Nowadays healthcare organizations globally recognize the importance of investing in information technologies to improve the quality of care delivery and reduce costs. The key drivers of healthcare sector such as continuously improving healthcare standards and insurance systems have introduced new requirements for hospitals, which in return provided a solid ground for decision-makers to consider implementing hospital information systems that are customized and improved versions of enterprise resource planning (ERP) systems designed according to the needs of the healthcare sector. The conventional discounted cash flow methods ignore the value of managerial and strategic flexibility inherent in these investments, which is crucial for justification of the investment decision. This study introduces a real options-based methodology which overcomes the limitations of traditional valuation methods and enables decision-makers to value an ERP system investment incorporating multiple options. The option valuation model developed in this study extends the binomial lattice framework to model a hospital information system (HIS) investment opportunity with compound options. The potential application of the proposed model is illustrated through evaluation of a real-world HIS investment.

Quality function deployment (QFD) is a product/service design and improvement tool which is basically a transformation of vague and imprecise customer needs into measurable product/service attributes. This article integrates compromise programming based goal programming into the QFD process to determine to what extent the product/service attributes should be improved. The fuzzy set theory is applied to the model to deal with the imprecise nature of data. Differing from existing QFD applications, our proposed methodology applies analytic network process to evaluate the inner dependencies among customer needs, among product attributes and also the relationships between them. Furthermore, it determines the best product/service in the market as the goal employing compromise programming. Finally, the methodology ends with the goal programming method which consists of this predefined goal and the product/service provider’s budget limitation. A real-world application on e-learning products provided by the higher education institutions in Turkey illustrates the applicability of our proposed methodology.

Bu calisma Karar Alicilar (KA) icin Islem Sistemi (IS) secim sistemi kurmayi hedeflemektedir. Karar Alicilar teknoloji seciminde hem ekonomik hem de ekonomik olmayan unsurlari goz onune almak zorunda olduklarindan, gelistirilen sistemde her iki unsura da yer verilmistir. Karar alma surecinin ekonomik yani, Bulanik Yenileme Analizi kullanilarak gelistirilmistir. Ekonomik olamayan unsurlar ve finansal veriler ise Bulanik Analitik Hiyerarsi Sureci (AHS) yaklasimi kullanilarak biraraya getirilmistir. Calisma icerisinde ayni zamanda sayisal bir ornege de yer verilmistir. AHS yaklasiminin finansal yonu gelistirilen Bulanik Yenileme Analizi altyapisi tarafindan desteklenmistir. Bulanik AHS yonteminin Muhendislik Ekonomisi’nin ana konularindan olan Yenileme Analizleri’nde kullanilmasi arastirmacilara yatirim alternatiflerinin degerlendirilmesinde etkin yollar saglamaktadir. Anahtar Kelimeler: Bulanik kumeler, yenileme analizleri, analitik hiyerarsi sureci, teknoloji secimi.

When no probabilities are available for states of nature, decisions are given under uncertainty. When probabilities are unattainable, the criteria such as minimax, maximin, minimaxregret can be used. While these criteria are used, a single value is assigned for every strategy and state of nature. Fuzzy numbers are a good tool for the operation research analyst facing uncertainty and subjectivity. A triangular fuzzy number has been used instead of a single value of outcome. Numerical Examples have been given for every fuzzy decision criterion.

This study aims at creating an Operating System (OS) selection framework for decision makers (DMs). Since DMs have to consider both economic and non-economic aspects of technology selection, both factors have been considered in the developed framework. The economic part of the decision process has been developed by Fuzzy Replacement Analysis. Non-economic factors and financial figures have been combined using a fuzzy analytic hierarchy process (Fuzzy AHP) approach. Since there exists incomplete and vague information of future cash flows and the crisp AHP cannot reflect the human thinking style in capturing the expert's knowledge, the fuzzy sets theory has been applied to both AHP and replacement analysis, which compares two OSs with and without license, respectively. A real numerical application has also been demonstrated. Both the theoretical and the practical background of this paper have shown that fuzzy AHP and fuzzy replacement analysis can cover the uncertainty of assigning crisp concepts in related investment decision-making topics.

Risk analysis involves the development of the probability distribution for the measure of effectiveness. The risk associated with an investment alternative is generally either given as the possibility of an unfavorable value of the measure of effectiveness or measured by the variance of the measure of effectiveness. In an uncertain economic decision environment, an expert's knowledge about discounting cash flows consists of a lot of vagueness instead of randomness. Cash amounts and interest rates are usually estimated by using educated guesses based on expected values or other statistical techniques to obtain them. Fuzzy numbers can capture the difficulties in estimating these parameters. In this paper, the formulas for the analyses of fuzzy present value, fuzzy equivalent uniform annual value, fuzzy future value, fuzzy benefit–cost ratio, and fuzzy payback period are developed and given some numeric examples. Then the examined cash flows are expanded to geometric and trigonometric cash flows and using these cash flows fuzzy present value, fuzzy future value, and fuzzy annual value formulas are developed for both discrete compounding and continuous compounding.

In this paper, a fuzzy decision algorithm is proposed to select the most suitable advanced manufacturing system (AMS) alternative from a set of mutually exclusive alternatives. Both economic evaluation criterion and strategic criteria such as flexibility, quality improvement, which are not quantitative in nature, are considered for selection. The economic aspects of the AMS selection process are addressed using the fuzzy discounted cash flow analysis. The decision algorithm aggregates the experts' preference ratings for the economic and strategic criteria weights, and the suitability of AMS investment alternatives versus the selection criteria to calculate fuzzy suitability indices. The fuzzy indices are then used to rank the AMS investment alternatives. Triangular fuzzy numbers are used throughout the analysis to quantify the vagueness inherent in the financial estimates such as periodic cash flows, interest rate and inflation rates, experts' linguistic assessments for strategic justification criteria, and importance weight of each criterion. A comprehensive numerical example is provided to illustrate the results of the analysis.

Dynamic programming is applicable to any situation where items from several groups must be combined to form an entity, such as a composite investment or a transportation route connecting several districts. The most desirable entity is constructed in stages by fonning sub-entities that are candidates for inclusion in the most desirable entity are retained, and all other sub-entities are discarded. In the paper, the fuzzy dynamic programming is applied to the situation where each investment in the set has the following characteristics: the amount to be invested has several possible values, and the rate of return varies with the amount invested. Each sum that may be invested represents a distinct level of investment, and the investment therefore has multiple levels. A numeric example constructing a combination of multilevel investments is given in the paper.

Risk analysis involves the development of the probability distribution for the measure of effectiveness. The risk associated with an investment alternative is generally either given as the possibility of an unfavorable value of the measure of effectiveness or measured by the variance of the measure of effectiveness. In an uncertain economic decision environment, an expert's knowledge about discounting cash flows consists of a lot of vagueness instead of randomness. Cash amounts and interest rates are usually estimated by using educated guesses based on expected values or other statistical techniques to obtain them. Fuzzy numbers can capture the difficulties in estimating these parameters. In this paper, the formulas for the analyses of fuzzy present value, fuzzy equivalent uniform annual value, fuzzy future value, fuzzy benefit-cost ratio, and fuzzy payback period are developed. In addition, continuous compounding, geometric and trigonometric cash flows are also examined.

Many authors have studied probabilistic cash flows in recent years. They have introduced analytical methods which determine the probability distribution function of the net present value and internal rate of return of a series of random discrete cash flows; considered serially correlated cash flows and the uncertainty of future capital investment and reinvestment rates; and presented formulas for benefit-cost ratio for probabilistic cash flows. Others have studied the arithmetic of inflation corrections in evaluating real present values. Here, the expected value and the variance of a probabilistic cash flow are obtained by means of moments. Assuming that cash flows are probabilistic because only future discount rates (involving inflation, risk and other factors) are uncertain, we calculate the mean level of future discount rates equivalent to the risk degree in probabilistic cash flows. A numerical example is given

Cash amounts and interest rates are usually estimated by using educated guesses based on expected values or other statistical techniques to obtain them. Fuzzy numbers can capture the difficulties in estimating these parameters. Fuzzy present value, fuzzy equivalent uniform annual value, fuzzy future value, fuzzy benefit-cost ratio, and fuzzy payback period are the methods examined with numeric examples in the paper. The paper also gives the ranking methods of fuzzy numbers.

The techniques of probabilistic risk analysis are mostly applied in the nuclear industry, with regard to the problems of quantifying risk arising from human risk or human error. Fuzzy clustering or pattern recognition can be also used to provide a better safety. Fuzzy clustering is a special type of clustering analysis that permits an object to belong to a cluster with a grade of membership and is better able to deal with ambiguity in the data and incomplete information. Based on statistical process control technology, a fuzzy clustering method is used to find out the most likely cause of any accident.

A stochastic finite horizon model incorporating inflation and income tax effects is formulated to evaluate overhaul-replacement decisions of equipment subject to technological change. Overhaul is introduced in the formulation as an alternative policy, and the cost of overhauling is assumed to vary with the age of the equipment. The revenue and cost elements in each period are functions of both equipment age, and age at last overhaul. Time-dependent differential rates of inflation are used throughout the analysis to remove the unrealistic assumption of traditional self-cancelling approach, which proposes that all prices in the economy are influenced by the uniform periodic rate of increase. The uncertainty due to variability in the outcome of policy decisions is taken into account using discrete time Markov chains. The optimal integrated policy that maximizes the expected present worth of the process over a specified planning horizon is determined via dynamic programming. An illustrative application is provided to test the model's sensitivity to the changes in differential rates of inflation, interest rate, and tax rate estimates. The results emphasize the proper handling of inflation in determining the optimal overhaul-replacement policy.

Many of the objectives of advanced manufacturing technologies (AMTs) lie in strategic areas such as better quality, flexibility, and shorter lead times. The authors propose fuzzy multiobjective linear programming which considers intangible benefits in AMTs and expands the constraints by adding tolerances. The transition from vagueness to quantification is performed by applying fuzzy set theory. The approach also considers vagueness of the objective functions by using membership functions. The main advantage of fuzzy LP, compared to the unfuzzy problem formulation, is the fact that the decision maker is not forced into a precise formulation because of mathematical reasons. A case example is given in the paper

When a range of alternative designs has been created, the designer is then faced with the problem of selecting the best one. There are a lot of economic evaluation methods that are with single criterion or multicriteria and deterministic or nondeterministic. The selected methods in the paper are the fuzzy present worth method for the economic analysis and the fuzzy scoring method for the strategic analysis. The triangular fuzzy numbers calculated are compared by a certain method. The method is applied to the economic and strategic design of motorcars.

Some researchers propose systematic procedures for quantifying flexibility in monetary terms and use financial evaluation models with decision criteria based on present worth, equivalent uniform annual worth, and the other discounting techniques. Many parameters are defined while quantifying flexibility value and are assumed to be accurate numerical estimates. But it must be considered that some changes in these numerical estimates may occur. Therefore, these parameters can be defined as triangular parameters. Thus, we obtained the fuzzy present worth formulas of the flexibility elements. Using these formulas, more reliable results can be obtained. The flexibility modeling using triangular fuzzy numbers allows expert's linguistic predicate about automated manufacturing systems. Taking the result of the fuzzy present worth formula of the flexibility, the fuzzy flexibility evaluation can be achieved by applying some dominance rules on triangular fuzzy numbers