Developing a group decision support system based on fuzzy information axiom Selcuk Cebi * , Cengiz Kahraman Department of Industrial Engineering, Istanbul Technical University, Macka 34367, Istanbul, Turkey article info Article history: Available online 3 August 2009 Keywords: Axiomatic design Information axiom Fuzzy Decision making Decision support system abstract Information axiom, one of two axioms of axiomatic design methodology which is proposed to improve a design, is used to select the best design among proposed designs. In the literature, there are a lot of stud- ies related to using of information axiom for the solution of decision making problems. Moreover, appli- cations of information axiom have been increasing day by day. However, calculation procedure of information axiom is not only incommodious but also difficult for decision makers. In this paper, a deci- sion support system (DSS) based on fuzzy information axiom (FIA) is developed in order to make this decision procedure easy. The developed system consists of a knowledge base module including facts and rules, inference engine module including FIA and aggregation method, and a user interface module including entrance windows. The main aim of this study is to present a DSS tool to help the decision mak- ers to solve their decision problems by modifying data-base of the program. In this paper, an application procedure will be presented based on the optimal selection of location for emergency service to illustrate the implementation procedure of the proposed model. Ó 2009 Elsevier B.V. All rights reserved. 1. Introduction Decision making is a procedure to find the best alternative among a set of feasible alternatives. So, the solution of decision making problems can be complex as well as simple. Since all deci- sion making problems have multiple alternatives and criteria, an increase in numbers of alternatives and criteria makes difficult to give a decision. Therefore, research on how to solve complicated decision making problems has been enormous [24]. Consequently, various methodologies such as Analytic Hierarchical Process (AHP) proposed by Saaty [39], Technique for Order Performance by Sim- ilarity to Ideal Solution (TOPSIS) proposed by Hwang and Yoon [14], Simple Additive Weighting method (SAW) [48], Elimination By Aspects (EBA) proposed by Tversky [44], ELimination Et Choix Traduisant la REalité (ELECTRE) [38,48], Preference Ranking Orga- nisation METHod for Enrichment Evaluations (PROMETHEE) [1], etc., have been developed and published in professional journals of different disciplines. Besides these methodologies, new decision making methodologies such as information axiom method [41], VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) [29,31] etc. or integrated methods like Hierarchical TOPSIS [17] have been proposed by researchers to solve complex decision mak- ing problems. Moreover, fuzzy decision making methods have been developed for solving problems in which description of activities and observations are imprecise, vague, and uncertain. Many deci- sion methods are extended to fuzzy environment such as fuzzy TOPSIS [11], fuzzy AHP [2,3,9,23] , fuzzy VIKOR [30], fuzzy informa- tion axiom [16,19–22] etc., in order to refer to the situation in which there are no well-defined boundaries of the set of activities or observations. Decision methods need complex calculation pro- cedures and increase in number of the alternatives and the criteria makes calculation procedure difficult. Hence, researchers develop decision support systems (DSS) to make the calculation procedure easy. DSS systems are a class of computer-based information sys- tems including knowledge-based systems that support decision making activities. The main aim of the DSS is to help decision makers to make the best decision when dealing with complex situations and information. DSS have been widely used by managers as a specific management tool and approach since it reduces the uncertainty and risk related to decision making. A good DSS should have a balance among dialog, data, and modeling. The means of dialog is that DSS should be easy to maintain the interaction between decision makers and DSS fully. The second term, data, guides that DSS should have include a wide variety of data sources. And the last term, modeling, indicates that DSS should provide an algo- rithm to reach a goal [27]. Lu et al. [27] identify five types of DSS; (1) model-driven DSS, (2) data-driven DSS, (3) knowledge- driven DSS, (4) group DSS, and (5) web-based DSS. In the litera- ture based on DSS, a lot of papers related to its various applica- tion areas have been studied. Some DSS examples which have been studied by researchers recently as follows: Pal and Palmer [32] developed a hybrid DSS for business acquisition process. Park 0950-7051/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.knosys.2009.07.005 * Corresponding author. Tel.: +90 212 293 1300 2746; fax: +90 212 240 7260. E-mail addresses: cebiselcuk@gmail.com, cebis@itu.edu.tr (S. Cebi). Knowledge-Based Systems 23 (2010) 3–16 Contents lists available at ScienceDirect Knowledge-Based Systems journal homepage: www.elsevier.com/locate/knosys