A New Fuzzy Decision Support Method for Analysis of Economic Factors of Turkey's Construction Industry

Imperfect knowledge cannot be avoided all the time. Imperfections may have several forms; uncertainties, imprecision and incompleteness. When we look to classification of methods for the management of imperfect knowledge we see fuzzy set-based techniques. The choice of a method to process data is linked to the choice of knowledge representation, which can be numerical, symbolic, logical or semantic and it depends on the nature of the problem to be solved for example decision support, which will be mentioned in our study. Fuzzy Logic is used for its ability to manage imprecise knowledge, but it can take advantage of the ability of neural networks to learn coefficients or functions. Such an association of methods is typical of so-called soft computing. In this study a new method was used for the management of imprecision for collected knowledge which related to economic analysis of construction industry in Turkey. Because of sudden changes occurring in economic factors decrease competition strength of construction companies. The better evaluation of these changes in economical factors in view of construction industry will made positive influence on company’s decisions which are dealing construction. Keywords—Fuzzy Logic, Decision Support Systems, Construction Industry. I. INRODUCTION HE Republic of Turkey is a major force in the international construction market [1]. Construction industry is a crucial field of activity for the economy of a country [2].The sector is a major employer. The percentage of population in total civil employment working in construction sector including construction material production varies between 6 % and 10% with respect to years. Construction industry is directly linked to other industrial sectors and it is a leading industry in developing countries [3][4]. Construction companies are usually functioning under the following categories; 1. Contractors Rifat Tur is with the Civil Engineering Department, University of Akdeniz, Antalya, 07200 TR, (phone: 90 242 3236889; fax: 90 242 323236465; e-mail: [email protected]). Ahmet Yardımcı is with the Industrial Automation Department, University of Akdeniz, Antalya, 07050 TR, (phone: 90 242 3106701; fax: 90 242 2274785; e-mail: [email protected]). 2. Subcontractors 3. Consultant companies There are some uncertainties about the future of the sector, as the future directly depends on the economical growth rate as well as the level of public expenditures. As a result of privatization, the share of public sector in construction sector will decrease [5]-[6]. When compared for sectors, construction sector has been complex because of including many experting areas, dealing with various sectors more than 200 and attracting the attention of international contracting activities. Due to the existence of many factors influencing economic conditions, it is essential to analyze these factors in several ways and determine the relationships between these factors. Defining with fuzzy expression of importance degree of those factors, which vary from company to company, makes, it is impossible to analyze with deterministic approach. II. ECONOMIC SITUATION ANALYSIS FOR CONSTRUCTION INDUSTRY The Turkish construction industry began to develop after the end of the Second World War in 1945. Until 1979, the country faced serious economic troubles with an increasing high rate of inflation, decreasing natural resources, declining exports, growing imports, and the accumulation of numerous unpaid debts to other countries. Consequently, like many other sectors, the construction industry suffered a crisis during this period. There were many bankruptcies, unfinished jobs, legal claims, and unemployed workers in the domestic market during that time [7]. The 1980’s marked a turning point in economy, because economic, political and social unrest necessitated radical economic and structural reforms these years signaled the birth of a liberal era for the country [8]. The most important factor affecting directly on developing of construction sector is changes in economic situations. Causing of economic uncertainties to long-term crisis, resulted from nation management, makes it difficult to decide about strategic issues which is relevant to economic conditions for companies [9]-[10]. One of the most important indicators showing importance and position of a sector in economy is its proportion in Gross National Product (GNP) [11]. According to GNP calculations A New Fuzzy Decision Support Method for Analysis of Economic Factors of Turkey’s Construction Industry R. Tur, and A. Yardımcı T World Academy of Science, Engineering and Technology International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering Vol:1, No:7, 2007 64 International Scholarly and Scientific Research & Innovation 1(7) 2007 scholar.waset.org/1999.3/2234 In te rn at io na l S ci en ce I nd ex , C iv il an d E nv ir on m en ta l E ng in ee ri ng V ol :1 , N o: 7, 2 00 7 w as et .o rg /P ub lic at io n/ 22 34 made by State Institute of Statistics (SIS), in 1990s’ economy and construction sectors have decreased %6.4 and %12.7, respectively, in 1999, when most economic reducing is occurred [12]. This awful reducing in the sector especially resulted from restriction in state sector. Other causes of reducing are the high interests in reel economy, high tax ratios and diminishing of demand for house and private sector. In addition to these negative conditions, dimensions of economic decreasing have become more severe due to earthquake disaster occurred in 1999 and proportion of the sector in GNP decreased to 5.1% [13]. Unfortunately, improving in economy in 2000 has not affected construction sector and while economy increased 4.2% in the first quarter of 2000 year, construction sector decreased 2.5%. Employment in Turkey for 1999 has been announced as 22 billion by SIS. 1.2 billion of this employment corresponded to 5.4% of total employment are working in construction sector. Thus, construction sector, which is a very important employment area for the workers who are generally assumed as unqualified worker, is being affected by economical changes while it affects economic improvements. Ratio of the construction sector in the employment has risen for the last three years but decreased a result of reducing occurred in the sector in 1999. Fluctuations in employment proved that economic factors have negative effects on the construction sector [14]. There is a very close relationship between construction sector and state investments in Turkey. In 1998, economical crisis, especially affecting the Far East countries and the Independent States Community, kept its effect in following year and state spending and investments has been reduced because of uncertainties caused by central and local elections, tax judgments and constancy precautions. The importance of state investments for construction sector seems more clearly when considering that 33% of construction spending is made by state. In this case, it can be said that state investments must be accelerated so as to develop the construction sector. But in practice, the situation is not in this way. While the ratio of state spending given from national budget was 4.8% in 1999, this ratio was determined as 4.9% in 2000 [15]. As a result of constancy precautions taken in 1999, diminishing in state investments and the uncertainty caused by taken precautions led to stop private sector investments or cancel for an indefinite time, and the construction sector entered to a reducing process. In addition, high credit interests and dull economy promoted reducing in private sector investments. Consequently, present situation has been forming a chaos case for construction sector and economical uncertainties with political unsteadiness are dragging the construction companies to some insurmountable problems. III. FUZZY LOGIC AND FUZZY SET THEORY Fuzzy set theory was introduced by Zadeh in the mid1960’s [16]. The basic concept is that propositions are not necessarily true or false but rather have a degree to which they are true, represented by a number between 0 and 1, inclusive. Traditional propositional binary logic is replaced by new mathematical concepts that permit conjunctions, disjunctions, and implications in terms of these partial truth values. A component of fuzzy logic, fuzzy set theory allows partial set membership, a concept that is useful in classification problems. Since the introduction of fuzzy logic, the field has expanded enormously in terms of both theoretical developments and application. For an overview of the theory behind fuzzy systems, see Kaufmann [17] and the journal Fuzzy Sets and Systems, which contains both theory and applications. Economic factors can be defined as fuzzy sets, along with membership functions that indicate the degree to which an item belongs to a fuzzy set. IV. PROPAGATION OF RESULTS A. Max-Min Networks Saito and Mukaidono [18] propose a max-min algorithm to replace the traditional sum-product algorithm that is used traditionally in neural network learning algorithms. A number of self-consistent fuzzy systems can be designed using tnorms and t-co-norms [19]. Thus the traditional sum-product paradigm for weight combination is replaced by one of these constructs. The minimum and maximum of membership functions are often used in place of AND and OR operations, respectively. A max-min network generally consists of a number of nodes that use either the min or the max operation to combine weights. The max-product networks are similar to the max-min networks except that the product is used in place of the min. B. Possibility Theory A possibility distribution can be defined by assigning to every element x in Ux a degree of possibility [20];