A stochastic multicriteria acceptability analysis–evidential reasoning method for uncertain multiattribute decision‐making problems

Evidential reasoning (ER) is an effective approach for assessing alternatives with uncertain attribute values in the context of decision making. For the ER approach to be able to handle variations in the weights of uncertain attributes in an appropriate manner, this paper proposes a method to solve problems of uncertain multiattribute decision making that involve both uncertain attribute values and uncertain attribute weights, which this method does by combining the ER approach and stochastic multicriteria acceptability analysis‐2 (SMAA‐2). First, the uncertainty in attribute values is described by using a belief decision matrix as in the ER approach. The analytical ER algorithm is then used to create the utility function in the SMAA‐2 model, and that function is used to calculate the probability of different sorting positions of the decision units under weight‐related restrictions. Finally, the results of ranking are obtained by combining the sorting weights. An example is provided to verify the effectiveness of the proposed method.     

Distance‐based multicriteria group decision‐making approach with probabilistic linguistic term sets

Probabilistic linguistic term sets (PLTSs) are an important expression for hesitant linguistic preference information under group decision‐making circumstances. This study investigates problems of multicriteria group decision making (MCGDM) with PLTSs. A novel and rational comparison method is first proposed, and two distance measures for PLTSs are defined. The weight of each criterion is then obtained via maximum deviation method. Subsequently, extended Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS) ‐ VIseKriterijumska Optimizacija I Kompromisno Resenje a Serbian name (VIKOR) and TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) methods are developed as decision support models to handle MCGDM problems. An illustrative example is also analysed to demonstrate the rationality and feasibility of the proposed methods.     

Semantic dominance analysis for multicriteria decision‐making problems with unbalanced linguistic scale

The multicriteria decision‐making problem with an unbalanced linguistic scale is analyzed by multicriteria semantic dominance (MCSD) technique. The technique only requires the available partial semantics information about the scale. The MCSD rules on two types of unbalanced linguistic scale are introduced and proven. With these rules, alternatives are divided into two mutually exclusive sets called the efficient set and inefficient set. The decision maker with a specific type of linguistic scale will never choose the alternative from the corresponding inefficient set as the feasible one. Thus he/she can easily choose from the original alternative set. An example is also provided to illustrate the procedure of the proposed method.     

Effects of increasing fuzziness on analytic hierarchy process for spatial multicriteria decision analysis

Multicriteria decision analysis (MCDA) involves techniques which relatively recently have received great increase in interest for their capabilities of solving spatial decision problems. One of the most frequently used techniques of MCDA is Analytic Hierarchy Process (AHP). In the AHP, decision-makers make pairwise comparisons between different criteria to obtain values of their relative importance. The AHP initially only dealt with crisp numbers or exact values in the pairwise comparisons, but later it has been modified and adapted to also consider fuzzy values. It is necessary to empirically validate the ability of the fuzzified AHP for solving spatial problems. Further, the effects of different levels of fuzzification on the method have to be studied. In the context of a hypothetical GIS-based decision-making problem of locating a dam in Costa Rica using real-world data, this paper illustrates and compares the effects of increasing levels of uncertainty exemplified through different levels of fuzzification of the AHP. Practical comparison of the methods in this work, in accordance with the theoretical research, revealed that by increasing the level of uncertainty or fuzziness in the fuzzy AHP, differences between results of the conventional and fuzzy AHPs become more significant. These differences in the results of the methods may affect the final decisions in decision-making processes. This study concludes that the AHP is sensitive to the level of fuzzification and decision-makers should be aware of this sensitivity while using the fuzzy AHP. Furthermore, the methodology described may serve as a guideline on how to perform a sensitivity analysis in spatial MCDA. Depending on the character of criteria weights, i.e. the degree of fuzzification, and its impact on the results of a selected decision rule (e.g. AHP), the results from a fuzzy analysis may be used to produce sensitivity estimates for crisp AHP MCDA methods.     

New exponential operational laws and their aggregation operators for interval‐valued Pythagorean fuzzy multicriteria decision‐making

In this article, we define two new exponential operational laws about the interval‐valued Pythagorean fuzzy set (IVPFS) and their corresponding aggregation operators. However, the exponential parameters (weights) of all the existing operational laws of IVPFSs are crisp values in IVPFS decision‐making problems. As a supplement, this paper first introduces new exponential operational laws of IVPFS, where bases are crisp values or interval numbers and exponents are interval‐valued Pythagorean fuzzy numbers. The prominent characteristic of these proposed operations is studied. Based on these laws, we develop some new weighted aggregation operators, namely the interval‐valued Pythagorean fuzzy weighted exponential averaging operator and the dual interval‐valued Pythagorean fuzzy weighted exponential averaging. Finally, a decision‐making approach is presented based on these operators and illustrated with some numerical examples to validate the developed approach.     

Signed distance‐based ORESTE for multicriteria group decision‐making with multigranular unbalanced hesitant fuzzy linguistic information

The objective of this study is to develop an integrated approach for solving multicriteria group decision‐making problems with multigranular unbalanced hesitant fuzzy linguistic term sets (HFLTSs). Firstly, a signed distance‐based transformation function is proposed to unify multigranular unbalanced hesitant fuzzy linguistic (HFL) assessments. Secondly, a mathematical programming model based on the maximum consensus is constructed to allocate decision‐makers (DMs)' weights objectively. Thirdly, a new signed distance‐based preference score function is defined to aggregate HFL assessments and determine the weak ranking of alternatives, and a novel preference, indifference, and incomparability test framework is constructed to identify the subtle relations among alternatives. On these bases, a signed distance‐based ORESTE (Organísation, rangement et Synthèse de données relarionnelles, in French) method, in which knowledge regarding criterion values and weights are expressed as multigranular unbalanced HFLTSs, is developed to obtain the ranking of alternatives. Finally, an illustrative example, followed by sensitivity and comparative analyses, is presented to verify the feasibility and effectiveness of the proposed approach.     

A linguistic multicriteria decision‐making model applied to hotel service quality evaluation from web data sources

Currently, there are a large number of hotel Web sites that develop their own seals of quality based on customer feedback. As a result, a hotel can be classified differently by various Web sites at the same time, creating confusion in the consumer perceptions about the quality of a given hotel. Moreover, there are attempts to standardize such service quality evaluation, such as the SERVQUAL instrument, which is a multiple‐item scale for measuring service quality with several dimensions. In this context, we present a two‐stage linguistic multicriteria decision‐making model to integrate the hotel guests' opinions included in several Web sites, with two objectives: on the one hand, obtaining a SERVQUAL scale evaluation value of service quality with the integrated answers of the input opinions; on the other hand, getting a SERVQUAL overall evaluation value of service quality. This model is incorporated into an opinion aggregation architecture to integrate heterogeneous data (natural language included) from various tourism Web sites. As a particular case study, we show an application example using the high‐end hotels located in Granada (Spain). © 2012 Wiley Periodicals, Inc.     

Selection of computer-integrated manufacturing technologies using a combined analytic hierarchy process and goal programming model

Investment in computer-integrated manufacturing (CIM) is a multi-attribute problem, which includes both qualitative and quantitative factors. In order to select the best set of CIM alternatives among the competing ones it is necessary to make a trade off between the quantative and qualitative factors some of which may conflict. Selection of appropriate CIM alternatives is vital in manufacturing companies’ long-term competitiveness, and it requires development of selection models. The selection model must consider various quantative and qualitative objectives and constraints simultaneously. For example, companies have generally limited funds to invest in new advanced technology. The size of the allocated fund limits the types and number of CIM alternatives a company can select in a given time period. In this article, a combined model of the analytical hierarchy process and goal programming is proposed to consider multiple objectives and constraints simultaneously. A real-world example is provided to illustrate the application of the combined approach.     

Linguistic Pythagorean fuzzy sets and its applications in multiattribute decision‐making process

In this article, a new linguistic Pythagorean fuzzy set (LPFS) is presented by combining the concepts of a Pythagorean fuzzy set and linguistic fuzzy set. LPFS is a better way to deal with the uncertain and imprecise information in decision making, which is characterized by linguistic membership and nonmembership degrees. Some of the basic operational laws, score, and accuracy functions are defined to compare the two or more linguistic Pythagorean fuzzy numbers and their properties are investigated in detail. Based on the norm operations, some series of the linguistic Pythagorean weighted averaging and geometric aggregation operators, named as linguistic Pythagorean fuzzy weighted average and geometric, ordered weighted average and geometric with linguistic Pythagorean fuzzy information are proposed. Furthermore, a multiattribute decision‐making method is established based on these operators. Finally, an illustrative example is used to illustrate the applicability and validity of the proposed approach and compare the results with the existing methods to show the effectiveness of it.     

Using analytic hierarchy process (AHP) to improve human performance: An application of multiple criteria decision making problem

In the global economy, the modern commercial and industrial organization needs to develop better methods of assessing the performance of the human resource than simply using performance measures such as efficiency or effectiveness. As organizations seek more aggressive ways to cut costs and to increase global competitiveness, the importance of establishing and sustaining high levels of employee performance increases. The main purpose of this paper is to solve the human performance improvement problem by employing Analytic Hierarchy Process (AHP) method. Decision makers (DMs) often deal with problems that involve multiple criteria. At given moments in time, companies will display characteristics that make certain factors; key factors in their competences. In this paper, we present a model, which illustrates the relations and importance between human performance improvement and the style of management. In using the AHP to model this problem, we developed a hierarchic structure to represent the problem of human performance management and made pairwise comparisons. In this paper, the AHP is suggested as a tool for implementing a multiple criteria performance improvement scheme. The AHP was used for the purpose of structuring and clarifying the relations and importance between human performance improvement and the style of management. The study found that in terms of company culture, participation, human capability, and attitudes the best management style in improving human performance is management by values.     

Monitoring healthcare performance by analytic hierarchy process: a developing-country perspective

The formulation and solution of multi‐criteria healthcare decision problems is of critical importance to the health and socio‐economic betterment of developing countries. The study shows how the multi‐criteria decision‐making method could facilitate implementation of healthcare performance analysis, especially for the public healthcare system of Bangladesh, which operates mainly through thana health complexes (THCs). We include outreach services and rural facilities together with ongoing THC activities, and analyze their relative performance. The methodology uses a phase of the Delphi method and of the analytic hierarchy process (AHP) approach. The outcome of Delphi is used as input for the hierarchical processing procedure in AHP and determines performance order of the healthcare activities. Results from AHP are discussed for implementation in decision‐making and the managerial policymaking process, towards improvement of overall healthcare performance.     

Codifying a crisis: Progressing from information sharing to distributed decision‐making

A key challenge in crisis management is maintaining an adequate information position to support coherent decision‐making between a range of actors. Such distributed decision‐making is often supported by a common operational picture that not only conveys factual information but also attempts to codify a dynamic and vibrant crisis management process. In this paper, we explain why it is so difficult to move from information sharing towards support for distributed decision‐making. We argue that two key processes need to be considered: supporting both the translation of meaning and the transformation of interests between those on the front line and those in the remote response network. Our analysis compares the information‐sharing processes in three large‐scale emergency response operations in the Netherlands. Results indicate that on several occasions the collaborative decision‐making process was hampered because actors limited themselves to factual information exchange. The decision‐making process only succeeds when actors take steps to resolve their varying interpretations and interests. This insight offers important lessons for improving information management doctrines and for supporting distributed decision‐making processes.     

New analytical hierarchical process/data envelopment analysis methodology for ranking decision‐making units

The analytical hierarchical process/data envelopment analysis (AHP/DEA) methodology for ranking decision‐making units (DMUs) has some problems: it illogically compares two DMUs in a DEA model; it is not compatible with DEA ranking in the case of multiple inputs/multiple outputs; and it leads to weak discrimination in cases where the number of inputs and outputs is large. In this paper, we propose a new two‐stage AHP/DEA methodology for ranking DMUs that removes these problems. In the first stage, we create a pairwise comparison matrix different from AHP/DEA methodology; the second stage is the same as AHP/DEA methodology. Numerical examples are presented in the paper to illustrate the advantages of the new AHP/DEA methodology.     

Use of the analytic hierarchy process in ergonomic analysis

The paper deals with the application of the Analytic Hierarchy Process (AHP) in decision making and conflict resolution for ergonomic evaluation. The procedure is illustrated through the selection of Manual Materials Handling Guidelines by comparing NIOSH standards with European Coal and Steel Community (ECSC) lifting guidelines. Based on 11 criteria proposed by Frievalds (1987), the NIOSH Lifting Guidelines have been found to better represent sagittal two-handed lifting than the ECSC guidelines. The implications of the adaptation of the procedure are briefly discussed.     

How cognitive and affective aspects can influence the outcome of the group decision‐making process

Supporting group decision‐making when the decision makers are spread around the world is a complex process. The mechanisms of automated negotiation, such as argumentation, can be used in Ubiquitous Group Decision Support Systems (UbiGDSS) to help decision makers find a solution based on their preferences. However, the decision‐making process is much more than just a simple criteria and alternative analysis. There are many cognitive and affective issues that affect the outcome, and these issues should not be ignored; otherwise, the quality of the decision could be compromised. In this paper, we detail an UbiGDSS architecture and explore 2 cognitive and affective methods that are essential to the group decision‐making process. We explain how agents can reason about self‐expertise and other decision makers' credibility, and how agents can verify and react to tendencies throughout the decision‐making process. We intend agents to achieve higher quality and more consensual decisions. In any simulation environment that we tested, agents that analysed credibility, expertise, and/or analysed tendencies always achieved a higher consensus compared to agents that used neither of the proposed methods. Likewise, agents that used neither of the proposed methods or only performed tendencies analysis obtained the worst average satisfaction levels for each simulation environment.     

The voting analytic hierarchy process method for discriminating among efficient decision making units in data envelopment analysis

Making optimal use of available resources has always been of interest to humankind, and different approaches have been used in an attempt to make maximum use of existing resources. Limitations of capital, manpower, energy, etc., have led managers to seek ways for optimally using such resources. In fact, being informed of the performance of the units under the supervision of a manager is the most important task with regard to making sensible decisions for managing them. Data envelopment analysis (DEA) suggests an appropriate method for evaluating the efficiency of homogeneous units with multiple inputs and multiple outputs. DEA models classify decision making units (DMUs) into efficient and inefficient ones. However, in most cases, managers and researchers are interested in ranking the units and selecting the best DMU. Various scientific models have been proposed by researchers for ranking DMUs. Each of these models has some weakness(es), which makes it difficult to select the appropriate ranking model. This paper presents a method for ranking efficient DMUs by the voting analytic hierarchy process (VAHP). The paper reviews some ranking models in DEA and discusses their strengths and weaknesses. Then, we provide the method for ranking efficient DMUs by VAHP. Finally we give an example to illustrate our approach and then the new method is employed to rank efficient units in a real world problem.     

Parameter reductions in N‐soft sets and their applications in decision‐making

Parameter reduction is an important operation for improving the performance of decision‐making processes in various uncertainty theories. The theory of N‐soft sets is emerging as a powerful mathematical tool for dealing with uncertainties beyond the standard formulation of the soft set theory. In this research article, we extend the notion of parameter reduction to N‐soft set theory, and we also justify its practical calculation. To this purpose, we define related theoretical concepts (e.g. N‐soft subset, reduct N‐soft set and redundant parameter) and examine some of their fundamental properties. Then, we argue that the idea of attributes reduction from the rough set theory cannot be employed in the N‐soft set theory in order to reduce the number of parameters. Consequently, we take an original position in order to adequately define and compute parameter reductions in N‐soft sets. Finally, we develop an application of parameter reduction of N‐soft sets.     

Hierarchical sensitivity analysis of priority used in analytic hierarchy process

The analytic hierarchy process (AHP) initialed by Saaty (1980) has recently been attracting attention as a useful support method for multi-objective decision making. This paper presents some useful theorems for the sensitivity analysis of priority that is playing an important role in the AHP. These theorems are derived from the principle of hierarchical composition which is expressed in the form of a reachability matrix. Using the theorems, we can easily calculate the degrees of effects caused by local or global changes in the priorities of some criteria, and also we can examine the possibility of rank reversal among alternatives. Furthermore, by applying these theorems to a dwelling selection problem, this paper verifies their effectiveness.     

On the valuation of alternatives for decision‐making under uncertainty

We focus on the problem of decision‐making in the face of uncertainty. The issue of the representation of uncertain information is considered and a number of different frameworks are described: possibilistic, probabilistic, belief structures, and graded possibilistic. We suggest methodologies for decision‐making in these different environments. The importance of decision attitude in the construction of decision functions is strongly emphasized. © 2002 Wiley Periodicals, Inc.