دانلود مقاله ISI انگلیسی شماره 6296
عنوان فارسی مقاله

تصمیم گیری چند معیاره بر اساس برنامه ریزی هدف و فرآیند تحلیل سلسله مراتبی فازی : برنامه برای مشکل بودجه بندی سرمایه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
6296 2012 6 صفحه PDF سفارش دهید 4890 کلمه
خرید مقاله
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عنوان انگلیسی
Multicriteria decision-making based on goal programming and fuzzy analytic hierarchy process: An application to capital budgeting problem
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Knowledge-Based Systems, Volume 26, February 2012, Pages 288–293

کلمات کلیدی
بودجه بندی سرمایه - فرآیند تحلیل سلسله مراتبی فازی - برنامه نویسی هدف - تصمیم گیری - تجزیه و تحلیل حساسیت -
پیش نمایش مقاله
پیش نمایش مقاله تصمیم گیری چند معیاره بر اساس برنامه ریزی هدف و فرآیند تحلیل سلسله مراتبی فازی : برنامه برای مشکل بودجه بندی سرمایه

چکیده انگلیسی

Our objective in this paper is to develop a decision-making model to assist decision-makers and researchers in understanding the effect of multiple criteria decision-making on a capital budgeting investment. This decision-making model helps decision-makers with reducing decision-making time and choosing a suitable decision alternative for a capital budgeting investment within the companies’ goals, constraints and strategies. The methods utilized in this paper are goal programming (GP) and fuzzy analytic hierarchy process (FAHP). We demonstrate a case study of the capital budgeting investment by using these two methods in a small car rental company.

مقدمه انگلیسی

Capital budgeting decision-making is one of the most demanding responsibilities of top management [21] and [12]. An increasing number of companies have struggled to justify strategic technology investments using traditional capital budgeting systems [2]. The existing accounting-based decision-making models (such as discounted cash flow (DCF)) are said to be no longer adequate to help evaluate investments in technological innovation, mainly because of the strategic, intangible nature of the benefits involved [13] and [22]. When business decisions are made, they involve not only consideration of information which is quantifiable in numerical terms (e.g. financial information), but also consideration of subjective (e.g. non-financial information) opinions [27], [2] and [1]. Such subjective considerations are naturally expressed in linguistic rather than in numerical terms [14]. Therefore, we realized that non-financial information needs to be quantified in order to integrate it with numerical information. This research will focus on how to integrate financial and non-financial information in the company’s constraints, goals and strategies. The methodologies presented within this research are goal programming (GP) and fuzzy analytic hierarchy process (FAHP) which address the problem of capital budgeting in uncertain environments. Capital budgeting is primarily concerned with sizable investments in long-term assets. Investment decisions deal with the funds raised in financial markets which are employed in productive activities to achieve the firm’s overall goal, in other words, how much should be invested and what assets should be invested are the main objectives. Therefore within this research it is assumed that the objective of the investment or capital budgeting decision is to achieve the company’s goals and to stay within its constraints. GP normally deals with conflicting objective measures. Each of these measures is given a goal or target value to be achieved. FAHP provides a relatively more complete description of decision-making process involving the subjective and imprecise judgments of decision makers [4]; [17]; [11]. The methods are divided into two steps. Firstly, financial and other objectives along with a company’s goals, constraints and strategies are formulated as important selection criteria. A set of decision alternatives (DAs) as preliminary outcomes will be sifted by using GP from financial information. Secondly, subjective opinions elicited from decision-makers (DMs) are transformed into fuzzy comparison matrices (for the details of FAHP also refer to Chang [8], Tang [23]). A simple practical preference ranking method (synthetic extent method) is investigated to rank alternatives in a multiplicative aggregation process. The extent analysis method has been employed in quite a number of applications, such as capital measurement [5], budget allocation [23], assets selections and investment [6], [7] and [24] and for more detail refer to Wang et al. [26]. However, disadvantages have also been pointed out, such as an inability to derive the true weights from a fuzzy or crisp comparison matrix [26]. This research will utilize the formulation of a degree of possibility for comparing two triangular fuzzy numbers as proposed in Zhu et al. [30]. One aspect of the FAHP method within this research is the prevalence of and allowance for incompleteness in the judgements made by DMs. For example, if a DM is not willing or is unable to specify the preference judgements, s/he is able to omit a judgement in the form of a pairwise comparison between two DAs. The rest of the paper is organized as follows. Section 2 describes the details of the used cars selection problem. Section 3 proposes GP procedures and the synthetic extent method of the FAHP. Section 4 illustrates the results of the used cars selection problem. The conclusion is provided in Section 5.

نتیجه گیری انگلیسی

The aim of this research is to investigate the application of the methods of the goal programming (GP) and the fuzzy analytic hierarchy process (FAHP) of multi-criteria decision making (MCDM), within a capital budgeting case study. In this research, GP is proposed to solve the financial information, such as the company goals, constraints, or other company’s strategy. FAHP is to deal with the imprecise judgements made by decision makers (DMs). The redefinition of the degree of fuzziness associated with the preference judgements made allows the change of imprecision (fuzziness) to be succinctly reported. The issue of imprecision is reformulated in this study, which further allows a sensitivity analysis on the preference weights evaluated to changes in the levels of imprecision. In this research, the goals are represented as numerical data. The scales used within this FAHP are verbal definitions and explanations of the integer (1–9) scale. If we use fuzzy AHP methodology then all the numerical data will be assessed into verbal assessment inputs. Ghosh and Roy [11] mentioned that AHP does not consider the relevant constraints and multiple conflicting goals. The verbal data’s limitations also can be referred to Cho [9]. They also mentioned that GP combined with the AHP can prove to be a flexible tool to reach the company’s goals and constraints. This research is focused on integrating the numerical and non-numerical information. Therefore, either FAHP or GP cannot stand alone to deal with both numerical and non-numerical information. This also can be referred to Badri [3]. Badri pointed out the drawbacks of using AHP alone and proposed that AHP and GP are combined to cover the limitations. Relevant references also can be referred to Tsai [25], Ravisankar and Ravi [16]. From the academic point of view, the model provided in this research can deal with the DMs consideration of the company’s goals, constraints, strategies and with imprecise judgements simultaneously. It also provides a high tolerance for ambiguity and a well-ordered sense of priorities. From the practical point of view, this research involved a field study and collection of data from semi-structured interviews, structured interviews and a questionnaire. It displays the integration of theory and practicality. The other contributions of this paper are saving decision-making time for decision makers – by using GP, and eliciting the subjective opinions from decision makers – by using FAHP, etc. Future research associated with the FAHP include, from the MCDM point of view, those developments with the traditional AHP. These include the appropriateness of the 9-unit scale (integer values one to nine), which within AHP is still an ongoing issue. The effect of using a different 9-unit scale within FAHP would further elucidate the sensitivity analysis issues.

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