A Fuzzy Data Envelopment Analysis Framework for Dealing with Uncertainty Impacts of Input–Output Life Cycle Assessment Models on Eco-efficiency Assessment

The uncertainty in the results of input–output-based life cycle assessment models makes the sustainability performance assessment and ranking a challenging task. Therefore, introducing a new approach, fuzzy data envelopment analysis, is critical; since such a method could make it possible to integrate the uncertainty in the results of the life cycle assessment models into the decision-making for sustainability benchmarking and ranking. In this paper, a fuzzy data envelopment analysis model was coupled with an input–output-based life cycle assessment approach to perform the sustainability performance assessment of the 33 food manufacturing sectors in the United States. Seven environmental impact categories were considered the inputs and the total production amounts were identified as the output category, where each food manufacturing sector was considered a decision-making unit. To apply the proposed approach, the life cycle assessment results were formulated as fuzzy crisp valued-intervals and integrated with fuzzy data envelopment analysis model, thus, sustainability performance indices were quantified. Results indicated that majority (31 out of 33) of the food manufacturing sectors were not found to be efficient, where the overall sustainability performance scores ranged between 0.21 and 1.00 (efficient), and the average sustainability performance was found to be 0.66. To validate the current study\u27s findings, a comparative analysis with the results of a previous work was also performed. The major contribution of the proposed framework is that the effects of uncertainty associated with input–output-based life cycle assessment approaches can be successfully tackled with the proposed Fuzzy DEA framework which can have a great area of application in research and business organizations that use with eco-efficiency as a sustainability performance metric

INTELLIGENT TECHNIQUES FOR HANDLING UNCERTAINTY IN THE ASSESSMENT OF NEONATAL OUTCOME

Objective assessment of the neonatal outcome of labour is important, but it is a difficult and challenging problem. It is an invaluable source of information which can be used to provide feedback to clinicians, to audit a unit's overall performance, and can guide subsequent neonatal care. Current methods are inadequate as they fail to distinguish damage that occurred during labour from damage that occurred before or after labour. Analysis of the chemical acid-base status of blood taken from the umbilical cord of an infant immediately after delivery provides information on any damage suffered by the infant due to lack of oxygen during labour. However, this process is complex and error prone, and requires expertise which is not always available on labour wards. A model of clinical expertise required for the accurate interpretation of umbilical acid-base status was developed, and encapsulated in a rule-based expert system. This expert system checks results to ensure their consistency, identifies whether the results come from arterial or venous vessels, and then produces an interpretation of their meaning. This 'crisp' expert system was validated, verified and commercially released, and has since been installed at twenty two hospitals all around the United Kingdom. The assessment of umbilical acid-base status is characterised by uncertainty in both the basic data and the knowledge required for its interpretation. Fuzzy logic provides a technique for representing both these forms of uncertainty in a single framework. A 'preliminary' fuzzy-logic based expert system to interpret error-free results was developed, based on the knowledge embedded in the crisp expert system. Its performance was compared against clinicians in a validation test, but initially its performance was found to be poor in comparison with the clinicians and inferior to the crisp expert system. An automatic tuning algorithm was developed to modify the behaviour of the fuzzy model utilised in the expert system. Sub-normal membership functions were used to weight terms in the fuzzy expert system in a novel manner. This resulted in an improvement in the performance of the fuzzy expert system to a level comparable to the clinicians, and superior to the crisp expert system. Experimental work was carried out to evaluate the imprecision in umbilical cord acid-base parameters. This information, in conjunction with fresh knowledge elicitation sessions, allowed the creation of a more comprehensive fuzzy expert system, to validate and interpret all acid-base data. This 'integrated' fuzzy expert system was tuned using the comparison data obtained previously, and incorporated vessel identification rules and interpretation rules, with numeric and linguistic outputs for each. The performance of each of the outputs was evaluated in a rigorous validation study. This demonstrated excellent agreement with the experts for the numeric outputs, and agreement on a par with the experts for the linguistic outputs. The numeric interpretation produced by the fuzzy expert system is a novel single dimensional measure that accurately represents the severity of acid-base results. The development of the crisp and fuzzy expert systems represents a major achievement and constitutes a significant contribution to the assessment of neonatal outcome.Plymouth Postgraduate Medical Schoo

Perancangan Sistem Penilaian Kinerja Karyawan Dan Pemberian Reward Menggunakan Analytical Hierarchy Process (Ahp) Dan Fuzzy Synthetic Decision Approach (Studi Kasus : Karyawan Administrasi Universitas Diponegoro)

Performance appraisal is an important process in human resources, because of the results of the performance appraisal will be measurable competencies, workplace behavior and employee performance within a specified time period as a basis for consideration in the consideration of the decision in the field of human resources. Diponegoro University also conduct performance measurements for a contract employee, from the results of the assessment will be given a reward in the form of direct compensation, named Performance Improvement Allowance (TPK), and in 2014 changed its name TPK Repairs Allowance Income (TPP). The scoring system is still focused on the aspects of discipline, so that the results obtained are not completely describe the condition of the employee. Therefore, formulate performance assessment consisting of 6 main criteria and 19 sub-criteria are built from a model for assessing the performance of "Annual Performance Appraisal-Temporary Employee (Classified or Administrative and Professional) University of Texas Dallas". Based on the result on the research carried out by using the weighting method AHP (Analytichal Hierarchy Process) weights obtained for kriteia attendance / punctuality (0248), initiative (0234), responsibility and dependence (0194), the quality of work (0139) knowledge work (0111) and interpersonal relationships (0075). Then an assessment with Fuzzy Synthetic pendekatan to get big TPP. TPP is the largest of the calculation is the biggest is Rp. 979.605, while the smallest Rp.396,000 TPP. The amount of the provision of the TPP is affected by the value of the resulting position

Development of a fuzzy-fule-base system with educational applications with case study

In criterion-referenced assessment method (CRA), total score for students’ work is gather by summing up the scores for each main criterion, where total score is overall mark awarded to student for their performed work e.g. assignment, test, project and etc. CRA is a linear assessment method where total score varies in direct proportion to the scores from each main criterion. A fuzzy inference system (FIS) based assessment model is proposed and developed to allow non-linear relationship between total score and score from each main criterion. FIS based assessment model is constructed with expert knowledge, rules collected from human expert are stored in fuzzy rule base for the use of inference. The number of rules increases exponentially as the number of main criteria increase. As a solution to this issue, a rule reduction system (RRS) is developed. The RRS can pin point a set of important rules, and it is suggested that only important rules is collected. A case study is conducted to evaluate the performance of the developed system. Empirical results show that the FIS based assessment model allow the non-linear relation among total score and the scores from each main criterion to be modeled. Besides, experiments show that the developed RRS can reduce the fuzzy rule significantly

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

IMPLEMENTASI LOGIKA FUZZY TAHANI UNTUK MODEL SISTEM PENDUKUNG KEPUTUSAN EVALUASI KINERJA KARYAWAN

The main purpose of employee performance evaluation is to monitor and determine the performance of an employee in a company, whether it is working optimally or not to conduct an assessment of the criteria for the performance of an employee . As for the criteria regarding the evaluation of staff performance will be evaluated in this study was the presence, quality of work , creativity , technical skills , communication skills and attitude . These criteria still have data that is ambiguous ( vague ) . By Tahani fuzzy , ambiguous data that can be processed to remove ambiguity data. The aim of this study is to apply fuzzy logic with Tahani method for evaluating employee performance and yield ranking of employee performance evaluation results . While the outcome of this research is a model of a decision support system for employee performance evaluation with fuzzy logic approach Tahani methods that provide information about the results of the performance evaluation of employees

The Principles Of Developing A Management Decision Support System For Scientific Employees

Employees engaged in mental work have become the most valuable assets of any organization in the 21st century. The satisfaction of those involved in mental work requires the provision of objectivity and transparency in their decision-making. This, in turn, entails the development of scientifically motivated decision making mechanisms and scientific-methodological approaches to evaluate their performance based on innovative technologies.The main goal of this article is in development of the scientific and methodological framework for the establishment of a decision support system to manage the employees engaged in mental work and operating in uncertainty. In this regard, initially, the question of evaluating the activities of scientific workers is examined, its characteristic features are determined, and the fuzzy relation model is proposed as a multi-criterion issue formed in uncertainty. Taking into consideration the hierarchical structure of the criteria that allows evaluating the activities of scientific workers, a phased solution method based on an additive aggregation method is proposed. In accordance with the methodology, a functional scheme of the decision support system to manage the scientific personnel is developed. The working principle of each block and the interaction of the blocks are described. The rules for the employees\u27 management decisions are shown by referring to the knowledge production model.Based on the proposed methodological approach, the implementation phases of the decision support system for the management of the scientific workers of the Institute of Information Technology of ANAS are described. To evaluate the employees\u27 performance, the tools to collect initial information, evaluate the system of criteria, define their importance coefficients and mathematical descriptions are provided. Some results of the system software are presented. The opportunities of the system based on the proposed methodology to support enterprise mangers to make scientifically justified decisions are provided

Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

Fuzzy document classification using ontology based approach for term weighting

With the surge in web corpus, document classification is a vital issue in information retrieval. Term weighting increases the accuracy of classification for documents represented in the vector space model. This paper proposes an ontoTf-idf term weighting method based on the assessment of semantic similarity between the group label and the term. In this paper, a comparative analysis of the performance of the traditional Term Frequency-Inverse Document Frequency (Tf-idf) method and ontoTf-idf method is carried on the WebKB and Reuters-21578 benchmark datasets. The efficiency of ontoTf-idf method is validated with kNN (k nearest neighbor) and Fuzzy kNN classifier on the WebKB and Reuters-21578 datasets. The experimental results obtained with the proposed ontoTf-idf method outperform the Tf-idf method. In the proposed work, distance metrics like Euclidean distance, Cosine similarity, Manhattan distance, and Jaccard co-efficient are applied with Fuzzy kNN classifier on the WebKB and Reuters-21578 dataset