Performance measurement and evaluation of suppliers in supply chain: an evolutionary fuzzy‐based approach

2004 ◽  
Vol 15 (8) ◽  
pp. 723-734 ◽  
Author(s):  
Rajkumar Ohdar ◽  
Pradip Kumar Ray
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Evaluation Process ◽  
Rule Base ◽  
Inference System ◽  
Process Plant ◽  
Measurement And Evaluation ◽  
Linguistic Assessment ◽  
Optimal Set ◽  
Relevant Factors

In order to ensure the uninterrupted supply of items, the purchasing manager needs to evaluate suppliers' performance periodically. The evaluation process typically consists of identifying the attributes and factors relevant to the decision, and measuring the performance of a supplier by considering the relevant factors. Linguistic assessment of suppliers may be carried out based on several criteria. In this paper, an attempt has been made to evaluate the suppliers' performance by adopting an evolutionary fuzzy system. One of the key considerations in designing the proposed system is the generation of fuzzy rules. A genetic algorithm‐based methodology is developed to evolve the optimal set of fuzzy rule base, and a fuzzy inference system of the MATLAB fuzzy logic toolbox is used to assess the suppliers' performance. The proposed methodology, illustrated with the data collected in a process plant, provides acceptable results in determining the suppliers' performance score.

Enhancing Fuzzy Inference System-Based Criterion-Referenced Assessment with a Similarity Reasoning Technique

2012 ◽  
pp. 302-327
Author(s):  
Tze Ling Jee ◽  
Kai Meng Tay ◽  
Chee Khoon Ng
Keyword(s):  
Fuzzy Inference System ◽  
Analogical Reasoning ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Large Set ◽  
Inference System ◽  
Similarity Reasoning ◽  
Criterion Referenced ◽  
Reasoning Technique

A search in the literature reveals that the use of fuzzy inference system (FIS) in criterion-referenced assessment (CRA) is not new. However, literature describing how an FIS-based CRA can be implemented in practice is scarce. Besides, for an FIS-based CRA, a large set of fuzzy rules is required and it is a rigorous work in obtaining a full set of rules. The aim of this chapter is to propose an FIS-based CRA procedure that incorporated with a rule selection and a similarity reasoning technique, i.e., analogical reasoning (AR) technique, as a solution for this problem. AR considers an antecedent with an unknown consequent as an observation, and it deduces a conclusion (as a prediction of the consequent) for the observation based on the incomplete fuzzy rule base. A case study conducted in Universiti Malaysia Sarawak is further reported.

Enhancing Quality of Learning Experience Through Intelligent Agent in E-Learning

2017 ◽  
Vol 25 (01) ◽  
pp. 31-52
Author(s):  
S. Bhattacharya ◽  
S. Chowdhury ◽  
S. Roy
Keyword(s):  
Fuzzy Inference ◽  
Learning Experience ◽  
Fuzzy Rule ◽  
School Level ◽  
Rule Base ◽  
Learning Tools ◽  
Inference System ◽  
Fuzzy Rule Base ◽  
Quality Of Learning

In this paper an interactive recommending agent is proposed which helps an e-learner to enhance the quality of learning experience resulting in efficient achievement of learning objectives. The agent achieves this with the help of a fuzzy rule base working on a variety of learning materials and recommending the appropriate learning path through them. In a learner-centric environment the learning behaviour of a learner may vary to a great extent due to the characteristics of the learner and his environment. Students are often misled while choosing the appropriate path of web learning tools owing to non-availability of a human teacher/guide. By the response of a learner to different positive and negative motivation factors the proposed system employs a fuzzy machine that is fed with realization parameters e.g. Satisfied, Depressed etc. The fuzzy machine working on the paradigm of fuzzy inference system processes these realization parameters with the help of a fuzzy rule base to produce the crisp measures of the learner’s cognitive states in terms of Belief, Behaviour and Attitude. On the basis of these defuzzified crisp diagnostic parameters the proposed system will enhanced the quality of learning experience of an e-learner. To ensure this the system will provide more detailed discussion on the subject matter along with some additional learning tools. Learners often get confused to select the proper tools among various. Therefore the proposed system will also suggest most popular path among those learners with the same understanding. This recommendation comes from the analysis of data mining result. The system was tested with a wide variety of school-level students. The response obtained indicates that it is able to enhance the quality of learning experience through its recommendation.

scholarly journals Z Number Based Fuzzy Inference System for Dynamic Plant Control

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Rahib H. Abiyev
Keyword(s):  
Transient Response ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Response Characteristic ◽  
Rule Base ◽  
Inference System ◽  
Dynamic Plant ◽  
Comparative Results

Frequently the reliabilities of the linguistic values of the variables in the rule base are becoming important in the modeling of fuzzy systems. Taking into consideration the reliability degree of the fuzzy values of variables of the rules the design of inference mechanism acquires importance. For this purpose, Z number based fuzzy rules that include constraint and reliability degrees of information are constructed. Fuzzy rule interpolation is presented for designing of an inference engine of fuzzy rule-based system. The mathematical background of the fuzzy inference system based on interpolative mechanism is developed. Based on interpolative inference process Z number based fuzzy controller for control of dynamic plant has been designed. The transient response characteristic of designed controller is compared with the transient response characteristic of the conventional fuzzy controller. The obtained comparative results demonstrate the suitability of designed system in control of dynamic plants.

Contractor default prediction model for surety bonding1This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management.

2012 ◽  
Vol 39 (9) ◽  
pp. 1027-1042 ◽  
Author(s):  
Adel Awad ◽  
Aminah Robinson Fayek
Keyword(s):  
Prediction Model ◽  
Fuzzy Inference ◽  
Evaluation Criteria ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Self Assessment ◽  
Inference System ◽  
Management Evaluation ◽  
Novel Approach ◽  
Default Prediction

Contractor default is one of the major risks that threaten a project’s success in the construction industry. Previous studies have focused mainly on evaluation of the contractor’s financial aspects to predict contractor default. There remains a need for a comprehensive model that has the ability to incorporate the evaluation of all the project aspects, project team, contractual risks, and project management evaluation criteria to predict the possibility of a contractor’s default on a specific construction project. This paper presents a contractor default prediction model (CDPM) from the surety bonding perspective that incorporates these criteria and uses a fuzzy inference system for reasoning. The CDPM provides a more objective, structured, and comprehensive approach for contractor default prediction for surety practitioners, project owners, and for self-assessment by contractors to reduce the risk of contractor default. The multi-attribute utility function was used to develop a group consensus system (GCS) to aggregate the participating experts’ opinions to build the CDPM. The accuracy of the GCS was found to be 91.1%. A novel approach for fuzzy rule base development is applied to develop the rule base for the CDPM. The CDPM was validated using 30 contractor default prediction cases, and the accuracy was found to be 86.5%.

Industry 4.0 road mapping: A fuzzy linguistic approach

2021 ◽  
Vol 01 ◽  
Author(s):  
Kerem Elibal ◽  
Eren Özceylan
Keyword(s):  
Expert Opinion ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Research Direction ◽  
Future Research ◽  
Rule Base ◽  
Inference System ◽  
Road Mapping

Background: The industry 4.0 transition is becoming crucial for organizations. The literature reviewed showed that whilst there are many studies on industry 4.0 assessment that help organizations evaluate their current state, limited studies exist for road-mapping activities. Objective: The main aim of this study is to construct a model that leads organizations to their fourth industrial revolution transition. Companies, especially small and medium-sized ones (SMEs), need clear, agile, and efficient road maps because of their limited resources. Lack of a procedure that guides organizations in the right way is the motivation of this study. Method: A linguistic fuzzy inference system is used in this study. Concepts are determined, and relations between concepts with if-then rules have been constructed according to the expert opinion. MATLAB R2015a is used for the inference system. Results: An exemplary case is considered, and the results show that the inference system can provide company-specific roadmaps. To which extend an industry 4.0 concept should be taken into account for a company can be seen with the proposed method. Conclusion: The proposed method showed that specific and agile roadmaps could be obtained. Because of the dependency of expert opinion for the fuzzy rule base, different methods for obtaining rules and relations may be a future research direction.

Query-focused multi-document text summarization using fuzzy inference

2021 ◽  
pp. 1-12
Author(s):  
Raksha Agarwal ◽  
Niladri Chatterjee
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Text Summarization ◽  
Rule Base ◽  
Decision Variable ◽  
Inference System ◽  
Fuzzy Rule Base ◽  
Integer Linear Programming Problem ◽  
Processing Step ◽  
Small Set

The present paper proposes a fuzzy inference system for query-focused multi-document text summarization (MTS). The overall scheme is based on Mamdani Inferencing scheme which helps in designing Fuzzy Rule base for inferencing about the decision variable from a set of antecedent variables. The antecedent variables chosen for the task are from linguistic and positional heuristics, and similarity of the documents with the user-defined query. The decision variable is the rank of the sentences as decided by the rules. The final summary is generated by solving an Integer Linear Programming problem. For abstraction coreference resolution is applied on the input sentences in the pre-processing step. Although designed on the basis of a small set of antecedent variables the results are very promising.

scholarly journals Generalized calibration of the Hargreaves equation for evapotranspiration under different climate conditions

2014 ◽  
Vol 9 (No. 2) ◽  
pp. 83-89 ◽  
Author(s):  
J. Patel ◽  
H. Patel ◽  
C. Bhatt
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Calibration Method ◽  
Irrigation Scheduling ◽  
Maximum Temperature ◽  
Accurate Estimation ◽  
Rule Base ◽  
Inference System ◽  
Climate Conditions ◽  
Hargreaves Equation

Accurate estimation of evapotranspiration (ETo) is a key factor in weather-based irrigation scheduling methods. To estimate ETo using the Hargreaves equation, just the data on the minimum and maximum temperature and solar radiation are required. However, this procedure cannot offer consistent accuracy for different climate conditions. To attain the accuracy, calibration of the equation constants (C<sub>H</sub>and E<sub>H</sub>) for different climate conditions have successfully been attempted by many researchers. Because these calibration procedures are lengthy and location-specific, there is a need of a generalized calibration method to make the Hargreaves equation more pertinent and effective. In this paper, fuzzy logic based calibration method for the Hargreaves equation is proposed and validated. The fuzzy inference system is developed to compute appropriate values of the constants C<sub>H</sub>and E<sub>H</sub> on the basis of past data on humidity and wind velocity of a selected location. The underlying relationship between weather conditions and the best values of the constants C<sub>H</sub>and E<sub>H</sub> are used to establish a fuzzy rule base. The performance of the method is checked at eight geographically different locations of India with diverse climate conditions. The Mean Absolute Error (MAE) in ETovalues estimated by the calibrated modified Hargreaves equation and the Penman-Monteith (PM) equation is in the range of 0.3220&ndash;1.0325. It is far more lower than if the error is calculated using the original Hargreaves equation. It confirms the correctness of the calibration method for different climate conditions.

Intelligent cooperative collision avoidance via fuzzy potential fields

Robotica ◽  
2021 ◽  
pp. 1-20
Author(s):  
Daegyun Choi ◽  
Anirudh Chhabra ◽  
Donghoon Kim
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Fuzzy Inference ◽  
Potential Fields ◽  
Rule Base ◽  
Local Minima ◽  
Inference System ◽  
Aerial Vehicles ◽  
Different Shapes ◽  
Simulation Results

Summary This paper proposes an intelligent cooperative collision avoidance approach combining the enhanced potential field (EPF) with a fuzzy inference system (FIS) to resolve local minima and goal non-reachable with obstacles nearby issues and provide a near-optimal collision-free trajectory. A genetic algorithm is utilized to optimize parameters of membership function and rule base of the FISs. This work uses a single scenario containing all issues and interactions among unmanned aerial vehicles (UAVs) for training. For validating the performance, two scenarios containing obstacles with different shapes and several UAVs in small airspace are considered. Multiple simulation results show that the proposed approach outperforms the conventional EPF approach statistically.

Fuzzy-Rule Based Adaptive Data Warehouse

2012 ◽  
Vol 3 (1) ◽  
pp. 47-65 ◽  
Author(s):  
Rajdev Tiwari ◽  
Anubhav Tiwari ◽  
Manu Pratap Singh
Keyword(s):  
Decision Making ◽  
Fuzzy Inference ◽  
Knowledge Workers ◽  
Fuzzy Rule ◽  
Data Warehouses ◽  
Large Reservoir ◽  
Inference System ◽  
Rule Based ◽  
Neuro Fuzzy ◽  
New Generation

Data Warehouses (DWs) are aimed to empower the knowledge workers with information and knowledge which helps them in decision making. Technically, the DW is a large reservoir of integrated data that does not provide the intelligence or the knowledge demanded by users. The burden of data analysis and extraction of information and knowledge from integrated data still lies upon the analyst’s shoulder. The overhead of analysts can be taken off by architecting a new generation data warehouses systems those shall be capable of capturing, organizing and representing knowledge along with the data and information in it. This new generation DW may be called as Knowledge Warehouse (KW) shall exhibit decision making capabilities themselves and can also supplement the Decision Support Systems (DSS) in making decisions quickly and effortlessly. This paper proposes and simulates a fuzzy-rule based adaptive knowledge warehouse with capabilities to learn and represent implicit knowledge by means of adaptive neuro fuzzy inference system (ANFIS).

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