Expert Preferences Research Articles

Attitudinal ranking and correlated aggregating methods for multiple attribute group decision making with triangular intuitionistic fuzzy Choquet integral

Purpose – The purpose of this paper is to propose novel attitudinal prioritization and correlated aggregating methods for multiple attribute group decision making (MAGDM) with triangular intuitionistic fuzzy Choquet integral. Design/methodology/approach – Based on the continuous ordered weighted average (COWA) operator, the triangular fuzzy COWA (TF-COWA) operator is defined, and then a novel attitudinal expected score function for triangular intuitionistic fuzzy numbers (TIFNs) is investigated. The novelty of this function is that it allows the prioritization of TIFNs by taking account of the expert’s attitudinal character. When the ranking order of TIFNs is determined, the triangular intuitionistic fuzzy correlated geometric (TIFCG) operator and the induced TIFCG (I-TIFCG) operator are developed. Findings – Their use is twofold: first, the TIFCG operator is used to aggregate the correlative attribute value; and second, the I-TIFCG operator is designed to aggregate the preferences of experts with some degree of inter-dependent. Then, a TIFCG and I-TIFCG operators-based approach is presented for correlative MAGDM problems. Finally, the propose method is applied to select investment projects. Originality/value – Based on the TIFCG and I-TIFCG operators, this paper proposes a novel correlated aggregating methods for MAGDM with triangular intuitionistic fuzzy Choquet integral. This method helps to solve the correlated attribute (criteria) relationship. Furthermore, by the attitudinal expected score functions of TIFNs, the propose method can reflect decision maker’s risk attitude in the final decision result.

Making Good Decisions in Healthcare with Multi-Criteria Decision Analysis: The Use, Current Research and Future Development of MCDA

Healthcare decision making is usually characterized by a low degree of transparency. The demand for transparent decision processes can be fulfilled only when assessment, appraisal and decisions about health technologies are performed under a systematic construct of benefit assessment. The benefit of an intervention is often multidimensional and, thus, must be represented by several decision criteria. Complex decision problems require an assessment and appraisal of various criteria; therefore, a decision process that systematically identifies the best available alternative and enables an optimal and transparent decision is needed. For that reason, decision criteria must be weighted and goal achievement must be scored for all alternatives. Methods of multi-criteria decision analysis (MCDA) are available to analyse and appraise multiple clinical endpoints and structure complex decision problems in healthcare decision making. By means of MCDA, value judgments, priorities and preferences of patients, insurees and experts can be integrated systematically and transparently into the decision-making process. This article describes the MCDA framework and identifies potential areas where MCDA can be of use (e.g. approval, guidelines and reimbursement/pricing of health technologies). A literature search was performed to identify current research in healthcare. The results showed that healthcare decision making is addressing the problem of multiple decision criteria and is focusing on the future development and use of techniques to weight and score different decision criteria. This article emphasizes the use and future benefit of MCDA.

Automated Discovery and Utilization of Tacit Knowledge in Facility Layout Planning and Optimization

This paper puts forth a novel methodology for facilities layout planning and optimization, where the fitness evaluation of layout alternatives is automatically performed by employing an artificial neural network trained to preferences of the domain experts. The inherently uncertain, unstructured, and often tacit nature of facilities layout design preferences, constraints, and fitness objectives demands the use of domain experts for the fitness evaluation of layout alternatives, which is a resource-intensive process. Indeed, the usual unavailability of domain experts in a timely or economical manner highlights the need for resorting to the use of some intelligent and effective automation technique in this important domain. In order to test the key novel ingredient of the proposed approach, a variety of artificial neural networks are trained on a large data set containing both qualitative and quantitative fitness values of layout alternatives, as well as subjective rankings by a seasoned domain expert utilizing the knowledge of the application domain. Simulation results strongly support the viability of the proposed idea. Such an automated approach to fitness evaluations of layout alternatives is expected to significantly increase the efficacy and efficiency of the overall facilities layout planning process. Moreover, such an approach would spur the much sought for research in decision support and expert systems in layout planning. As such, the paper also provides some very interesting and promising, albeit challenging, future research directions.

SETTING THE WEIGHTS OF SUSTAINABILITY CRITERIA FOR THE APPRAISAL OF TRANSPORT PROJECTS

Although the Multi-Criteria Decision Analysis (MCDA) has made progress towards appraising and measuring the performance of smart and sustainable transport projects, it still has important issues that need to be addressed such as the problem associated with incomparable quantities, the inherent subjective qualitative assessment, the complexity of identifying impacts to be included and its measurement method, and the corresponding weights. The issue of trading-off different sustainability criteria is the main unresolved matter. This problem may lead to lack of accuracy in the decision making process. This paper presents a new methodology to set the weights of the sustainability criteria used in the MCDA in order to reduce subjectivity and imprecision. We suggest eliciting criteria weights based on both expert preferences and the importance that the sustainability criteria have in the geographical and social context where the project is developed. This novel methodology is applied to a real case study to quantify sustainable practices associated with the design and construction of a new roadway in Spain. The outcome demonstrates that the approach to the weighting problem has significance and general application in a multi-criteria evaluation process.

Quantifying ecosystem quality by modeling multi-attribute expert opinion.

The evaluation of ecosystem quality is inherently subjective, requiring decisions about which variables to notice or measure, and how these variables are integrated into a coherent evaluation. Despite the central role of human judgment, few evaluation methods address the subjectivity that is inherent in their design. There are, however, advantages to directly using opinion to create an expert system where the metric is constructed around opinion data. These advantages include stakeholder inclusion and the encouragement of a dialogue of data-driven criticism rather than subjective counter-opinion. We create an expert system to express the quality of a grassland ecosystem in Australia. We use an ensemble of bagged regression trees trained on calibrated expert preference data, to model the perceived quality of this grassland using a set of eight site variables as inputs. The model provides useful predictions of grassland quality, producing predictions similar to real expert evaluations of independent synthetic test sites not used to train the model. We apply the model to real grassland sites ranging from pristine to highly degraded, and confirm that our model orders the sites according to their degree of modification. We demonstrate that the use of too few experts produces relatively poor results, and show that for our problem the use of data from over twenty experts is appropriate. The scaling approach we used to calibrate between-expert data is shown to be an appropriate mechanism for aggregating the opinions of multiple experts. The resultant model will be useful in many contexts, and can be used by managers as a tool to evaluate real sites. It can also be integrated into ecological models of change as a means of evaluating predicted changes, for example, as a measure of utility when combined with cost estimates. The basic approach demonstrated here is applicable to any ecosystem, and we discuss the opportunities and limitations of its wider use.

Group Aggregation Techniques for Analytic Hierarchy Process and Analytic Network Process: A Comparative Analysis

The analytic hierarchy process (AHP) and the analytic network process are important multiple criteria decision making methods for supporting complex, discrete strategic management decision problems. In order to exploit a broader information basis as well as to achieve a sufficient degree of objectivity strategic decision settings are mostly embedded into a multi-personal decision context to which different individuals with expert status contribute. Owing to the fact that there is a vast number of different methods and further internal possibilities (derivation of means) to aggregate the individual expert preferences to a group consensus, the first aim of this paper is to present a comprehensive literature review on various aggregation possibilities. The second aim is the conduction of a transparent comparative analysis of selected approaches and methods (geometric/arithmetic aggregation of individual judgments, geometric/arithmetic aggregation of individual priorities, geometric/arithmetic loss function approach and Group AHP model). Therefore, we use four different evaluation scenarios and point out under which assumptions which solution is suitable. Starting from these results, the aggregation techniques adequate to a specific decision context are provided.

Multi-criteria group decision making with incomplete hesitant fuzzy preference relations

In order to simulate the hesitancy and uncertainty associated with impression or vagueness, a decision maker may give her/his judgments by means of hesitant fuzzy preference relations in the process of decision making. The study of their consistency becomes a very important aspect to avoid a misleading solution. This paper defines the concept of additive consistent hesitant fuzzy preference relations. The characterizations of additive consistent hesitant fuzzy preference relations are studied in detail. Owing to the limitations of the experts’ professional knowledge and experience, the provided preferences in a hesitant fuzzy preference relation are usually incomplete. Consequently, this paper introduces the concepts of incomplete hesitant fuzzy preference relation, acceptable incomplete hesitant fuzzy preference relation, and additive consistent incomplete hesitant fuzzy preference relation. Then, two estimation procedures are developed to estimate the missing information in an expert's incomplete hesitant fuzzy preference relation. The first procedure is used to construct an additive consistent hesitant fuzzy preference relation from the lowest possible number, (n−1), of pairwise comparisons. The second one is designed for the estimation of missing elements of the acceptable incomplete hesitant fuzzy preference relations with more known judgments. Moreover, an algorithm is given to solve the multi-criteria group decision making problem with incomplete hesitant fuzzy preference relations. Finally, a numerical example is provided to illustrate the solution processes of the developed algorithm and to verify its effectiveness and practicality.

A study of crowdsourced segment-level surgical skill assessment using pairwise rankings.

Currently available methods for surgical skills assessment are either subjective or only provide global evaluations for the overall task. Such global evaluations do not inform trainees about where in the task they need to perform better. In this study, we investigated the reliability and validity of a framework to generate objective skill assessments for segments within a task, and compared assessments from our framework using crowdsourced segment ratings from surgically untrained individuals and expert surgeons against manually assigned global rating scores. Our framework includes (1) a binary classifier trained to generate preferences for pairs of task segments (i.e., given a pair of segments, specification of which one was performed better), (2) computing segment-level percentile scores based on the preferences, and (3) predicting task-level scores using the segment-level scores. We conducted a crowdsourcing user study to obtain manual preferences for segments within a suturing and knot-tying task from a crowd of surgically untrained individuals and a group of experts. We analyzed the inter-rater reliability of preferences obtained from the crowd and experts, and investigated the validity of task-level scores obtained using our framework. In addition, we compared accuracy of the crowd and expert preference classifiers, as well as the segment- and task-level scores obtained from the classifiers. We observed moderate inter-rater reliability within the crowd (Fleiss' kappa, κ = 0.41) and experts (κ = 0.55). For both the crowd and experts, the accuracy of an automated classifier trained using all the task segments was above par as compared to the inter-rater agreement [crowd classifier 85 % (SE 2 %), expert classifier 89 % (SE 3 %)]. We predicted the overall global rating scores (GRS) for the task with a root-mean-squared error that was lower than one standard deviation of the ground-truth GRS. We observed a high correlation between segment-level scores (ρ ≥ 0.86) obtained using the crowd and expert preference classifiers. The task-level scores obtained using the crowd and expert preference classifier were also highly correlated with each other (ρ ≥ 0.84), and statistically equivalent within a margin of two points (for a score ranging from 6 to 30). Our analyses, however, did not demonstrate statistical significance in equivalence of accuracy between the crowd and expert classifiers within a 10 % margin. Our framework implemented using crowdsourced pairwise comparisons leads to valid objective surgical skill assessment for segments within a task, and for the task overall. Crowdsourcing yields reliable pairwise comparisons of skill for segments within a task with high efficiency. Our framework may be deployed within surgical training programs for objective, automated, and standardized evaluation of technical skills.

Comparison of Three Major Forest Types of Mid Hills Region of Nepal for Conservation and Local Benefits

Nepal’s historically successful practice of community forestry has demonstrated the importance of encouraging relevant stakeholder groups to participate in the decision-making process and foster successful management and use of forest resources. Concerns over potential inequity in policy construction can be mitigated by fully involving experts as well as local community forest users whose daily subsistence is heavily dependent on forest products. This study analyzes the preferences of conservation experts and community forest users in consideration of three forest types in the Mid Hills region of Nepal: Pinus roxburghii, Alnus nepalensis and Schima-Castanopsis. All three have distinct benefits for conservation as well as for the provision of forest products and potential to improve the local economy and livelihoods. Focus group discussions were conducted with both groups of stakeholders to identify and later compare local and conservation benefits for the three forest types with the help of the analytic hierarchy process through repeated pairwise comparisons. Overall, this study finds that Schima-Castanopsis is the preferred forest type for satisfying both the subsistence needs of local forest users and the conservation concerns of forestry experts. This study also addresses the importance of combining the views of both groups in order to create a representative and effective forest management policy.

A multi-attribute large group emergency decision making method based on group preference consistency of generalized interval-valued trapezoidal fuzzy numbers

In this paper, a new decision making approach is proposed for the multi-attribute large group emergency decision-making problem that attribute weights are unknown and expert preference information is expressed by generalized interval-valued trapezoidal fuzzy numbers (GITFNs). Firstly, a degree of similarity formula between GITFNs is presented. Secondly, expert preference information on different alternatives is clustered into several aggregations via the fuzzy clustering method. As the clustering proceeds, an index of group preference consistency is introduced to ensure the clustering effect, and then the group preference information on different alternatives is obtained. Thirdly, the TOPSIS method is used to rank the alternatives. Finally, an example is taken to show the feasibility and effectiveness of this approach. These method can ensure the consistency degree of group preference, thus decision efficiency of emergency response activities can be improved.

Korean Medication Algorithm Project for Bipolar Disorder: third revision.

ObjectiveTo constitute the third revision of the guidelines for the treatment of bipolar disorder issued by the Korean Medication Algorithm Project for Bipolar Disorder (KMAP-BP 2014).MethodsA 56-item questionnaire was used to obtain the consensus of experts regarding pharmacological treatment strategies for the various phases of bipolar disorder and for special populations. The review committee included 110 Korean psychiatrists and 38 experts for child and adolescent psychiatry. Of the committee members, 64 general psychiatrists and 23 child and adolescent psychiatrists responded to the survey.ResultsThe treatment of choice (TOC) for euphoric, mixed, and psychotic mania was the combination of a mood stabilizer (MS) and an atypical antipsychotic (AAP); the TOC for acute mild depression was monotherapy with MS or AAP; and the TOC for moderate or severe depression was MS plus AAP/antidepressant. The first-line maintenance treatment following mania or depression was MS monotherapy or MS plus AAP; the first-line treatment after mania was AAP monotherapy; and the first-line treatment after depression was lamotrigine (LTG) monotherapy, LTG plus MS/AAP, or MS plus AAP plus LTG. The first-line treatment strategy for mania in children and adolescents was MS plus AAP or AAP monotherapy. For geriatric bipolar patients, the TOC for mania was AAP/MS monotherapy, and the TOC for depression was AAP plus MS or AAP monotherapy.ConclusionThe expert consensus in the KMAP-BP 2014 differed from that in previous publications; most notably, the preference for AAP was increased in the treatment of acute mania, depression, and maintenance treatment. There was increased expert preference for the use of AAP and LTG. The major limitation of the present study is that it was based on the consensus of Korean experts rather than on experimental evidence.

A Selection Method Based on MAGDM with Interval-Valued Intuitionistic Fuzzy Sets

As the cloud computing develops rapidly, more and more cloud services appear. Many enterprises tend to utilize cloud service to achieve better flexibility and react faster to market demands. In the cloud service selection, several experts may be invited and many attributes (indicators or goals) should be considered. Therefore, the cloud service selection can be regarded as a kind of Multiattribute Group Decision Making (MAGDM) problems. This paper develops a new method for solving such MAGDM problems. In this method, the ratings of the alternatives on attributes in individual decision matrices given by each expert are in the form of interval-valued intuitionistic fuzzy sets (IVIFSs) which can flexibly describe the preferences of experts on qualitative attributes. First, the weights of experts on each attribute are determined by extending the classical gray relational analysis (GRA) into IVIF environment. Then, based on the collective decision matrix obtained by aggregating the individual matrices, the score (profit) matrix, accuracy matrix, and uncertainty (risk) matrix are derived. A multiobjective programming model is constructed to determine the attribute weights. Subsequently, the alternatives are ranked by employing the overall scores and uncertainties of alternatives. Finally, a cloud service selection problem is provided to illustrate the feasibility and effectiveness of the proposed methods.

Benchmarking and target setting with expert preferences: An application to the evaluation of educational performance of Spanish universities

Abstract Benchmarking and target setting should identify best practices that are not only technically achievable but also desirable in the light of prior knowledge and expert opinion. It should also be considered the possibility of finding targets by minimizing the gap between actual and efficient performances, so that the units under evaluation can achieve these targets with less effort. We extend here the DEA models that provide closest targets for use when expert preferences are incorporated into the analysis. This approach is illustrated by applying the model proposed to the evaluation of educational performance of public Spanish universities.

Assessing the sustainability of small wastewater treatment systems: A composite indicator approach

The assessment of the sustainability of wastewater treatment (WWT) systems has gained interest in recent years. However, most previous studies have focused on environmental and/or economic dimensions ignoring social aspects. Moreover, they tend to be based on sets of indicators rather than providing a holistic assessment. To overcome this limitation, this paper proposes an innovative methodology to assess the sustainability of WWT systems based on the development of a composite indicator embracing economic, environmental and social issues. Subsequently, the global sustainability of seven WWT technologies for secondary treatment in small communities is compared. The joint application of the analytical hierarchical process (AHP) to assign weights to each indicator allows the incorporation of the preferences of experts. Initially, the global sustainability of the WWT technologies evaluated is quite similar. However, a scenario analysis illustrates that constructed wetlands technology is the most sustainable in five out of the seven scenarios evaluated. Moreover, extended aeration and rotating biological contactors are identified as the technologies with the lowest variability in their sustainability. Hence, in an uncertain context, they might be considered the preferred options. The proposed approach contributes to ease of interpretation of a complex problem such as the selection of the most sustainable WWT alternative.

Large Naval Ship Evaluation Method Attended by Multiple Experts

An integrating, coordinating and optimization for experts’ evaluate opinion method according to naval ship evaluation case study is researched. Ship projects evaluation index system is proposed pointing at large surface naval ship, meanwhile the weights of index of experts’ opinion and ship projects are identified. After combining the actual demand of evaluation with the algorithm, a sequence deviation estimating method is formed for estimating the difference between individual and group opinions. Thus a naval ship evaluation platform is proposed which could integrate, coordinate and optimize expert's opinion effectively. Calculation shows this new method system, which mixed the numerical computing and computer iteration technology together, could meet the actual demand of naval ship evaluation commendably. Not only can it retain each expert's original preference, but also consistent the consulting result rapidly. Thus the new method has great applicability in naval ship evaluating progress, with the ability of solving conflict among the expert group.

Using ecological attributes as criteria for the selection of plant species under three restoration scenarios

We used a conjoint analysis to reveal the preferences of experts with respect to plant attributes under three different restoration scenarios (high-level conservation, functional native vegetation, perennial native pasture) and to generate prioritized lists for restoration of grassy woodland species. Nineteen experts participated in the conjoint analysis. The sample comprised researchers and practitioners with local knowledge of grassy ecosystems. The survey involved repeated pairwise ranking of the relevance of attributes of seven ecological criteria. The relative weightings of the attributes were then used to generate the 50 top-ranked species for each of the three scenarios. Overall phosphorus tolerance was considered the most important criterion, followed by grazing tolerance. Species favoured for high-level conservation management included nutrient- and grazing-intolerant plants with narrower distributions and some species of threatened status. The two scenarios with histories of fertilization and varying levels of ongoing grazing were most similar in that their lists were dominated by graminoids and did not contain any shrubs or geophytes. The ranking of species provides an initial list that could be tailored to take into account specific site conditions and additional knowledge of species. This approach to the selection of species shows promise as either a repeatable process to select species for particular sites, or to generate a classification of species that could be used generically for a small number of common situations.

Assessing Trade‐offs among Multiple Objectives for Humanitarian Aid Delivery Using Expert Preferences

Humanitarian aid agencies deliver emergency supplies and services to people affected by disasters. Scholars and practitioners have developed modeling approaches to support aid delivery planning, but they have used objective functions with little validation as to the trade‐offs among the multiple goals of aid delivery. We develop a method to value the performance of aid delivery plans based on expert preferences over five key attributes: the amount of cargo delivered, the prioritization of aid by commodity type, the prioritization of aid by delivery location, the speed of delivery, and the operational cost. Through a conjoint analysis survey, we measure the preferences of 18 experienced humanitarian logisticians. The survey results quantify the importance of each attribute and enable the development of a piecewise linear utility function that can be used as an objective function in optimization models. The results show that the amount of cargo delivered is the most valued objective and cost the least important. In addition, experts prioritize more vulnerable communities and more critical commodities, but not to the exclusion of others. With these insights and the experts’ utility functions, better humanitarian objective functions can be developed to enable better aid delivery in emergency response.

Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study

Consensus reaching processes play an increasingly important role in the resolution of group decision making problems: a solution acceptable to all the experts participating in a problem is necessary in many real-life contexts. A large number of consensus approaches have been proposed to support groups in such processes, each one with its own characteristics, such as the methods utilized for the fusion of information regarding the preferences of experts. Given this variety of existing approaches in the literature to support consensus reaching processes, this paper considers two main objectives. Firstly, we propose a taxonomy that provides an overview and categorization of some existing consensus models for group decision making problems defined in a fuzzy context, taking into account the main features of each model. Secondly, the paper presents AFRYCA, a simulation-based analysis framework for the resolution of group decision making problems by means of different consensus models. The framework is aimed at facilitating a study of the performance of each consensus model, as well as determining the most suitable model/s for the resolution of a specific problem. An experimental study is carried out to show the usefulness of the framework.

The Alchian-Allen Theorem and the Economics of Internet Animals

The Alchian-Allen Theorem and the Economics of Internet Animals

Multi-attribute decision analysis under a linguistic hesitant fuzzy environment

In this paper, a new class of fuzzy sets called linguistic hesitant fuzzy sets (LHFSs) is defined, which can address the qualitative preferences of experts as well as reflect their hesitancy, uncertainty and inconsistency. Based on the defined operational laws of LHFSs and the order relationship, two linguistic hesitant fuzzy hybrid aggregation operators are defined: the generalized linguistic hesitant fuzzy hybrid weighted averaging (GLHFHWA) operator and the generalized linguistic hesitant fuzzy hybrid geometric mean (GLHFHGM) operator. Furthermore, to address the situation in which the elements in a set are interdependent, the generalized linguistic hesitant fuzzy hybrid Shapley weighted averaging (GLHFHSWA) operator and the generalized linguistic hesitant fuzzy hybrid Shapley geometric mean (GLHFHSGM) operator are presented, which are extensions of the GLHFHWA and GLHFHGM operators. Models designed to obtain the optimal fuzzy measures and additive measures on an attribute set and on an ordered set are, respectively, constructed. Then, an approach to linguistic hesitant fuzzy multi-attribute decision analysis is developed. Finally, two numerical examples are provided to demonstrate the practicality and efficiency of the proposed procedure.