Shady Aly

Fuzzy aggregation and averaging for group decision making: A generalization and survey

This article presents some systematic sorting and ordering of approaches dealing with fuzzy aggregation and fuzzy averaging from different authors. The aggregation of fuzzy information from a group of experts for developing collective opinion or verdict is the important question in the expert systems theory and practice. This is to obtain a more comprehensive and realistic solution to the given decision problem. This note tries to outline an overall formal umbrella to various methods to aggregate several fuzzy sets, which describe the individual points of view of experts, or results of judgements from the various characteristics.

Assessing candidate industrial technologies utilising hierarchical fuzzy decision making systems

The adoption of new industrial technology is a type of critical decisions. Important characteristics of such significant decision problem are ill-structuredness, subjectivity and vagueness of input and output factors and their relationships. Most of past researches have considered only the quantitative view, and little or even no researches have treated inherent ambiguity in determining exact values of quantitative inputs and in quantifying subjective ones. In this paper, a hierarchical fuzzy decision making model is proposed for handling vagueness and subjectivity associated with the problems inputs (i.e. technology performance factors), and for structuring the relationships between them at one side and a technology evaluation score at the other side. The inputs to the model are groups of technical, economical and transferability-related measures. The output of the model is a crisp score for comparing merits of candidate technologies. Finally, a hypothetical illustrative example is provided.

Modeling Heterogeneous Experts’ Preference Ratings for Environmental Impact Assessment through a Fuzzy Decision Making System

Currently, there is an increasing demand for more efficient and practical environmental impact assessment (EIA) tools due to the emerging climate change challenges and need to better evaluate and control impacts of industrial technologies and activities. However, due to the inherent uncertainties, vagueness’s of assessment data, traditional EIA methods are unable to handle efficiently and properly such decision making process, and consequently more efficient method resorts to the opinions of group of relevant experts in order to enhance the reliability of the assessment decision. However, experts’ assessments are usually in heterogeneous forms, multi-metric or multi-criterion and usually conflicting. This article presents a fuzzy decision making systems (FDMS) that enables heterogeneous experts’ preference ratings assessment and provides for aggregation of those opinions over multi-metric scales. Experts can provide their opinion in form of crisp, linguistic or fuzzy values.

A Feasible Approximation to Optimum Decision Support System for Multidimensional Cases through a Modular Decomposition

The today’s decision making tasks in globalized business and manufacturing become more complex, and ill-defined, and typically multiaspect or multi-discipline due to many influencing factors. The requirement of obtaining fast and reliable decision solutions further complicates the task. Intelligent decision support system (DSS) currently exhibit wide spread applications in business and manufacturing because of its ability to treat ill-structuredness and vagueness associated with complex decision making problems. For multi-dimensional decision problems, generally an optimum single DSS can be developed. However, with an increasing number of influencing dimensions, increasing number of their factors and relationships, complexity of such a system exponentially grows. As a result, software development and maintenance of an optimum DSS becomes cumbersome and is often practically unfeasible for real situations. This paper presents a technically feasible approximation of an optimum DSS through decreasing its complexity by a modular structure. It consists of multiple DSSs, each of which contains the homogenous knowledge’s, decision making tools and possibly expertise’s pertaining to a certain decision making dimension. Simple, efficient and practical integration mechanism is introduced for integrating the individual DSSs within the proposed overall DSS architecture.