based Reasoning Combined with AHP Wighting for Cutter Conceptual Configuration Prediction Model

2011 ◽  
Vol 418-420 ◽  
pp. 1919-1924
Author(s):  
Xian Yun Wang ◽  
Jian Qin Liu ◽  
Wei Guo
Keyword(s):  
Analytic Hierarchy Process ◽  
Prediction Model ◽  
Relative Weight ◽  
Case Based Reasoning ◽  
Analytic Hierarchy ◽  
Disc Cutters ◽  
The Analytic Hierarchy Process ◽  
Hierarchy Process ◽  
Case Based

Abstract For complex and difficult geology, it is difficult to design right cutters for TBM in the conventional ways. So the successful experiences and data accumulated are very useful in TBM disc cutters design. CBR is a reasoning methodology that exploits similar experienced solutions, in the form of past cases, to solve new problems. This paper proposes an AHP-Based CBR model that overcomes the difficulty of measuring experience for determining the relative weight of attributes by the analytic hierarchy process. By comparing, the model using the analytic hierarchy process was more accurate, reliable, and explanatory for solving new problems using experience from previous cases.

scholarly journals Forecasting Repair Schedule for Building Components Based on Case-Based Reasoning and Fuzzy-AHP

2019 ◽  
Vol 11 (24) ◽  
pp. 7181
Author(s):  
Sojin Park ◽  
Nahyun Kwon ◽  
Yonghan Ahn
Keyword(s):  
Fuzzy Ahp ◽  
Case Based Reasoning ◽  
Analytic Hierarchy ◽  
Average Case ◽  
Related Information ◽  
Building Maintenance ◽  
Proposed Model ◽  
Building Components ◽  
Hierarchy Process ◽  
Case Based

Building maintenance is closely related to the performance and sustainability of buildings. However, existing approaches to maintenance are limited in terms of estimating required repairs. Therefore, this study proposes a case-based reasoning (CBR)-based model for estimating the time when the first repair will be needed after the completion of construction, even in phases where maintenance-related information is scarce. CBR and fuzzy-analytic hierarchy process (AHP) were employed as research methodologies. A database was established by collecting 257 cases related to maintenance of apartment buildings, and attributes were extracted through literature reviews and expert interviews. Then, attributes were weighted by fuzzy-AHP and case similarities were computed by measuring the Euclidean distance. Similar cases were retrieved based on similarity scores. The model was validated via a comparison of 20 randomly selected test cases with the output of retrieved cases. The results showed that the average case similarities of 3-, 5-, 7-, and 10-nearest neighbors (NN) were 98.05%, 97.86%, 97.73%, and 97.59%, respectively, and mean absolute percentage errors for 3-, 5-, 7-, and 10-NN were mostly lower than 20%, confirming the applicability of the proposed model. The proposed method will help in the preliminary estimation of the repair time of building components.

Using FAHP and CBR to evaluate the intention of adoption of internet banking service: The example of web ATM

2020 ◽  
Vol 39 (3) ◽  
pp. 2869-2879
Author(s):  
Shih-Jui Chang ◽  
Chi-I Hsu ◽  
Chin-Tsai Lin
Keyword(s):  
User Behavior ◽  
Internet Banking ◽  
Three Dimensions ◽  
Case Based Reasoning ◽  
Analytic Hierarchy ◽  
Service Adoption ◽  
Banking Service ◽  
Prediction Mechanism ◽  
Financial Transactions ◽  
Hierarchy Process

This research combines the Fuzzy Analytic Hierarchy Process (FAHP) with Case-Based Reasoning (CBR) to evaluate the intention of adoption of web ATM services. Compared with physical ATM service, web ATM allows users to perform financial transactions over the internet conveniently. Based on literature and considering the characteristics of web ATM, this study constructs a model for web ATM adoption that comprises three dimensions: The knowledge, the potential value, and the security. 222 valid user questionnaires are collected, and factor analysis is used to verify the factor structure of the decision hierarchy. FAHP is then used to calculate the weights of criteria with six experts through pairwise comparisons. Finally, FAHP weights are integrated into a CBR prediction mechanism for evaluating a user’s adoption intention toward web ATM. The results are helpful for financial institutions to understand and to evaluate the user behavior toward internet banking service adoption.

Case-Based Reasoning Model with Genetic Algorithms, Group Decision-Making and Template Reduction

2016 ◽  
Vol 25 (02) ◽  
pp. 1550032 ◽  
Author(s):  
Aijun Yan ◽  
Hairuo Song ◽  
Pu Wang
Keyword(s):  
Decision Making ◽  
Genetic Algorithms ◽  
Group Decision Making ◽  
Nearest Neighbor ◽  
Group Decision ◽  
Small Samples ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Attribute Weights ◽  
Case Based

Case retrieval, case reuse and case retention are critical to the reasoning performance of the traditional case-based reasoning (CBR) model. In this paper, the integrated use of template reduction technology (TR), genetic algorithms (GA), nearest neighbor (NN) rules and group decision-making (GDM) establishes the CBR-GDM model. First, the TR method of the case base is introduced. Then, an attribute weights optimization using GA is discussed in the case retrieval phase. After that, a case reuse method is carried out with NN and GDM. Finally, 10 data sets from UCI are used to carry out a comparison experiment by 5-fold cross-validation. The classification accuracy rate and the classification efficiency are analyzed under the small samples, before and after the data reduction. The results show that, combined with TR, GA and GDM, the pattern classification performance by CBR can be improved.

Predicting the End Temperature of Molten Steel Using CBR

2012 ◽  
Vol 164 ◽  
pp. 7-11 ◽  
Author(s):  
De Hua Liu ◽  
Hong Bing Wang ◽  
An Jun Xu
Keyword(s):  
Molten Steel ◽  
Relative Weight ◽  
Case Based Reasoning ◽  
Analytic Hierarchy ◽  
Steel Material ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational ◽  
Hierarchy Process

Steel is a kind of important material. The accurate control about the end temperature of molten steel has significant impact on the quality of steel material. Case Based Reasoning (CBR) is used to predict the end temperature of molten steel in Argon Oxygen Decarburization (AOD). Grey Relational Degree (GRD) with different weights of attributes is adopted to calculate the similarity between cases. Analytic Hierarchy Process (AHP) is taken to determine the weights of attributes. Multiple Linear Regression (MLR) is applied to compute the relative weight of two different attributes for AHP. Two methods, CBR using AHP with Equal Weights (CBR_AHP_EW) and CBR using AHP with Different Weights (CBR_AHP_DW), are employed to for a comparison. The results show that CBR_AHP_DW is effective in predicting the end temperature of molten steel in AOD and CBR_AHP_DW outperforms CBR_AHP_EW.

Sign in / Sign up

Export Citation Format

Share Document