Hideo Tanaka

Bio: Hideo Tanaka is an academic researcher from International University, Cambodia. The author has contributed to research in topics: Rough set & Fuzzy classification. The author has an hindex of 19, co-authored 50 publications receiving 2139 citations. Previous affiliations of Hideo Tanaka include Osaka Prefecture University & Toyohashi Sozo College.

On Fuzzy-Mathematical Programming

Abstract: In problems of system analysis, it is customary to treat imprecision by the use of probability theory. It is becoming increasingly clear, however, that in the case of many real world problems involving large scale systems such as economic systems, social systems, mass service systems, etc., the major source of imprecision should more properly be labeled ‘fuzziness’ rather than ‘randomness.’ By fuzziness, we mean the type of imprecision which is associated with the lack of sharp transition from membership to nonmembership, as in tall men, small numbers, likely events, etc. In this paper our main concern is with the application of the theory of fuzzy sets to decision problems involving fuzzy goals and strategies, etc., as defined by R. E. Bellman and L. A. Zadeh [1]. However, in our approach, the emphasis is on mathematical programming and the use of the concept of a level set to extend some of the classical results to problems involving fuzzy constraints and objective functions.

A formulation of fuzzy decision problems and its application to an investment problem

Abstract: Although the decision‐making problem at the lower level is generally well‐defined, the decision‐making problem at the higher level would not contain the detail. Much of decision‐making at the higher level might take place in a fuzzy environment, so that it is only necessary to decide roughly what actions, what states and what parameters should be considered. This paper deals with the higher level problem in which we can regard the elements‐states of nature, feasible actions and available information‐as fuzzy objects. Since the uncertainty of meaning of objects is represented by the fuzzy sets and the uncertainty of occurrence of objects is defined by the probability, a specific formulation of the higher level decision problem can be defined by the probability of fuzzy events. From the same aspect, the definitions of worth, entropy and quantity concerning fuzzy information are given in this paper, and we have tried to extend some of the statistical decision theory to the fuzzy decision problem. To explain our formulation, an investment problem is presented as an example.

Hesitant Fuzzy Linguistic Term Sets for Decision Making

Abstract: Dealing with uncertainty is always a challenging problem, and different tools have been proposed to deal with it. Recently, a new model that is based on hesitant fuzzy sets has been presented to manage situations in which experts hesitate between several values to assess an indicator, alternative, variable, etc. Hesitant fuzzy sets suit the modeling of quantitative settings; however, similar situations may occur in qualitative settings so that experts think of several possible linguistic values or richer expressions than a single term for an indicator, alternative, variable, etc. In this paper, the concept of a hesitant fuzzy linguistic term set is introduced to provide a linguistic and computational basis to increase the richness of linguistic elicitation based on the fuzzy linguistic approach and the use of context-free grammars by using comparative terms. Then, a multicriteria linguistic decision-making model is presented in which experts provide their assessments by eliciting linguistic expressions. This decision model manages such linguistic expressions by means of its representation using hesitant fuzzy linguistic term sets.

Theory and practice of uncertain programming

Abstract: Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

Financial advisory system

Abstract: A financial advisory system is provided. According to one aspect of the present invention, return scenarios for optimized portfolio allocations are simulated interactively to facilitate financial product selection. Return scenarios for each asset class of a plurality of asset classes are generated based upon estimated future scenarios of one or more economic factors. A mapping from each financial product of an available set of financial products onto one or more asset classes of the plurality of asset classes is created by determining exposures of the available set of financial products to each asset class of the plurality of asset classes. In this way, the expected returns and correlations of a plurality of financial products are generated and used to produce optimized portfolios of financial products. Return scenarios are simulated for one or more portfolios including combinations of financial products from the available set of financial products based upon the mapping.