全局非均匀积性语言评估标度

提出了两种全局非均匀积性语言评估标度, 在决策过程中, 决策者可以根据实际需要调节这两种标度中相邻语言术语之间的偏差, 从而表明它们具有较好的灵活性. 最后给出了积性语言评估标度和加性语言评估标度之间的转换关系.     

Group decision making based on intuitionistic multiplicative aggregation operators

Preference relations are the most common techniques to express decision maker’s preference information over alternatives or criteria. To consistent with the law of diminishing marginal utility, we use the asymmetrical scale instead of the symmetrical one to express the information in intuitionistic fuzzy preference relations, and introduce a new kind of preference relation called the intuitionistic multiplicative preference relation, which contains two parts of information describing the intensity degrees that an alternative is or not priority to another. Some basic operations are introduced, based on which, an aggregation principle is proposed to aggregate the intuitionistic multiplicative preference information, the desirable properties and special cases are further discussed. Choquet Integral and power average are also applied to the aggregation principle to produce the aggregation operators to reflect the correlations of the intuitionistic multiplicative preference information. Finally, a method is given to deal with the group decision making based on intuitionistic multiplicative preference relations.     

Group preference aggregation with the AHP – implications for multiple-issue agendas

We extend different group preference aggregation procedures applied in the analytic hierarchy process (AHP) to multiple-issue decision problems. We demonstrate how existing procedures that are specifically developed for single-issue decisions will generally fail to generate Pareto optimal agreements when applied to multiple issues. By relating these procedures to formal concepts of social choice theory, we develop a utilitarian weighted arithmetic mean method of aggregation that ensures efficiency. Our approach thus provides a theoretical basis for designing the AHP to implement social choice functions in practice.     

A comparative study of the numerical scales and the prioritization methods in AHP

In the analytic hierarchy process (AHP), a decision maker first gives linguistic pairwise comparisons, then obtains numerical pairwise comparisons by selecting certain numerical scale to quantify them, and finally derives a priority vector from the numerical pairwise comparisons. In particular, the validity of this decision-making tool relies on the choice of numerical scale and the design of prioritization method. By introducing a set of concepts regarding the linguistic variables and linguistic pairwise comparison matrices (LPCMs), and by defining the deviation measures of LPCMs, we present two performance measure algorithms to evaluate the numerical scales and the prioritization methods. Using these performance measure algorithms, we compare the most common numerical scales (the Saaty scale, the geometrical scale, the Ma–Zheng scale and the Salo–Hämäläinen scale) and the prioritization methods (the eigenvalue method and the logarithmic least squares method). In addition, we also discuss the parameter of the geometrical scale, develop a new prioritization method, and construct an optimization model to select the appropriate numerical scales for the AHP decision makers. The findings in this paper can help the AHP decision makers select suitable numerical scales and prioritization methods.     

The role of weights and scales in the application of multiobjective decision making

The application of multiobjective solution techniques requires, at some point in the analysis, the specification of the preference structure of the decision maker over the set of objectives. In practice, this step must often take the form of determining a set of weights that attempts to quantify the relative importance of the various competing objectives. This paper looks at how some of the well known techniques make use of these weights in attempting to achieve the best compromise solution. The definition of the weights (i.e., what meaning they carry into the analysis) is also explored. It is found that one must be very careful about applying weights when using these techniques. A different set of weights for the same objectives may apply when implementing two different techniques. In addition, a related factor, the specification of scales or ranges over which the objectives are to be evaluated, is explored. Again, it is found that different techniques use these scales in ways that change the manner in which they should be defined. The techniques reviewed are compromise programming, multiattribute utility theory, ELECTRE and cooperative game theory. Concepts are illustrated through the use of a water resources example problem.     

Scale transitivity in the AHP

Transitivity is important in multicriteria decision-making. The analytic hierarchy process (AHP), as one of the widely used decision analysis tools, is criticized since it suffers from scale intransitivity. This paper first reviews and compares different scales from different aspects, then discusses the transitivity of AHP scales and derives a scale based on the transitivity, so it is naturally transitive. Besides, two approaches are provided to determine the scale parameter for the derived transitive scale. In order to deal with the transitivity problem, the AHP provides a consistency index for testing pairwise comparison consistency. So, finally, this paper proposes a consistency measure to reflect the judgmental inconsistency.     

Numerical scales generated individually for analytic hierarchy process

Because individual interpretations of the analytic hierarchy process (AHP) linguistic scale vary for each user, this study proposes a novel framework that AHP decision makers can use to generate numerical scales individually, based on the 2-tuple linguistic modeling of AHP scale problems. By using the concept of transitive calibration, individual characteristics in understanding the AHP linguistic scale are first defined. An algorithm is then proposed for detecting the individual characteristics from the linguistic pairwise comparison data that is associated with each of the AHP individual decision makers. Finally, a nonlinear programming model is proposed to generate individual numerical scales that optimally match the obtained individual characteristics. Two well-known numerical examples are re-examined using the proposed framework to demonstrate its validity.     

Multi-attribute decision-making in individual and social choice

This paper provides an analysis of individual and social decision criteria for alternatives that are composed of several attributes. We derive additive and multiplicative criteria for individual decision-making with new axioms and apply these criteria to obtain new justifications of known social choice rules with a bargaining interpretation, namely the generalized utilitarian and Nash social choice functions. Unlike most axiomatizations of bargaining solutions, our approach is, to a large extent, based on the multi-attribute structure of the underlying alternatives and the resulting individual decision criteria instead of axioms that impose restrictions on the choice function directly.     

Does AHP help us make a choice? An experimental evaluation

In this paper, we use experimental economics methods to test how well Analytic Hierarchy Process (AHP) fares as a choice support system in a real decision problem. AHP provides a ranking that we statistically compare with three additional rankings given by the subjects in the experiment: one at the beginning, one after providing AHP with the necessary pair-wise comparisons and one after learning the ranking provided by AHP. While the rankings vary widely across subjects, we observe that for each individual all four rankings are similar. Hence, subjects are consistent and AHP is, for the most part, able to replicate their rankings. Furthermore, while the rankings are similar, we do find that the AHP ranking helps the decision makers reformulate their choices by taking into account suggestions made by AHP.     

A new data envelopment analysis method for priority determination and group decision making in the analytic hierarchy process

The DEAHP method for weight deviation and aggregation in the analytic hierarchy process (AHP) has been found flawed and sometimes produces counterintuitive priority vectors for inconsistent pairwise comparison matrices, which makes its application very restrictive. This paper proposes a new data envelopment analysis (DEA) method for priority determination in the AHP and extends it to the group AHP situation. In this new DEA methodology, two specially constructed DEA models that differ from the DEAHP model are used to derive the best local priorities from a pairwise comparison matrix or a group of pairwise comparison matrices no matter whether they are perfectly consistent or inconsistent. The new DEA method produces true weights for perfectly consistent pairwise comparison matrices and the best local priorities that are logical and consistent with decision makers (DMs)’ subjective judgments for inconsistent pairwise comparison matrices. In hierarchical structures, the new DEA method utilizes the simple additive weighting (SAW) method for aggregation of the best local priorities without the need of normalization. Numerical examples are examined throughout the paper to show the advantages of the new DEA methodology and its potential applications in both the AHP and group decision making.     

Group preference aggregation in the multiplicative AHP The model of the group decision process and Pareto optimality

A recent paper has focused awareness on group aggregation procedures in the AHP, showing that geometric mean aggregation violates the desirable social choice axiom of Pareto optimality. We show that this violation can be attributed to the representation used to model the group decision process, thereby questioning the legitimacy of the Pareto optimality axiom. We furthermore propose a geometric mean group aggregation procedure which satisfies all the social choice axioms suggested.     

Geometry of decision making and the vector space formulation of the analytic hierarchy process

We extend the conventional Analytic Hierarchy Process (AHP) to an Euclidean vector space and develop formulations for aggregation of the alternative preferences with the criteria preferences. Relative priorities obtained from such a formulation are almost identical with the ones obtained using conventional AHP. Each decision is represented by a preference vector indicating the orientation of the decision maker's mind in the decision space spanned by the decision alternatives. This adds a geometric meaning to the decision making processes. We utilise the measure of similarity between any two decision makers and apply it for analysing decisions in a homogeneous group. We propose an aggregation scheme for calculating the group preference from individual preferences using a simple vector addition procedure that satisfies Pareto optimality condition. The results agree very well with the ones of conventional AHP.     

Additive preconditioning in matrix computations

We combine our novel SVD-free additive preconditioning with aggregation and other relevant techniques to facilitate the solution of a linear system of equations and other fundamental matrix computations. Our analysis and experiments show the power of our algorithms, guide us in selecting most effective policies of preconditioning and aggregation, and provide some new insights into these and related subjects. Compared to the popular SVD-based multiplicative preconditioners, our additive preconditioners are generated more readily and for a much larger class of matrices. Furthermore, they better preserve matrix structure and sparseness and have a wider range of applications (e.g., they facilitate the solution of a consistent singular linear system of equations and of the eigen-problem). (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Fuzzy logic modifications of the Analytic Hierarchy Process

The Analytic Hierarchy Process (AHP) is a measurement methodology based on pair-wise comparisons that relies on judgment to derive priority scales. During its implementation, one constructs hierarchies, then makes judgments or performs measurements on pairs of elements with respect to a criterion to derive preference scales, which are then synthesized throughout the structure to select the preferred alternative.One of the areas where the AHP finds application is in the subjective phases of risk assessment (RA), where it is used to structure and prioritize diverse risk factors, including the judgments of experts. Since fuzzy logic (FL) has been shown to be an effective tool for accommodating human experts and their communication of linguistic variables, there has been research aimed at modeling the fuzziness in the AHP (FAHP), and recently the focus of some of that modeling has been with respect to RA.The literature discusses more than one FAHP model, which raises the question as to which are the prominent models and what are their characteristics. In response to this question, we examine three of the most influential FAHP models. The article proceeds as follows. It begins with a brief overview of the AHP and its limitations when confronted with a fuzzy environment. This is followed by a discussion of FL modifications of the AHP. A RA-based likelihood score example is used throughout. The article ends with a commentary on the findings.     

An analysis of distributions of priority values from alternative comparison scales within AHP

The analytic hierarchy process (AHP) introduced by T.L. Saaty is a well known and popular method of multi-criteria decision making. Central to this method are the pairwise comparisons between criteria (and decision alternatives) made using a 9-unit scale. The appropriateness of Saaty's original one-to-nine (1–9) scale has been the subject of much debate and cause for concern. This paper contrasts the appropriateness of the 1–9 scale with other alternative 9-unit scales also used in AHP, by looking at the probability distributions of the associated priority values. For large problems, estimated probability distributions are found for the priority values through using the method of Parzen Windows.     

Retail price markup commitment in decentralized supply chains

We investigate the operational decisions and resulting profits for a supply chain facing price-dependent demand under a policy where there is an ex-ante commitment made on the retail price markup. We obtain closed-form solutions for this policy under the assumption of a multiplicative demand function and we analytically compare its performance with that of a traditional price-only policy. We compare these results to results obtained when demand follows a linear additive form. These formulations are shown to be qualitatively different as the manufacturer’s wholesale pricing decision is independent of the retail price markup commitment in the multiplicative case, but not when demand is linear additive. We demonstrate that the ex-ante commitment can lead to Pareto-improving solutions under linear additive demand, but not under the multiplicative demand function. We also consider the effect of pricing power in the supply chain by varying who determines the retail price markup.     

A two-phase algorithm for consensus building in AHP-group decision making

Group decision making through the AHP has received significant attention in contemporary research, the primary focus of which has been on the issues of consistency and consensus building. In this paper, we concentrate on the latter and present a two-phase algorithm based on the optimal clustering of decision makers (members of a group) into sub groups followed by consensus building both within sub groups and between sub groups. Two-dimensional Sammon’s mapping is proposed as a tool for generating an approximate visualization of sub groups identified in multidimensional vector space, while the consensus convergence model is suggested for reaching agreement amongst individuals in and between sub groups. As a given, all decision makers evaluate the same decision elements within the AHP framework and produce individual scores of these decision elements. The consensual scores are obtained through the iterative procedure and the final scores are declared as the group decision. The results of two selected numerical examples are compared with two sets of results: the results obtained by the commonly used geometric mean aggregation method and also the results obtained if the consensus convergence model is applied directly without the prior clustering of the decision makers. The comparisons indicated the expected differences among the aggregation schemes and the final group scores. The matrices of respect values in the consensus convergence model, obtained for cases when the decision makers are optimally clustered and when they are not, show that in the latter case the decision makers receive lower weights of respect from other members in the group. Various tests showed that our approach is efficient in cases when no clusters can be visually and undoubtedly identified, especially if the number of group members is high.     

On Non-additive Probabilistic Inequalities of H?lder-type

Non-additive measure is a generalization of additive probability measure. Integral inequalities play important roles in classical probability and measure theory. Some well-known inequalities such as the Minkowski inequality and the H?lder inequality play important roles not only in the theoretical area but also in application. Non-additive integrals are useful tools in several theoretical and applied statistics which have been built on non-additive measure. For instance, in decision theory and applied statistics, the use of the non-additive integrals can be envisaged from two points of view: decision under uncertainty and multi-criteria decision-making. In fact, the non-additive integrals provide useful tools in many problems in engineering and social choice where the aggregation of data is required. In this paper, H?lder and Minkowski type inequalities for semi(co)normed non-additive integrals are discussed. The main results of this paper generalize some previous results obtained by the authors.     

Scale space multiresolution correlation analysis for time series data

We propose a new scale space method for the discovery of structure in the correlation between two time series. The method considers the possibility that correlation may not be constant in time and that it might have different features when viewed at different time scales. The time series are first decomposed into additive components corresponding to their features in different time scales. Temporal changes in correlation between pairs of such components are then explored by using weighted correlation within a sliding time window of varying length. Bayesian, sampling-based inference is used to establish the credibility of the correlation structures thus found and the results of analyses are summarized in scale space feature maps. The performance of the method is demonstrated using one artificial and two real data sets. The results underline the usefulness of the scale space approach when the correlation between the time series exhibit time-varying structure in different scales.