Use of consistency index, expert prioritization and direct numerical inputs for generic fuzzy-AHP modeling: A process model for shipping asset management
Highlights
► In the recent literature, many studies do not present or check consistency of applied fuzzy-AHP surveys. ► The proposed method improves the conventional fuzzy-AHP method by Centric Consistency Index (CCI) control. ► This paper provides a standard procedure for direct numerical inputs without expert consultation. ► When the numerical results are comparable, ranking procedure is useful, time saving and capable of comparing priority. ► This expert prioritization framework is combined with fuzzy-AHP method for ranking the level of experience.
Introduction
Design and improvement of decision support systems have great potential in the literature. Many decision processes are highly complicated and investigation of expert choice has several pitfalls since expert judgment is subject to bias. Such bias includes underestimation, optimism and limited capacity for concurrent analysis of multi-factor problems (Tversky & Kahneman, 2000). Difficulties of accurate decision support are also common in series choice problems. For instance, an investment decision has many dimensions ranging from the purchasing of an asset to the management and supply of subsequent processes. In such complicated cases, a series of decisions should be applied to all the components of purchase-to-operation. Use of expert decision systems deals with a multi-factor preference problem and inconsistency is important in terms of confidence in preferences. In the existing literature, many scholars present studies on the FAHP method, but none of them discuss whether the decision matrices are consistent (Chang, 1996, Kahraman et al., 2004, Kreng and Wu, 2007, Mikhailov and Tsvetinov, 2004). Saaty, 1977, Saaty, 1980 first suggested the classical AHP method and emphasized on consistency control. Because of the long and complicated content of a pairwise comparison survey, subjects may lose concentration with the result that their responses may just be a simulation of random choices or somewhat better than random selection. Therefore, the survey process of AHP is a crucial element of accurate decision support.
Ramanathan and Ganesh (1994) discussed the group preference aggregation problem and pointed out that expert from different backgrounds and levels of expertise may cause disparities among the group decision matrix. Therefore, aggregation of individual decision matrices is suggested through by weighting expert judgments with a normalized scale.
In many of the AHP studies, factors of decision are selected from intangible linguistic terms. Many preference problems have tangible and comparable aspects which can be directly investigated without expert aid. For example, financial analysis outcome is an important input of investment appraisal and results are readily comparable by ranking according to cost or benefit perception. The highest rate of return on investment directly indicates superiority due to financial perspective. Investigation of such problems without these tangible data may cause lack of generality and rationality.
The proposed model, GF-AHP, deals with problems mentioned in previous discussions and suggests improving expert system performance by classification of decision makers and use of numerical data with the ranking procedure of Saaty (2008). GF-AHP also proposes a control procedure for decision matrix consistency as it is particularly suggested by the initial study of Saaty and Vargas (1987). Although it is not used in existing FAHP studies, GF-AHP contributes to the literature by developing a centric consistency index (CCI) which is an extended version of the geometric consistency index (GCI). CCI is a detection tool for inconsistent expert consultation and over the boundaries of CCI; the decision support survey should be replaced with collecting new responses. Rethinking of preferences ensures an additional opportunity to increase consensus among the expert group (Saaty & Vargas, 1987).
The remainder of this paper is organized as follows: Section 2 briefly describes the ship investment. Section 3 states the methodology used in this paper to investigate the multi-strategy selection. Next, Section 4 presents empirical study, application and result. Finally, conclusion is the subject of the last Section 5.
Section snippets
Brief description of the ship investment
World merchandise trade is broadly based on seaborne transportation. Therefore, Maritime transportation is a critical part of global economics. Economic growth and increase of shipping services are parallel issues since most of the world’s surface is covered by waterways. For example, the most important industrial product of the world, steel, is produced by two major raw materials, iron ore and coal. Both raw materials are mainly transferred from several exporting countries by merchant ships.
Fuzzy sets and triangular fuzzy numbers
The fuzzy set theory is developed to cope with the extraction of the principal outcome from a variety of information distantly and roughly (Zadeh, 1965). It is an effective instrument for modeling in the lack of comprehensive and accurate information. The fuzzy set theory is particularly applied in complex business, finance and management problems. A triangular fuzzy number is a particular fuzzy set Ã, and its membership function is a continuous linear function. Definition 1 Let X be universe of
Empirical study
The empirical study is designed in three layers of the decision process. In the first layer, the particulars of the shipping asset are selected. The second layer defines whether the owner should contract a management outsourcing service in part or completely. Finally, the third layer is designed for further management decisions such as defining crew nationality regime, selection of chartering techniques in the spot or period market, etc. According to the results of the second layer, crew
Conclusion
In the recent literature, the FAHP method has popularity and it is applied for several reasons. In spite of its popularity, the FAHP method has drawbacks such as lack of consistency control and incapability to execute DNI values. The present paper contributes to the literature by ensuring a proper consistency control mechanism, expertise classification and use of DNI values. The CCI method is developed for consistency control of fuzzy extended AHP studies. As discussed previously, many scholars
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