Optimized Tourism Destination Selection Based on InLinPreRa

2013 ◽  
Vol 774-776 ◽  
pp. 1786-1789
Author(s):  
Hsiu Fen Chen ◽  
Shu Chen Hsu ◽  
Ching Tien Shih
Keyword(s):  
Decision Making ◽  
Preference Relation ◽  
Tourism Destination ◽  
Analytic Hierarchy ◽  
Preference Relations ◽  
Importance Weights ◽  
Speed Up ◽  
Destination Selection ◽  
Preference Matrix ◽  
Selection Of

This research is regarding the optimized tourism destination selection when tourists choose tourist products or travel destinations. This study adopts the method of Incomplete Linguistic Preference Relations to simplify calculation and speed up the process of comparison and selection of alternative. This method considers only judgments, whereas the tradition analytic hierarchy approach (AHP) takes judgments in a preference matrix with n elements to establish a complete preference relation decision making matrix. According to the importance weights of evaluationcriteria, C was the optimize tourism destination.

A Soft Hierarchical Process Approach for Decision Making in A Supply Chain

2015 ◽  
Vol 4 (1and2) ◽  
Author(s):  
Rajeev Dhingra ◽  
Preetvanti Singh
Keyword(s):  
Decision Making ◽  
Process Approach ◽  
Decision Makers ◽  
Analytic Hierarchy ◽  
Conflicting Objectives ◽  
Realistic View ◽  
Hierarchy Process ◽  
Pair Wise Comparison ◽  
Supplier Selection Problem ◽  
Selection Of

Decision problems are usually complex and involve evaluation of several conflicting criteria (parameters). Multi Criteria Decision Making (MCDM) is a promising field that considers the parallel influence of all criteria and aims at helping decision makers in expressing their preferences, over a set of predefined alternatives, on the basis of criteria (parameters) that are contradictory in nature. The Analytic Hierarchy Process (AHP) is a useful and widespread MCDM tool for solving such type of problems, as it allows the incorporation of conflicting objectives and decision makers preferences in the decision making. The AHP utilizes the concept of pair wise comparison to find the order of criteria (parameters) and alternatives. The comparison in a pairwise manner becomes quite tedious and complex for problems having eight alternatives or more, thereby, limiting the application of AHP. This paper presents a soft hierarchical process approach based on soft set decision making which eliminates the least promising candidate alternatives and selects the optimum(potential) ones that results in the significant reduction in the number of pairwise comparisons necessary for the selection of the best alternative using AHP, giving the approach a more realistic view. A supplier selection problem is used to illustrate the proposed approach.

Expected consistency-based model and multiplicative DEA cross-efficiency for group decision-making with incomplete distribution linguistic preference relations

2021 ◽  
pp. 1-21
Author(s):  
Jinpei Liu ◽  
Longlong Shao ◽  
Ligang Zhou ◽  
Feifei Jin
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Optimization Model ◽  
Group Decision ◽  
Preference Relation ◽  
Preference Relations ◽  
Priority Vector ◽  
Linguistic Preference Relation ◽  
Multiplicative Preference Relations ◽  
High Consistency

Faced with complex decision problems, Distribution linguistic preference relation (DLPR) is an effective way for decision-makers (DMs) to express preference information. However, due to the complexity of the decision-making environment, DMs may not be able to provide complete linguistic distribution for all linguistic terms in DLPRs, which results in incomplete DLPRs. Therefore, in order to solve group decision-making (GDM) with incomplete DLPRs, this paper proposes expected consistency-based model and multiplicative DEA cross-efficiency. For a given incomplete DLPRs, we first propose an optimization model to obtain complete DLPR. This optimization model can evaluate the missing linguistic distribution and ensure that the obtained DLPR has a high consistency level. And then, we develop a transformation function that can transform DLPRs into multiplicative preference relations (MPRs). Furthermore, we design an improved multiplicative DEA model to obtain the priority vector of MPR for ranking all alternatives. Finally, a numerical example is provided to show the rationality and applicability of the proposed GDM method.

scholarly journals Additively Consistent Interval-Valued Intuitionistic Fuzzy Preference Relations and Their Application to Group Decision Making

Information ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 260 ◽  
Author(s):  
Hua Zhuang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Preference Relation ◽  
Weighted Averaging ◽  
Preference Relations ◽  
Intuitionistic Fuzzy ◽  
Priority Weights ◽  
Interval Valued ◽  
Fuzzy Preference

This paper aims to propose an innovative approach to group decision making (GDM) with interval-valued intuitionistic fuzzy (IVIF) preference relations (IVIFPRs). First, an IVIFPR is proposed based on the additive consistency of an interval-valued fuzzy preference relation (IVFPR). Then, two mathematical or adjusted programming models are established to extract two special consistent IVFPRs. In order to derive the priority weight of an IVIFPR, after taking the two special IVFPRs into consideration, a linear optimization model is constructed by minimizing the deviations between individual judgments and between the width degrees of the interval priority weights. For GDM with IVIFPRs, the decision makers’ weights are generated by combining the adjusted subjective weights with the objective weights. Subsequently, using an IVIF-weighted averaging operator, the collective IVIFPR is obtained and utilized to derive the IVIF priority weights. Finally, a practical example of a supplier selection is analyzed to demonstrate the application of the proposed method.

Some Conflicting Results in the Analytic Hierarchy Process

2019 ◽  
Vol 18 (02) ◽  
pp. 465-486 ◽  
Author(s):  
Ardalan Bafahm ◽  
Minghe Sun
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Decision Makers ◽  
Unexpected Result ◽  
Multi Criteria Decision Making ◽  
Analytic Hierarchy ◽  
Preference Relations ◽  
The Analytic Hierarchy Process ◽  
Hierarchy Process

The analytic hierarchy process (AHP) has been believed to be one of the most pragmatic and widely accepted methods for multi-criteria decision making. However, there have been various criticisms of this method within the last four decades. In this study, the results of AHP contradicting common expectations are examined for both the distributive and ideal modes. Specifically, conflicting priorities, conflicting decisions, and conflicting preference relations are investigated. A decision-making scenario is used throughout the paper and an illustrative example constructed from the decision-making scenario is provided to demonstrate each of the conflicting results recommended by AHP. With a parametric formulation of each unexpected result, the possibility of unexpected results of AHP is generalized irrespective of applying the distributive or ideal mode. The logic and causes of these contradictions are also analyzed. This study shows that AHP is not always reliable, and could lead the decision makers towards incorrect decisions.

Material Selection Decision-Making Method for Multi-material Lightweight Automotive Body Driven by Performance

2021 ◽  
pp. 146442072110556
Author(s):  
Dengfeng Wang ◽  
Shenhua Li
Keyword(s):  
Decision Making ◽  
Material Selection ◽  
The Body ◽  
Analytic Hierarchy ◽  
Weighting Method ◽  
Lightweight Material ◽  
Automotive Body ◽  
Selection Decision ◽  
Hierarchy Process ◽  
Selection Of

This work proposes a material selection decision-making method for multi-material lightweight body driven by performance to achieve that the right materials are used for the correct positions of the automotive body. The internal relationship between performance and mass, cross-sectional shape, wall thickness parameters, and material properties of a thin-walled structure is studied. The lightweight material indices driven by performance are then established. The lightweight material indices and material price are taken as the decision-making criteria for the material selection of automotive body components. A hybrid weighting method integrated with the analytic hierarchy process, fuzzy analytic hierarchy process, and quality function deployment is proposed. The difficulty of quantitatively evaluating the performance requirements of different components of the body is solved using the proposed weighting method combined with the numerical analytical results of the component performance under multiple operating conditions of the automotive body. Then, the weight of the decision-making criteria for material selection is calculated. Grey relational analysis is used to make multicriteria decision-making on a variety of candidate materials to select the best material for body components. After the lightweight material selection of the front longitudinal beam of the automotive body, the frontal collision safety performance of the body is effectively improved, and the mass of the front longitudinal beam is reduced by 45%. Material selection result of the front longitudinal beam indicates that the proposed material selection decision-making method can effectively achieve the fast material selection of components in different positions of the body.

Dynamic QoS-Aware Cloud Service Selection Using Best-Worst Method and Timeslot Weighted Satisfaction Scores

2020 ◽  
Author(s):  
Falak Nawaz ◽  
Naeem Khalid Janjua
Keyword(s):  
Decision Making ◽  
Service Selection ◽  
Cloud Service ◽  
Cloud Services ◽  
Satisfaction Score ◽  
Analytic Hierarchy ◽  
Hierarchy Process ◽  
Best Worst Method ◽  
Selection Of

Abstract The number of cloud services has dramatically increased over the past few years. Consequently, finding a service with the most suitable quality of service (QoS) criteria matching the user’s requirements is becoming a challenging task. Although various decision-making methods have been proposed to help users to find their required cloud services, some uncertainties such as dynamic QoS variations hamper the users from employing such methods. Additionally, the current approaches use either static or average QoS values for cloud service selection and do not consider dynamic QoS variations. In this paper, we overcome this drawback by developing a broker-based approach for cloud service selection. In this approach, we use recently monitored QoS values to find a timeslot weighted satisfaction score that represents how well a service satisfies the user’s QoS requirements. The timeslot weighted satisfaction score is then used in Best-Worst Method, which is a multi-criteria decision-making method, to rank the available cloud services. The proposed approach is validated using Amazon’s Elastic Compute Cloud (EC2) cloud services performance data. The results show that the proposed approach leads to the selection of more suitable cloud services and is also efficient in terms of performance compared to the existing analytic hierarchy process-based cloud service selection approaches.

scholarly journals A Multicriteria Decision-Making Approach Based on Fuzzy AHP with Intuitionistic 2-Tuple Linguistic Sets

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shahzad Faizi ◽  
Tabasam Rashid ◽  
Sohail Zafar
Keyword(s):  
Decision Making ◽  
Fuzzy Ahp ◽  
Preference Relation ◽  
Multicriteria Decision Making ◽  
Transformation Mechanism ◽  
Analytic Hierarchy ◽  
Multicriteria Decision ◽  
Linguistic Representation ◽  
Representation Model ◽  
Linguistic Preference Relation

In the modern literature related to linguistic decision-making, the 2-tuple linguistic representation model and its useful applications in various fields have been extensively studied and used during the last decade. Recently, some useful multicriteria decision-making (MCDM) methods have been introduced based on fuzzy analytic hierarchy process (AHP) for 2-tuple linguistic representation model. By keeping in mind the importance of this linguistic model, in this paper, we introduce a fuzzy AHP methodology for intuitionistic 2-tuple linguistic sets (I2TLSs) which is a useful extension of the 2-tuple linguistic representation model. This study is comprised of four stages. In the first stage, we define some operational laws for I2TL elements (I2TLEs) and prove some related important properties. In the second stage, intuitionistic 2-tuple linguistic preference relation (I2TLPR) and multiplicative I2TLPR are defined using I2TLSs. In the 3rd stage, a transformation mechanism is introduced which can transform an I2TLPR to a corresponding intuitionistic preference relation (IPR) and vice versa. In the fourth stage, an approach is proposed for checking the consistency of an I2TLPR and presented a method to repair the inconsistent one by using the proposed transformation mechanism. Finally, a numerical example is given and comparative analysis is carried out with the TOPSIS method to verify the validity of the proposed method.

INDIVIDUAL AND SOCIAL STRATEGIES TO DEAL WITH IGNORANCE SITUATIONS IN MULTI-PERSON DECISION MAKING

2009 ◽  
Vol 08 (02) ◽  
pp. 313-333 ◽  
Author(s):  
S. ALONSO ◽  
E. HERRERA-VIEDMA ◽  
F. CHICLANA ◽  
F. HERRERA
Keyword(s):  
Decision Making ◽  
Preference Relation ◽  
Individual Preference ◽  
Missing Information ◽  
Social Strategies ◽  
Preference Relations ◽  
Advantages And Disadvantages ◽  
Fuzzy Preference Relations ◽  
Level Of Knowledge ◽  
Individual Strategies

Multi-person decision making problems involve the preferences of some experts about a set of alternatives in order to find the best one. However, sometimes experts might not possess a precise or sufficient level of knowledge of part of the problem and as a consequence that expert might not give all the information that is required. Indeed, this may be the case when the number of alternatives is high and experts are using fuzzy preference relations to represent their preferences. In the literature, incomplete information situations have been studied, and as a result, procedures that are able to compute the missing information of a preference relation have been designed. However, these approaches usually need at least a piece of information about every alternative in the problem in order to be successful in estimating all the missing preference values. In this paper, we address situations in which an expert does not provide any information about a particular alternative, which we call situations of total ignorance. We analyze several strategies to deal with these situations. We classify these strategies into: (i) individual strategies that can be applied to each individual preference relation without taking into account any information from the rest of experts and (ii) social strategies, that is, strategies that make use of the information available from the group of experts. Both individual and social strategies use extra assumptions or knowledge, which could not be directly instantiated in the experts preference relations. We also provide an analysis of the advantages and disadvantages of each one of the strategies presented, and the situations where some of them may be more adequate to be applied than the others.

A CONSENSUS MODEL FOR GROUP DECISION-MAKING PROBLEMS WITH INTERVAL FUZZY PREFERENCE RELATIONS

2012 ◽  
Vol 11 (04) ◽  
pp. 709-725 ◽  
Author(s):  
J. M. TAPIA GARCÍA ◽  
M. J. DEL MORAL ◽  
M. A. MARTÍNEZ ◽  
E. HERRERA-VIEDMA
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Selection Process ◽  
Preference Relation ◽  
Feedback Mechanism ◽  
Consensus Model ◽  
Preference Relations ◽  
Fuzzy Preference Relations ◽  
Fuzzy Preference

Interval fuzzy preference relations can be useful to express decision makers' preferences in group decision-making problems. Usually, we apply a selection process and a consensus process to solve a group decision situation. In this paper, we present a consensus model for group decision-making problems with interval fuzzy preference relations. This model is based on two consensus criteria, a consensus measure and a proximity measure, and also on the concept of coincidence among preferences. We compute both consensus criteria in the three representation levels of a preference relation and design an automatic feedback mechanism to guide experts in the consensus reaching process. We show an application example in social work.

Sign in / Sign up

Export Citation Format

Share Document