1,984 research outputs found
Architecture value mapping: using fuzzy cognitive maps as a reasoning mechanism for multi-criteria conceptual design evaluation
The conceptual design phase is the most critical phase in the systems engineering life cycle. The design concept chosen during this phase determines the structure and behavior of the system, and consequently, its ability to fulfill its intended function. A good conceptual design is the first step in the development of a successful artifact. However, decision-making during conceptual design is inherently challenging and often unreliable. The conceptual design phase is marked by an ambiguous and imprecise set of requirements, and ill-defined system boundaries. A lack of usable data for design evaluation makes the problem worse. In order to assess a system accurately, it is necessary to capture the relationships between its physical attributes and the stakeholders\u27 value objectives. This research presents a novel conceptual architecture evaluation approach that utilizes attribute-value networks, designated as \u27Architecture Value Maps\u27, to replicate the decision makers\u27 cogitative processes. Ambiguity in the system\u27s overall objectives is reduced hierarchically to reveal a network of criteria that range from the abstract value measures to the design-specific performance measures. A symbolic representation scheme, the 2-Tuple Linguistic Representation is used to integrate different types of information into a common computational format, and Fuzzy Cognitive Maps are utilized as the reasoning engine to quantitatively evaluate potential design concepts. A Linguistic Ordered Weighted Average aggregation operator is used to rank the final alternatives based on the decision makers\u27 risk preferences. The proposed methodology provides systems architects with the capability to exploit the interrelationships between a system\u27s design attributes and the value that stakeholders associate with these attributes, in order to design robust, flexible, and affordable systems --Abstract, page iii
Risk-based system to control safety level of flooded passenger ships
Predicting the consequences of flooding is a key issue that may help the ship master of a large passenger ship to make rational decisions in emergency situations. To this end, the Delphi Emergency Decision Support System (Delphi EDSS) has been designed and is under implementation to continuously assess ship's state of survivability. Analyses are performed by means of a time-domain simulation program, where transient stages of flooding are investigated and stored off-line for all the potential damage scenarios. The Delphi EDSS evaluates the ship risk level including the most important aspects related to safety state while establishing the time-to-capsize which is of primary concern for the safe evacuation of the damaged ship.
The methodology is based on a scientific approach, setting an overall platform for rational assessment of non-survivability risk. Determination of the global risk level and its components requires solution of a multicriterial problem, where the level of importance of each criterion contributing to determination of a global risk index is combined with fuzzified contributors to risk calculated at lower levels
Fuzzy AHP Method for Selection of a Suitable Seismic Retrofitting Alternative in Low-Rise Buildings
Decision making for selecting an appropriate alternative among nominated alternatives is still a problem among retrofit designers. It is clear that selected alternative should comply the current codes in terms of structural criteria, but the other criteria may not be considered. The main goal of this study is to introduce a suitable method for making a decision in order to find the best alternative considering the effective criteria in retrofitting of low-rise buildings. Analytic Hierarchy Process (AHP), as a technique of Multi-Criteria Decision Making (MCDM), is compatible to solve the problem. Effective criteria have been categorized to structural, operational, economic and functional criteria and sixteen sub-criteria considered as a pattern that satisfies the entire involved group including structural and architectural engineers, contractor, client, and authorities in retrofitting of low-rise buildings. Since most of the involved criteria such as aesthetic, durability, and compatibility have fuzzy nature and cannot be compared numerically, fuzzy AHP can be a compatible method for comparison different retrofitting alternatives among both fuzzy and non-fuzzy criteria. A matrix of pair-wise comparison (MPC) is used for determining the weight of criteria and also for scoring the alternatives respect to each criterion. A Fuzzy Importance scale with Triangular Fuzzy Numbers (TFN) is applied for comparing the criteria. The method is examined by a case study and the results show the used method can help designers for selecting the appropriate alternative
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
DATA CLASSIFICATION SYSTEM WITH FUZZY NEURAL BASED APPROACH
Knowledge Discovery in Database and Data Mining use techniques derived from
machine learning, visualization and statistics to investigate real world data. Their aim is
to discover patterns within the data which are new, statistically valid, interesting and
understandable.
In recent years, there has been an explosion in computation and information technology.
With it have come vast amounts of data. Lying hidden in all this data is potentially useful
information that is rarely made explicit or taken advantage. New tools based both on
clever applications of established algorithms and on new methodologies, empower us to
do entirely new things. In this context, data mining has arisen as an important research
area that helps to reveal the hidden interesting information from the rawdatacollected.
The project demonstrates how data mining can address the need of business intelligence
in the process of decision making. An analysis on the field of data mining is done to show
how data mining can help in business such as marketing, credit card approval. The
project's objective is identifying the available data mining algorithms in data
classification and applying new data mining algorithm to perform classification tasks.
The proposed algorithm is a hybrid system which applied fuzzy logic and artificial neural
network, which applies fuzzy logic inference to generate a set of fuzzy weighted
production rules and applies artificial neural network to train the weights of fuzzy
weighted rules for better classification results.
Theresult of this system using the iris dataset and credit card approval dataset to evaluate
the proposed algorithm's accuracy, interpretability. The project has achieved the target
objectives; it can gain high accuracy for data classification task, generate rules which can
help to interpret the output results, reduce the training processing. But the proposed
algorithm still require high computation, the processing time will be long if the dataset is
huge. However the proposed algorithm offers a promising approach to building
intelligent systems
Vertical Handoff Target Selection in a Heterogeneous Wireless Network Using Fuzzy ELECTRE
Global connectivity is on the verge of becoming a reality to provide high-speed, high-quality, and reliable communication channels for mobile devices at anytime, anywhere in the world. In a heterogeneous wireless environment, one of the key ingredients to provide efficient and ubiquitous computing with guaranteed quality and continuity of service is the design of intelligent handoff algorithms. Traditional single-metric handoff decision algorithms, such as Received Signal Strength (RSS), are not efficient and intelligent enough to minimize the number of unnecessary handoffs, decision delays, call-dropping and blocking probabilities.
This research presents a novel approach for of a Multi Attribute Decision Making (MADM) model based on an integrated fuzzy approach for target network selection
Fuzzy Logic
Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems
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