Preference learning has recently received a lot of attention within the machine learning field, concretely learning by pairwise comparisons is a well-established technique in this field. We focus on the problem of learning the overall preference weights of a set of alternatives from the (possibly conflicting) uncertain and imprecise information given by a group of experts into the form of inter...

T problem of group ranking, also known as rank aggregation, has been studied in contexts varying from sports, to multicriteria decision making, to machine learning, to ranking Web pages, and to behavioral issues. The dynamics of the group aggregation of individual decisions has been a subject of central importance in decision theory. We present here a new paradigm using an optimization framewor...

Globalization of business and the competitiveness of manufacturing have motivated companies to improve the facilities to respond to the market requirements. Machine tool selection plays a major role in the improvement of productivity, flexibility in uncertain manufacturing. The objective of this study is to present the simple approach for machine tool selection based on the integration of consi...

Reliability, availability, and maintainability (RAM) analysis has helped to identify the critical and sensitive subsystems in the production systems that have a major effect on system performance. But the collected or available data, reflecting the system failure and repair patterns, are vague, uncertain, and imprecise due to various practical constraints. Under these circumstances it is diffic...

Abstract Data Envelopment Analysis (DEA) can be regarded as a useful management tool to the assessment evaluation of decision making units (DMUs) using multiple inputs to produce multiple outputs. In some cases, to evaluate the efficiency having imprecise inputs and outputs such as fuzzy or interval data the efficiency of DMUs won’t be exact as well. Most researches have been conducted were bas...

In many real world applications (even in banking), imprecise data is a matter of fact. However, classic database management systems provide little if any help in the management of imprecise data. We are applying methods from interval arithmetic, epsilon serializability, and other related areas to help the application designers in the management of naturally imprecise data. Our approach includes...