This book examines one of the more common and wide-spread methodologies to deal with uncertainty in real-world decision making problems, the computing with words paradigm, and the fuzzy linguistic approach. The 2-tuple linguistic model is the most popular methodology for computing with words (CWW), because it improves the accuracy of the linguistic computations and keeps the interpretability of the results. The authors provide a thorough review of the specialized literature in CWW and highlight the rapid growth and applicability of the 2-tuple linguistic model. They explore the foundations and methodologies for CWW in complex frameworks and extensions. The book introduces the software FLINTSTONES that provides tools for solving linguistic decision problems based on the 2-tuple linguistic model. Professionals and researchers working in the field of classification or fuzzy sets and systems will find The 2-tuple Linguistic Model: Computing with Words in Decision Making a valuable resource. Undergraduate and postdoctoral students studying computer science and statistics will also find this book a useful study guide.

Modern systems for information retrieval, fusion and management need to deal more and more with information coming from human experts usually expressed qualitatively in natural language with linguistic labels.

This book proposes a novel CWW model to personalize individual semantics in linguistic decision making, based on two new concepts: numerical scale and consistency-driven methodology. The numerical scale model provides a unified framework to connect different linguistic symbolic computational models for CWW, and the consistency-driven methodology customizes individuals’ semantics to support linguistic group decision making by setting personalized numerical scales. The book is a valuable resource for researchers and postgraduates who are interested in CWW in linguistic decision making.

In this chapter, Herrera-Martınez 2-tuple linguistic representation model is extended for combining imprecise qualitative information using fusion rules drawn from Dezert-Smarandache Theory (DSmT) or from Dempster-Shafer Theory (DST) frameworks.

This book constitutes the refereed proceedings of the 9th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2014, held in Granada and Madrid, Spain, in July 2014. RSCTC 2014 together with the Conference on Rough Sets and Emerging Intelligent Systems Paradigms (RSEISP 2014) was held as a major part of the 2014 Joint Rough Set Symposium (JRS 2014) The 23 regular and 17 short papers presented were carefully reviewed and selected from 120 submissions. They are organized in topical sections such as fuzzy logic and rough set: tools for imperfect information; fuzzy-rough hybridization; three way decisions and probabilistic rough sets; new trends in formal concept analysis and related methods; fuzzy decision making and consensus; soft computing for learning from data; web information systems and decision making; image processing and intelligent systems.

This book offers an introduction to various models and methods for green supplier evaluation and selection (GSES) within different information settings. As such, it provides valuable knowledge, useful GSES methods and practical examples that can be considered by manufacturers in selecting appropriate green suppliers to improve their environmental performance and customer satisfaction. This book is useful for practitioners and researchers working in the fields of supply chain management, operation management, information science, industrial engineering, and management science. It is also useful as a textbook for postgraduate and senior undergraduate students.

As an expansion of 2-tuple linguistic intuitionistic fuzzy set, the newly developed 2-tuple linguistic neutrosophic set (2-TLNS) is more satisfactory to define decision maker’s assessment information in decision making problems. 2-TLN aggregation operators are of great significance in multiple attribute group decision making (MAGDM) problems with a 2-tuple linguistic environment. Therefore, in this article our main contribution is to develop novel 2-TLN power Heronian aggregation (2-TLNPHM) operators.