Project Risk Assessment for Customer Relationship Management Using Adaptive Nero Fuzzy Inference System (ANFIS)

2009 ◽  
Author(s):  
Seyed Hossein Iranmanesh ◽  
Seyed Mostafa Alem ◽  
Eisa Maleki Berneti
Keyword(s):  
Risk Assessment ◽  
Customer Relationship Management ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Relationship Management ◽  
Customer Relationship ◽  
Project Risk ◽  
Inference System ◽  
Project Risk Assessment

scholarly journals Risk Assessment and Mitigation Model for Overseas Steel-Plant Project Investment with Analytic Hierarchy Process—Fuzzy Inference System

2018 ◽  
Vol 10 (12) ◽  
pp. 4780 ◽  
Author(s):  
Min-Sung Kim ◽  
Eul-Bum Lee ◽  
In-Hye Jung ◽  
Douglas Alleman
Keyword(s):  
Risk Assessment ◽  
Analytic Hierarchy Process ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Raw Materials ◽  
Decision Makers ◽  
Analytic Hierarchy ◽  
Project Risk ◽  
Inference System ◽  
Hierarchy Process

This paper presents an analytic hierarchy process (AHP)-fuzzy inference system (FIS) model to aid decision-makers in the risk assessment and mitigation of overseas steel-plant projects. Through a thorough literature review, the authors identified 57 risks associated with international steel construction, operation, and transference of new technologies. Pairwise comparisons of all 57 risks by 14 subject-matter experts resulted in a relative weighting. Furthermore, to mitigate human subjectivity, vagueness, and uncertainty, a fuzzy analysis based on the findings of two case studies was performed. From these combined analyses, weighted individual risk soring resulted in the following top five most impactful international steel project risks: procurement of raw materials; design errors and omissions; conditions of raw materials; technology spill prevention plan; investment cost and poor plant availability and performance. Risk mitigation measures are also presented, and risk scores are re-assessed through the AHP-FIS analysis model depicting an overall project risk score reduction. The model presented is a useful tool for industry performing steel project risk assessments. It also provides decision-makers with a better understanding of the criticality of risks that are likely to occur on international steel projects.

A hybrid risk assessment method for mining sector based on QFD, fuzzy inference system, and AHP

2020 ◽  
Vol 39 (5) ◽  
pp. 6047-6058
Author(s):  
Ulas Cinar ◽  
Selcuk Cebi
Keyword(s):  
Risk Assessment ◽  
Fuzzy Inference System ◽  
Quality Function Deployment ◽  
Fuzzy Inference ◽  
Assessment Method ◽  
Working Environment ◽  
Industrial Safety ◽  
Mining Sector ◽  
Inference System ◽  
Conventional Methods

Conventional risk assessment methods are widely used for industrial safety applications. However, there are serious obstacles to their usage as; (i) all of the potential hazards are considered as an independent event, (ii) various risks are identified based on these hazards, (iii) risk magnitudes of these risks are obtained without considering interdependencies among the hazards, and then (iv) the protective measures against the defined risks are taken based on these risk magnitudes. Therefore, conventional methods do not provide any assessment for overall risks in the working environment. Furthermore, although an accident may cause different severity such as loss of working days, loss of limbs, occupational disease, and death, the conventional methods do not consider all potential consequences of any accident, simultaneously. The main objective of this paper is to propose an effective risk assessment approach by using the fuzzy set theory, Analytical Hierarchy Process (AHP), Fuzzy Inference System (FIS), and Quality Function Deployment (QFD) methods to quantify the risk of any hazard considering interdependencies among all potential hazards and consequences in working environment. Within the scope of this research, an application in the mining sector has been presented to illustrate the validation and the effectiveness of the proposed approach**.

scholarly journals Using Artificial Neural Network and Fuzzy Inference System Based Prediction to Improve Failure Mode and Effects Analysis: A Case Study of the Busbars Production

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1444
Author(s):  
Saeed Na’amnh ◽  
Muath Bani Salim ◽  
István Husti ◽  
Miklós Daróczi
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Classical Method ◽  
Fuzzy Model ◽  
Ann Model ◽  
Inference System ◽  
Artificial Neural

Nowadays, Busbars have been extensively used in electrical vehicle industry. Therefore, improving the risk assessment for the production could help to screen the associated failure and take necessary actions to minimize the risk. In this research, a fuzzy inference system (FIS) and artificial neural network (ANN) were used to avoid the shortcomings of the classical method by creating new models for risk assessment with higher accuracy. A dataset includes 58 samples are used to create the models. Mamdani fuzzy model and ANN model were developed using MATLAB software. The results showed that the proposed models give a higher level of accuracy compared to the classical method. Furthermore, a fuzzy model reveals that it is more precise and reliable than the ANN and classical models, especially in case of decision making.

scholarly journals A Novel Method for Navigational Risk Assessment in Wind Farm Waters Based on the Fuzzy Inference System

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Peilin Lv ◽  
Rong Zhen ◽  
Zheping Shao
Keyword(s):  
Risk Assessment ◽  
Fuzzy Inference System ◽  
Wind Farm ◽  
Fuzzy Inference ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Inference System ◽  
Natural Factors

Offshore wind power is an effective way to solve the energy crisis problem and achieve sustainable economic development. Aiming at the problems that the navigational risk of ships in the waters of offshore wind farms is difficult to quantify due to complex factors, this paper proposes a method of navigational risk assessment in the waters of offshore wind farms based on a fuzzy inference system. Firstly, through the analysis of the factors affecting the navigation system of wind farm waters, it is found that the navigational risk is affected by natural factors and navigational environment factors. Then, the visibility, the number of traffic flows, the number of encounter areas, and the distance between the sailing route and the wind farm are extracted to evaluate the risk of natural factors and the risk of the sailing environment in the navigation system of the wind farm waters, respectively. Considering the mutual influence of the factors, the fuzzy inference rules of navigational risk influence are established according to the expert experience, and a method of navigational risk assessment based on the fuzzy inference system in offshore wind farm waters is developed. In order to verify the effectiveness of the proposed method, a comprehensive evaluation of the navigational risk of wind farm waters in Changle offshore sea of Fujian Province is carried out, and the evaluation results are consistent with the actual situation. The proposed method has important theoretical significance for the navigational safety supervision of offshore wind farm waters.

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