A fuzzy clustering-based method for scenario analysis in strategic planning: The case of an Asian pharmaceutical company

In today’s rapid changing market situations, many nations and companies try to keep or make better their situation and gain more market share by creating competitive advantages. Because of growing number of uncertain parameters in the environment and lack of information about the future, the strategic choice has become very complex and critical. One of the popular tools for solving the problem is scenario analysis. In this paper based on fuzzy clustering we propose a method for building, analyzing and ranking the possible scenarios. To cope with the issue of uncertain parameters of the environment in strategic planning, we use the concept of fuzzy set theory to enhance the proposed method. Finally the performance of the proposed method is illustrated in a strategic planning case in a pharmaceutical company.

Download Full-text

Applications of Fuzzy Technology in Business Intelligence

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2011.3.2128 ◽

2011 ◽

Vol 6 (3) ◽

pp. 428 ◽

Cited By ~ 12

Author(s):

Andreas Meyer ◽

Hans-Jürgen Zimmermann

Keyword(s):

Set Theory ◽

Fuzzy Clustering ◽

Fuzzy Set ◽

Business Intelligence ◽

Fuzzy Set Theory ◽

Mathematical Theory ◽

Business Management ◽

Knowledge Based Systems ◽

Knowledge Based ◽

Single Method

Fuzzy Set Theory has been developed during the last decades to a demanding mathematical theory. There exist more than 50,000 publications in this area by now. Unluckily the number of reports on applications of fuzzy technology has become very scarce. The reasons for that are manifold: Real applications are normally not single-method-applications but rather complex combinations of different techniques, which are not suited for a publication in a journal. Sometimes considerations of competition my play a role, and sometimes the theoretical core of an application is not suited for publication. In this paper we shall focus on applications of fuzzy technology on real problems in business management. Two versions of fuzzy technology will be used: Fuzzy Knowledge based systems and fuzzy clustering. It is assumed that the reader is familiar with basic fuzzy set theory and the goal of the paper is, to show that the potential of applying fuzzy technology in management is still very large and hardly exploited so far.

Download Full-text

A GENERALIZED APPROACH TO POSSIBILISTIC CLUSTERING ALGORITHMS

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488507004650 ◽

2007 ◽

Vol 15 (supp02) ◽

pp. 117-138 ◽

Cited By ~ 11

Author(s):

JIAN ZHOU ◽

CHIH-CHENG HUNG

Keyword(s):

Set Theory ◽

Fuzzy Clustering ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Clustering Algorithms ◽

Infinite Family ◽

Real Data ◽

Point Of View ◽

New Family ◽

Possibilistic Clustering

Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has advantages over traditional clustering in many applications. Many fuzzy clustering algorithms have been developed in the literature including fuzzy c-means and possibilistic clustering algorithms, which are all objective-function based methods. Different from the existing fuzzy clustering approaches, in this paper, a general approach of fuzzy clustering is initiated from a new point of view, in which the memberships are estimated directly according to the data information using the fuzzy set theory, and the cluster centers are updated via a performance index. This new method is then used to develop a generalized approach of possibilistic clustering to obtain an infinite family of generalized possibilistic clustering algorithms. We also point out that the existing possibilistic clustering algorithms are members of this family. Following that, some specific possibilistic clustering algorithms in the new family are demonstrated by real data experiments, and the results show that these new proposed algorithms are efficient for clustering and easy for computer implementation.

Download Full-text

A Short Survey on Genetic Sequences, Chou’s Pseudo Amino Acid Composition and its Combination with Fuzzy Set Theory

The Open Bioinformatics Journal ◽

10.2174/1875036201307010041 ◽

2013 ◽

Vol 7 (1) ◽

pp. 41-48 ◽

Cited By ~ 37

Author(s):

D.N. Georgiou ◽

T.E. Karakasidis ◽

A.C. Megaritis

Keyword(s):

Amino Acid ◽

Sequence Analysis ◽

Amino Acid Composition ◽

Acid Composition ◽

Set Theory ◽

Fuzzy Clustering ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Pseudo Amino Acid Composition ◽

Genetic Sequences

The study of genetic sequences is of great importance in biology and medicine. Sequence analysis and taxonomy are two major fields of application of bioinformatics. In this survey, we present results concerning genetic sequences and Chou's pseudo amino acid composition as well as methodologies developed based on this concept along with elements of fuzzy set theory, and emphasize on fuzzy clustering and its application in analysis of genetic sequences.

Download Full-text

Uncertain Analysis of a Stationary Solar Compound Parabolic Concentrator PV Collector System Using Fuzzy Set Theory

Journal of Renewable Energy ◽

10.1155/2018/2915731 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12

Author(s):

Hoe-Gil Lee ◽

Singiresu S. Rao

Keyword(s):

Solar Energy ◽

Set Theory ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Uncertain Parameters ◽

Membership Functions ◽

Solar Pv ◽

Compound Parabolic Concentrator ◽

Collector System ◽

Analysis Methodology

The uncertain analysis of fixed solar compound parabolic concentrator (CPC) collector system is investigated for use in combination with solar PV cells. Within solar CPC PV collector systems, any radiation within the collector acceptance angle enters through the aperture and finds its way to the absorber surface by multiple internal reflections. It is essential that the design of any solar collector aims to maximize PV performance since this will elicit a higher collection of solar radiation. In order to analyze uncertainty of the solar CPC collector system in the optimization problem formulation, three objectives are outlined. Seasonal demands are considered for maximizing two of these objectives, the annual average incident solar energy and the lowest month incident solar energy during winter; the lowest cost of the CPC collector system is approached as a third objective. This study investigates uncertain analysis of a solar CPC PV collector system using fuzzy set theory. The fuzzy analysis methodology is suitable for ambiguous problems to predict variations. Uncertain parameters are treated as random variables or uncertain inputs to predict performance. The fuzzy membership functions are used for modeling uncertain or imprecise design parameters of a solar PV collector system. Triangular membership functions are used to represent the uncertain parameters as fuzzy quantities. A fuzzy set analysis methodology is used for analyzing the three objective constrained optimization problems.

Download Full-text