Görüntüleme (gezinme ile): 4 -- Görüntüleme (arama ile): 4 -- IP: 10.2.8.4 -- Ziyaretçi Sayısı:

Özgün Başlık
Büyük Yatırım Analizlerinde Yeni Genel Bir Yöntem

Yazarlar
Burak Ömer Saraçoğlu, Ahmet Yücel Odabaşı

Dergi Adı
İTÜ Dergisi D : Mühendislik

Cilt
Şubat 2011, Cilt 10, Sayı 1, ss. 81-92

Anahtar Kelimeler
Yatırım analizleri ; gemi inşaatı ; liman yatırımı ; karar verme yöntemleri ; bula-nık mantık

Özet
Bu araştırma genel olarak büyük yatırımların gerektiği sektörlerde yatırım analizleri problemlerine çözüm olmayı amaçlayan çalışmalardan biridir. Bu çalışma sırasında çeşitli alanlarda sınıflandır-ma bilimi çalışması yapılmış olmasına rağmen bu makale ile sadece yatırım analizleri literatür ta-raması çalışması sunulmuştur. Yatırım analizleri sınıflandırma bilimi çalışmasında 40 makale ve kitap detaylı şekilde taranmıştır ve özlü şekilde sunulmuştur. En eski eser 1988 yılında yayımlan-mıştır, en yeni eser 2007 yılında yayımlanmıştır. Bu kaynaklardan detaylı şekilde faydalanarak çe-şitli performans göstergeleri açıklanmış ve sunulmuştur. Bu makale çok amaçlı karar verme optimi-zasyonuna bağlı bulanık mantık temelli çok seçimli karar verme yöntemlerine dayalı yeni genel bir yöntemin yatırım analizlerinde kullanılabilmesi için hazırlanmış bir doktora tezinin belirli bir bö-lümünün sunulması için hazırlanmıştır. Yöntem üç aşamadan oluşmaktadır. Birincisi ön karar ver-me aşaması olup 15 adımdan oluşmaktadır. İkinci aşama ise 31 ana adımdan oluşmaktadır. Son aşama ise 5 ana adımdan oluşmaktadır. Bu aşamalar en özlü şekilde bu makale ile sunulmuştur. Bu makale ile sunulmamış olsa bile bu yöntem Gelibolu Gemi Endustrisi Sanayi ve Ticaret A.Ş. için yeni gemi inşaa ve sanal liman - bakım onarım tersanesi yatırım analizleri vaka çalışmasında uy-gulanmıştır. Makalenin sonuç bölümünde, bahsi geçen doktora çalışmasına dayalı olabilecek gele-cek çalışmalar hakkında bilgi verilmiştir.

Başlık (Yabancı Dil)
A New Generic Method for Large Investment Analysis in Industry

Anahtar Kelimeler (Yabancı Dil)
Investment analysis ; shipbuiling ; port investment ; decision making methods ; fuzzy logic

Özet (Yabancı Dil)
This research might be one of the most impressive “cross-industry study”, which is devoted to solve basically the decision making problems at invest-ment analysis in shipbuilding industry, logistics in-dustry (port investment), shipping industry, energy sector and other mega investment based industries. Although a taxonomic study was conducted for the literature review of decision making, management systems, investment analysis, mathematical and sta-tistical methods, software and coding in this paper only investment analysis literature review was pre-sented. In investment analysis taxonomic study, forty papers and books were studied in detail. The oldest study was published in 1988 and the newest study was published in 2007. In investment analysis taxonomic study, the performance measures were explained and presented in detail. A new generic method for large investment analysis in industry based on multi-objective optimization and fuzzy multi attribute decision making is ex-plained. The proposed method has three main phas-es respectively named as pre-decision phase that has 15 main steps in which definition and description of investment decision is executed, decision phase that has 31 main steps in which collection and analyze of investment decision is executed and post-decision phase that has 5 main steps in which analyze and conclusion is executed. These steps are notification and intention for investment ; gather information for several industries ; select the industries intended to be accessed ; review the sources for forecasts of the industry ; select the industry to be run in the model ; decide whether the investment is location free or lo-cation oriented ; generate the location free attributes pool ; select the attributes, generate objectives, pa-rameters, constraints pool and select the objectives, parameters, constraints ; cross check the objectives, parameters, constraints and attributes ; generate the expert pool and select the experts ; decide the inves-tors names who shall attend the study ; publish the pre-decision report ; collect data for investment analysis study ; fill the data into the model and in-vestment calculations ; generate Pareto Optimal de-sign alternatives (PODA) ; select filter / set Pareto Optimal design alternatives ; assign each investor the weights by the share rate ; normalize the weight of investors ; collect each investor opinion for each expert ; assign expert weight according to investors point of view ; collect each expert opinion for each expert ; transform fuzzy data into fuzzy membership function for each weight assignment of each expert ; synthesize fuzzy membership function and find the value of the fuzzy synthetic degree ; defuzzify the syn-thetic degrees to calculate each weight assignment of each expert ; compare the expert weight of inves-tors and experts-adjust the expert weights ; normal-ize the weight of experts to calculate the weight as-signment of each expert, collect each expert opinion for each attribute to assign the relative importance of attributes ; transform fuzzy data into fuzzy mem-bership function for each weight assignment of each attribute ; synthesize fuzzy membership function and find the value of the fuzzy synthetic degree ; defuzzify the synthetic degrees to calculate each weight as-signment of each attribute ; normalize the weight of attributes to calculate the weight assignment of each attribute ; collect each expert opinion for each PODA with respect to each objective attribute ; col-lect each expert opinion for each PODA with respect to each subjective attribute ; transform fuzzy data into fuzzy membership function for each PODA with respect to each subjective attribute ; synthesize fuzzy membership function and find the value of the fuzzy synthetic degree ; defuzzifying the synthetic degrees to calculate each weight ; assignment of each PODA with respect to each subjective attribute ; normalize the weight of attributes to calculate the weight as-signment of each PODA with respect to each subjec-tive attribute ; check whether all data are collected for the ANP model ; check whether all data are transformed into crips number or not ; build up ANP model with crips numbers converted fuzzy numbers ; find overall alternative ranking values (OARs) for PODAs ; order or rank PODAs according to OARs ; publish the decision report ; send the decision report to experts and investors ; the final decision ; the con-sensus achieved ; publish the post-decision report ; and finally investment. This paper also concludes by highlighting future di-rections for research in several industries and in different research areas based on this area and sub-ject.