Incomplete Linguistic Preference Relations Research Articles

Selection of goals with incomplete linguistic preference relations

In goal oriented requirements elicitation process (GOREP), the requirements of the stakeholders are refined and decomposed using AND/OR graph. Goal is a high level objective of a company which is refined and decomposed into sub-goals. After refinement and decomposition, a system may have several goals and it is not possible to implement all goals because of the time, budget, and other constraints of an organisation. It is a primarily research issue that how to select those goals that would be designed, implemented and tested during different phases of the software development process. On the basis of our review, we found that there is no support for the selection of goals in GOREP when preference relations are incomplete and the preferences of the decision-makers are represented by linguistic variables. Therefore, to address this research issue, we present a method for goal selection when linguistic preference relations are incomplete. Proposed method was applied to select the goals of the institute examination system.

Fuzzy incomplete linguistic preference relations.

The effectiveness of preference relations in modeling decision-making processes makes it one of the most common representations of information use for solving decision-making problems. This research presents the fuzzy incomplete linguistic preference relations (Fuzzy InLinPreRa) approach as evaluated by decision-makers dealing with increasing complexity and uncertain economics, as well as social and managerial problems. By using Fuzzy InLinPreRa, the consistency measurements of decision-makers' evaluations will provide more accurate and reasonable solutions, allowing decision-makers to consider the objective weights of both the criteria and experts. An empirical example of the measurement of brand personality is included herein to demonstrate the feasibility of this method.

Consistency-Driven Methodology to Manage Incomplete Linguistic Preference Relation: A Perspective Based on Personalized Individual Semantics.

In linguistic decision-making problems, there may be cases when decision makers will not be able to provide complete linguistic preference relations. However, when estimating unknown linguistic preference values in incomplete preference relations, the existing research approaches ignore the fact that words mean different things for different people, that is, decision makers have personalized individual semantics (PISs) regarding words. To manage incomplete linguistic preference relations with PISs, in this article, we propose a consistency-driven methodology both to estimate the incomplete linguistic preference values and to obtain the personalized numerical meanings of linguistic values of the different decision makers. The proposed incomplete linguistic preference estimation method combines the characteristic of the personalized representation of decision makers and guarantees the optimum consistency of incomplete linguistic preference relations in the implementation process. Numerical examples and a comparative analysis are included to justify the feasibility of the PISs-based incomplete linguistic preference estimation method.

Granular computing and optimization model-based method for large-scale group decision-making and its application

In large-scale group decision-making process, some decision makers hesitate among several linguistic terms and cannot compare some alternatives, so they often express evaluation information with incomplete hesitant fuzzy linguistic preference relations. How to obtain suitable large-scale group decision-making results from incomplete preference information is an important and interesting issue to concern about. After analyzing the existing researches, we find that: i) the premise that complete preference relation is perfectly consistent is too strict, ii) deleting all incomplete linguistic preference relations that cannot be fully completed will lose valid assessment information, iii) semantics given by decision makers are greatly possible to be changed during the consistency improving process. In order to solve these issues, this work proposes a novel method based on Granular computing and optimization model for large-scale group decision-making, considering the original consistency of incomplete hesitant fuzzy linguistic preference relation and improving its consistency without changing semantics during the completion process. An illustrative example and simulation experiments demonstrate the rationality and advantages of the proposed method: i) semantics are not changed during the consistency improving process, ii) completion process does not significantly alter the inherent quality of information, iii) complete preference relations are globally consistent, iv) final large-scale group decision-making result is acquired by fusing complete preference relations with different weights.

Software Requirements Selection with Incomplete Linguistic Preference Relations

Software requirements (SRs) selection is a multicriteria group decision making (MCGDM) problem whose objective is to select the SRs from the pool of the requirements on the basis of different criteria. In MCGDM, different decision makers have different opinions of the same requirement so it is difficult to decide which set of SRs to implement during the different releases of the software. During the MCGDM process, decision makers may use linguistic variables to specify preferences of requirements over other requirements. In real life applications, it has been observed that sometimes decision makers cannot evaluate the SRs due to their lack of knowledge and limited expertise related to the problem domain. In this situation, incomplete linguistic preference relations (LPRs) are constructed. In literature, SRs selection with incomplete LPRs is still an unresearched problem. Therefore, to address this issue, a method is presented for the selection of SRs with incomplete LPRs. Finally, the applicability of the proposed method is explained with the help of an example.

Reaching a minimum adjustment consensus in social network group decision-making

In this paper, we propose a minimum adjustment consensus framework for the social network group decision-making (SN-GDM) with incomplete linguistic preference relations (ILPRs). The extant studies ignore the influence of network structure on the decision-makers’ (DMs’) weights, and set a fixed parameter to adjust DM’s preferences that may lead to the inefficiency of reaching a consensus. To solve these issues, we first propose a weight allocation method with the structural hole theory by analyzing the tie strength and topology structure of DM’s social networks. After obtaining DMs’ weights, the consistency/consensus indexes at three levels are constructed and used to identify the inconsistent DMs. Then, a novel minimum adjustment consensus model (MACM) for ILPRs is proposed to obtain the optimal adjustment parameters, which are used to recommend customized adjustments in the feedback mechanism. The existence of optimal solutions and the convergence of the proposed consensus models under certain conditions are also proved. Finally, the validity of the proposed method is verified by an application example. Different from the extant MACMs, we optimized the adjustment parameters just for inconsistent DMs instead of all DMs’ adjusted preference values. With less number of consensus rounds and lower costs, we also improved the classical feedback mechanism and established its connection with the current MACMs.

Selection of Goals with Incomplete Linguistic Preference Relations

Selection of Goals with Incomplete Linguistic Preference Relations

An Interactive Consensus Model for Group Decision-Making with Incomplete Multi-Granular 2-Tuple Fuzzy Linguistic Preference Relations

As a result of the heterogeneity of decision-makers, it is more suitable to use multi-granular linguistic information to express assessing information over alternatives for decision-makers. Decision-makers may only provide partial preference information as the limitation of knowledge over the alternatives. Thus, the incomplete multi-granular 2-tuple fuzzy linguistic preference relations (IMGFLPRs) can be applied to manage the GDM problems in complex environments. In this paper, we propose a novel group decision-making (GDM) model based on mathematical programming with IMGFLPRs. It is more important that the proposed mathematical programming can directly deal with IMGFLPRs and does not need to be converted into a uniform form. By this means, we construct a consensus reaching process based on IMGFLPRs, considering simultaneously dynamic adjustment of experts’ weight. Finally, an emergency plan selection problem is solved by the proposed model, which shows the result of GDM is effectiveness.

Addressing consistency and consensus with group decision support model based on incomplete hesitant fuzzy linguistic preference relations

Addressing consistency and consensus with group decision support model based on incomplete hesitant fuzzy linguistic preference relations

Deriving heterogeneous experts weights from incomplete linguistic preference relations based on uninorm consistency

In this study, we provide an expertise-based weight allocation approach for heterogeneous experts with incomplete linguistic preference relations (ILPRs) in heterogeneous group decision-making (HGDM). Based on the uninorm consistency (U-consistency) theory, this paper proposes a new four-way iteration step to estimate the missing preference values so as to preserve the original information as much as possible. After obtaining the complete linguistic preference relations that satisfies reciprocity and boundedness, the discrimination indicator is introduced to measure the expertise level of heterogeneous experts, and its definition and calculation method in linguistic context is provided. In contrast to complete preference information, when assigning weights for heterogeneous experts with ILPRs, the contradictory relationship between their inconsistency and incompleteness, which had been proved by numerical simulation, exists and needs solving. Then, using defined discrimination, inconsistency and incompleteness, we propose a New-Index and a weight allocation method, the validity of which had been illustrated by the numerical example, to measure objectively heterogeneous experts’ expertise. Considering these three indicators, the proposed weight is more reasonable than existent weight allocation methods with single indicator.

The Research of Economic Value of Innovative Business—Turn Health Care Food into Service

Due to people’s attention to health concept, changing of diet habit, awareness of alternative medicine and active development of life science research, people’s care of food not only how the product taste but also how nutritious it is. And this flourishes the industry of health food. The growth of health food market results of the early coming of the ageing society, increasing medical expenses, the raise of education level and the expand of average ages.Regarding health care of mainstream, consumers prefer selecting products which marked health food while they couldn’t ensure that the product is really healthy. Thus, if we take pursuing health as our guideline and provide a series of health care products and service, there will be a lot to dig in.Innovative business method is the most valuable type nowadays. The goal of this type of innovation is to changing the way of consuming and increase the earnings of producers. Investigated in the side of how different distributors provide their service to customers and find out which consuming method is the favorite way of customers. What’s more, we could figure out the Optimization Strategy Alliance Advantage Ratio when a health care food store trying to extend its branches.The study takes the expansion of health care food store and development of partnership as object and the purpose is to find out the advantage of Taiwan health care product industry partnership with the qualitative and quantitative prediction methods. First of all, the study gathers information about factors that impact the success of expanding branches from literature and interviews. And then do questionnaire according to experts and scholars’ acceptance with the method of Incomplete Linguistic Preference Relations, which helps to build the weights of the success of expanding branches. Using SAW and ADW, we would get a relative priority to each decision maker and evaluate the performance of all the partnership. Finally, we would obtain the optimal success rate of the health food industry partnership. And the data would become analysis basic for the team who organizes and participates partnership.

Completing 2-tuple linguistic preference relations based on upper bound condition

The purpose of this paper is to propose a method to estimate missing preference values when handling incomplete linguistic preference relations assessed by using 2-tuple linguistic preference values. We assert that additive transitivity alone may result in estimated values that do not qualify as 2-tuple linguistic preference values. Therefore, an upper bound condition based on additive transitivity is proposed to deal with incomplete 2-tuple linguistic preference relations (2TLPRs). We prove that if experts with incomplete information abide by this property, then the missing preference values can be estimated such that they are expressible. Additionally, because of this property, the resultant complete 2TLPR is proved to be consistent. Based on additive transitivity and the upper bound condition, an interactive completing algorithm is developed to estimate the missing entries in incomplete 2TLPR. When the upper bound condition is not satisfied or there exist entries that do not coincide with the real opinions of the experts, the proposed algorithm can automatically adjust the known entries by interacting with the experts. By using the proposed algorithm, the completed 2TLPR is not only additively consistent, but also can accurately reflect the actual preference of the experts.

Group prioritisation with unknown expert weights in incomplete linguistic context

ABSTRACTIn this paper, we study a group prioritisation problem in situations when the expert weights are completely unknown and their judgement preferences are linguistic and incomplete. Starting from the theory of relative entropy (RE) and multiplicative consistency, an optimisation model is provided for deriving an individual priority vector without estimating the missing value(s) of an incomplete linguistic preference relation. In order to address the unknown expert weights in the group aggregating process, we define two new kinds of expert weight indicators based on RE: proximity entropy weight and similarity entropy weight. Furthermore, a dynamic-adjusting algorithm (DAA) is proposed to obtain an objective expert weight vector and capture the dynamic properties involved in it. Unlike the extant literature of group prioritisation, the proposed RE approach does not require pre-allocation of expert weights and can solve incomplete preference relations. An interesting finding is that once all the experts express their preference relations, the final expert weight vector derived from the DAA is fixed irrespective of the initial settings of expert weights. Finally, an application example is conducted to validate the effectiveness and robustness of the RE approach.

A method considering and adjusting individual consistency and group consensus for group decision making with incomplete linguistic preference relations

For group decision making with incomplete linguistic preference relations, this paper first make an analysis about individual consistency, based on which a goal programming model is established to estimate the missing values for incomplete linguistic preference relations. Different to the existing methods, the proposed model can cope with the problem in ignorance situations. Then, a multi-stage algorithm which takes both the individual consistency and group consensus into consideration is presented, including the following main stages: initial check for the incomplete preference relations, completion, check and adjustment of individual consistency, aggregation, check and adjustment of group consensus and final selection. In the aggregation stage of the algorithm, an experience and quality hybrid weight vector (EQWV) is defined to obtain the collective linguistic preference relation in group decision making. To prove the feasibility and efficiency of the proposed method, some examples and the corresponding comparisons with other methods are provided. Besides, the analysis on the threshold values proposed by the multi- stage algorithm is made, which can help the decision maker determine the threshold values according to his/her expectations. Finally, a further discussion about comparison analysis, extensible application and some details of the proposed method is presented.

Interactive algorithms for improving incomplete linguistic preference relations based on consistency measures

Incomplete linguistic preference relations (InLPRs) are generally inevitable in group decision making problems due to several reasons. Two vital issues of InLPRs are the consistency and the estimation of missing entries. The initial InLPR may be not consistent, which means that some of its entries do not reflect the real opinions of the experts accurately. Thus, there are deviations between some initial provided values and real opinions. Therefore, it is valuable to elicit the providers to realize and repair the deviations. In this paper, we discuss the consistency and the completing algorithms of InLPRs by interacting with the experts. Servicing as the minimum condition of consistency, the weak consistency of InLPRs is defined and a weak consistency reaching algorithm is designed to guarantee the logical correctness of InLPRs. Then two distinct completing algorithms are presented to estimate the missing entries. The former not only estimates all possible linguistic terms and represents them by the extended hesitant fuzzy linguistic terms sets but also keeps weak consistency during the computing procedures. The later can automatically revise the existing entries using the new opinions supplemented by the experts during interactions. All the proposed algorithms interact with the experts to elicit and mine their actual opinions more accurately. A real case study is also presented to clarify the advantages of our proposal. Moreover, these algorithms can serve as assistant tools for the experts to present their preferences.

Composite Material Selection Using Multi Criteria Decision Making for Automobile Torsion Bar

Automotive components made from composite materials can result in significant weight savings over steel and Aluminum. The main purpose of this research is to study about the selection of suitable composite material for automobile torsion bar which possesses good strength to weight ratio and yield considerable weight savings. This paper involves identification of potential composite materials, selection of evaluation criteria, use of fuzzy theory to quantify criteria values under uncertainty and application of fuzzy Linguistics to evaluate and select the best material for replacing conventional steel material with composite material used in automobile torsion bar. The strength of the proposed paper is the ability to deal with uncertainty arising due to the lack of real data in material selection for replacing the conventional material. Keywords:- Composite material, Incomplete linguistic preference relations, AHP, Decision analysis, Consistent fuzzy preference relations, Multi-criteria decision making

A Structural Model of Leisure Activity of Females Based on InLinPreRa

This research is regarding the optimized leisure activity selection when women choose. Apply the method of Incomplete Linguistic Preference Relations (InLinPreRa) to process of comparison and selection of the leisure activity. The most popular leisure which women always participate are listed in order: (1) exercise related (2) handicaft related (3) entertain related (4) knowledge related (5) social related. The most welcomed leisure activity is mountain climbing.

Predicting the Success of Design Assistive Input Devices for Disabled People

It is important issue to select and design a suitable Assistive Input Devices (AID) for disabled people. This study applies the Incomplete Linguistic Preference Relations (InLinPreRa) analytical framework to predicting the success of design Assistive Input Devices (AID) for disabled people implementation. The results demonstrate that the five most important influential factors are (C6) User-friendly interface (0.166) (C4) Budget (0.163), (C5) Stability of Device (0.159), (C3) easy to maintain (0.144), (C1) easy to operate (0.126). The prediction success rate for AID for disabled people system implementation is 62.7%.

Some methods to deal with unacceptable incomplete 2-tuple fuzzy linguistic preference relations in group decision making

In this paper, we first propose a four-way procedure to estimate missing preference values when dealing with acceptable incomplete 2-tuple fuzzy linguistic preference relations (FLPRs). The proposed revised procedure can estimate more missing elements in the first iteration and also has more advantages than the existing methods. Then, we address situations in which an expert do not provide any comparison information for a particular alternative with other alternatives, and thus provides an unacceptable incomplete 2-tuple FLPR. We propose some methods to deal with the unacceptable situations. The main idea of these methods is to set some seed values for the unknown alternative and then to complete the unacceptable incomplete 2-tuple FLPRs by the revised procedure. We also give a simple algorithm to select the best alternative for the group decision making (GDM) problem with incomplete 2-tuple FLPRs. The analysis of advantages and drawbacks for each method are also presented.

Measuring the Satisfaction of Implementing Assistive Input Devices for Disabled People with InLinPreRa

This study applies the Incomplete Linguistic Preference Relations (InLinPreRa) analytical framework to measuring the satisfaction of Assistive Input Devices (AID) for disabled people implementation as well as to verify the feasibility of this proposed approach. The main objective of this study is helping the organizations become aware of the essential factors affecting the AIDs for disabled people, and identifying the actions necessary before implementing AIDs for disabled people. Then predicted success/failure values are obtained to enable organizations to decide whether to initiate AIDs for disabled people, inhibit adoption or take remedial actions to increase the possibility of a satisfaction AIDs for disabled people.