Consistent Fuzzy Preference Relations Research Articles

An analytic framework for social life cycle impact assessment—part 1: methodology

This study aims to develop a new framework of social life cycle impact assessment (SLCIA) method based on the United Nations Environment Program/Society of Environmental Toxicology and Chemistry (UNEP/SETAC) Guidelines for analyzing the social impact in Taiwan, particularly in the electronics industry. After reviewing the literature on social life cycle assessment (SLCA), we analyzed existing case studies and developed SLCIA methods based on the UNEP/SETAC Guidelines. We thereafter identified stakeholders, subcategories, and indicators in accordance with the current status of SLCA case studies and opinions from ten experts in the Taiwanese electronics industry. Both quantitative and semi-quantitative indicators were subsequently proposed to assess the social impact of workers in the Taiwanese electronics sector. Each indicator was given the score of 1 to 5 by classifying the social impact percentage of nine scales. To formulate an analytic framework for SLCIA, the weighting values of each subcategory and indicator were determined using the consistent fuzzy preference relations (CFPR) method. Seven subcategories and 19 qualitative and quantitative indicators of worker stakeholders for the electronics sector were identified based on the UNEP/SETAC Guidelines. A score of 1 to 5 is assigned to each quantitative indicator by classifying the social impact percentage of nine scales. The data obtained from companies for each quantitative indicator were subsequently transformed into social impact percentage in terms of the statistical data on social situations at the country or industry level. With regard to semi-quantitative indicators, three implementation levels of management efforts on social performance within five elements were identified. The CFPR method was then employed to determine the weights of each indicator by ten experts. Results indicated that preventing forced work practices, protecting children from having to work, and providing minimum and fair wages for workers are the three most important indicators for assessing social impact. A new SLCIA method that incorporates both quantitative and semi-quantitative indicators was proposed for assessing social impact in the electronics sector in Taiwan. Nine quantitative indicators can be easily organized using available social data from government statistics as performance reference points (PRPs) to determine the social impact exerted by companies. The relative weights were determined to allow for an impact assessment and thus solve the limitation of their currently assumed equal weights. The proposed framework is examined to analyze the social impact of three production sites for semiconductor packaging and manufacturing in Taiwan.

A goal programming model of obtaining the priority weights from an interval preference relation

The main aim of this paper is to investigate the weight vectors of interval fuzzy preference relations and interval multiplicative preference relations. In view of the excellent properties of multiplicative transitivity in modeling the consistency of fuzzy preference relations, based on the multiplicative consistency property, a goal programming model of obtaining the priority weights from an interval fuzzy preference relation is introduced along with some of its desired properties. The priority vector of a fuzzy preference relation can also be derived using this goal programming model, where the optimal value of the objective function is always equal to zero. Numerical examples are provided to illustrate the validity of the proposed model.

Knowledge-based strategy selection: a hybrid model and its implementation

Purpose– The purpose of this paper is to develop a novel hybrid multi-criteria decision-making (MCDM) model to help organizations select their knowledge-based strategy effectively. Knowledge management (KM) initiatives are often started with the selection of a strategy, which is a critical decision for a successful KM implementation.Design/methodology/approach– KM initiatives are often started with the selection of a strategy, which is a critical decision for a successful KM implementation. Thus, the aim of this paper is to develop a novel hybrid MCDM model to help organizations select their knowledge-based strategy effectively.Findings– Results illustrate that the proposed model is efficient to consider the complex interactions among criteria and provides a consistent decision with less pair-wise comparisons. Furthermore, a case study indicates that a “codification versus tacitness” strategy is preferred over other strategies considering nine main domain criteria.Originality/value– The contribution of this paper is threefold: it addresses the gaps in KM literature on the effective and efficient assessment of KM strategy selection; it provides a comprehensive and systematic framework that combines analytic network process (ANP) and consistent fuzzy preference relations (CFPR) to assess KM implementation strategy; and it illustrates a real-world study to exhibit the applicability of the proposed approach and the efficacy of the framework.

Consistency-based linear programming models for generating the priority vector from interval fuzzy preference relations

Interval fuzzy preference relations that can well cope with the vagueness and uncertainty are commonly used by the decision maker. The most crucial issue is how to derive the interval priority vector from an interval fuzzy preference relation. This paper first analyzes the size of the interval priority weights. Then, two linear programming models are built, by which the interval priority weights are obtained, respectively. Considering the inconsistent case, two consistency-based linear programming models are built to derive the additive consistent fuzzy preference relations. Different to the current methods, new models consider the consistency and the interval priority weight simultaneously. In some situations, the decision maker may only offer an incomplete interval fuzzy preference relation, namely, some judgments are missing. To cope with this situation, we first classify the missing intervals into three categories and then apply the associated linear equations to denote the missing values. After that, we construct two consistency-based linear programming models to determine the missing values to cope with the consistent and inconsistent cases. It is worth noting that the built models can cope with the situation where ignorance objects exist. Meanwhile, the associated numerical examples are offered, and the analysis comparison is made.

Improvement of the Consistent Fuzzy Preference Relation Method and Comparison with the AHP Method

AHP is always a popular method adopted by researchers in dealing with the issues of multiple criteria decision making. However, with the increase in the criteria, the traditional AHP may cause a problem in terms of the consistency of the questionnaires. Later on, the method of Consistent Fuzzy Preference Relation was developed to resolve such issue. But currently the utilization rate of this method is low, -probably because most researchers still doubt the result of the CFPR method. The main purpose of this study is using experiment and statistics analysis to compare the two methods. The final report shows that the results obtained by the CFFR method and the traditional AHP method are similar. It indicates CFPR is a good method for dealing with the MCDM problem. Moreover, this study also proposes a suggestion to improve the CFPR, so as to resolve the issue of screening out the valid questionnaires. Finally, this study used the CFPR method to code the software for use of follow-up researchers.

The Taiwanese Businessmen Assess the Investment Environment of China - Construction and Application of PR AHP Program

The economy of China has been rapidly growing in recent years. One of the important reasons is that there are a large number of foreign capital investments made in China. Creating a good investment environment in order to continuously attract foreign capitals is therefore an important topic for discussion. Taiwanese businessmen play an important role in the investments made in China, so this study investigates which aspects of China's investment environment concerns Taiwanese businessmen more. Whether in the engineering or management field, AHP is a popular method on multi-criteria decision-making issues. However, with AHP, one must consider the problem of inconsistency. Later on, the Consistent Fuzzy Preference Relation method was developed. However, since this method does not have the easy-to-use software, this study is to construct and introduce the software using this method. From the results, the following items top the list: number of specialized manpower, quality and quantity of skilled manpower, electronic capabilities, electronic service, administrative efficiency, efficiency of finance and steady financial policies.

Group Decision Making Using Fuzzy Linguistic Preference Relations under Uncertain Environment

When facing problems, evaluators use fuzzy preference relations to express their opinions toward attributes or alternatives. Studies on fuzzy preference relations have been gained much attention recently, but the values of constructing fuzzy preference relations are crisp. This paper thus proposes the evaluators the consistent fuzzy linguistic preference relations (Fuzzy LinPreRa) parameterized with fuzzy linguistic variables rather than crisp values to compare the alternatives/attributes. Furthermore, an approach for group decision making based on the Fuzzy LinPreRa method is presented. The proposed method enables evaluators to deal with some uncertainty or vagueness and ensures a more convincing and effective decision making. A practical example is demonstrated to illustrate this method.

A new method for group decision making with incomplete fuzzy preference relations

This paper presents a new method to coping with group decision making with incomplete fuzzy preference information. To do this, it first defines an additively consistent index of fuzzy preference relations, and then gives a method to calculating the priority vector for additively consistent fuzzy preference relations. When the individual fuzzy preference relation is incomplete, a goal programming model is constructed, by which the missing values can be obtained. Then, an iterative approach to obtain the acceptably additive consistency of fuzzy preference relations is introduced. After that, an induced hybrid weighted aggregation (IHWA) operator is presented to aggregate the collective fuzzy preference relation. The main features of this aggregation operator are that the group consistency is no smaller than the highest individual inconsistency, and the group consensus is no smaller than the smallest consensus between the individual fuzzy preference relations. As a series of development, an algorithm based on the acceptable consistency and the group consensus is developed. Finally, three examples are given to show the efficiency and feasibility of the developed procedure, and comparisons are also offered.

D-CFPR: D numbers extended consistent fuzzy preference relations

How to express an expert’s or decision maker’s preference for alternatives is an open issue. Consistent fuzzy preference relation (CFPR) is with big advantages to handle this problem due to it can be construed via a smaller number of pairwise comparisons and satisfies transitivity property. However, the CFPR is incapable of dealing with the cases involving uncertain and incomplete information. In this paper, a D numbers extended consistent fuzzy preference relation (D-CFPR) is proposed to overcome the weakness. The D-CFPR extends the classical CFPR by using a new model of expressing uncertain information called D numbers. The D-CFPR inherits the merits of classical CFPR and can totally reduce to the classical CFPR. This study can be integrated into our previous study about D-AHP (D numbers extended AHP) model to provide a systematic solution for multi-criteria decision making (MCDM).

Application of CFPR to fashion design scheme selection

Purpose – The purpose of this paper is to improve the complicated pairwise comparison problem and the consistency of analytic hierarchy process (AHP), the consistent fuzzy preference relation (CFPR) method is applied to select the best fashion design scheme in this study. Design/methodology/approach – The first stage is to review the traditional AHP for fashion design scheme evaluation. The second stage is based on the procedure of CFPR to select the best fashion design scheme. The procedure includes: establishment of the fuzzy preference relation matrix of pairwise comparisons, calculation of criteria weight, and calculation of the evaluation value for ratings of fashion design schemes with respect to criteria. The final stage is to compare the procedures and results between AHP and CFPR for the evaluations of fashion design schemes. Findings – For fashion design scheme selection, there are 76 data for fashion design scheme evaluation in AHP, but only 31 in CFPR. Comparing both AHP and CFPR methods, the rankings of the criteria weight are identical and the priority scores of the fashion design scheme are similar. The CFPR is more efficient than AHP, obviously, to solve the inconsistency of hierarchical decision-making problem. Originality/value – This paper proposes the CFPR method for the selection of the best fashion design scheme. According to the finding of this study, the CFPR is proposed as a practicable option for solving complicated pairwise comparison problems in fashion design decision.

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

Incomplete preference relations

In decision making, consistency in fuzzy preference relations is associated with the study of transitivity property. While using additive consistency property to complete incomplete preference rela...

Using the Fuzzy Preference Relations to Measure the Aggregative Risk Degree of Implementing E-Manufacturing

Today’s competition in manufacturing industry depends not just on lean manufacturing but also on the ability to provide customers with total solutions and life-cycle costs for sustainable value. This study therefore proposes an analytic hierarchy model to help the administrators understand the critical risk factors influence the E-manufacturing system initiation, and an aggregative risk degree is indicated which risk grade they are in. The importance weights of risk factors and possible occurrence ratings of four risk grade (high-risk, medium-risk, low-risk and none-risk) are determined by using consistent fuzzy preference relations. Keywords: E-manufacturing, risk management, fuzzy preference relations.

On Improving the Additive Consistency of the Fuzzy Preference Relations Based on Comparative Linguistic Expressions

Linguistic fuzzy preference relations are commonly used in decision-making problems. The recent proposal of comparative linguistic expressions has been introduced to deal with the situation that decision makers hesitate among several linguistic terms to express their assessments. To apply this idea to linguistic fuzzy preference relations, we propose the linguistic fuzzy preference relations based on the comparative linguistic expressions. We then transform the linguistic fuzzy preference relations into linguistic 2-tuple fuzzy preference relations, and introduce an iterative method to measure and improve the additive consistency of the linguistic 2-tuple fuzzy preference relations. An illustrative example is finally presented to clarify the computational processes.

INCOMPLETE INTERVAL FUZZY PREFERENCE RELATIONS FOR SUPPLIER SELECTION IN SUPPLY CHAIN MANAGEMENT

In the analytical hierarchy process (AHP), it needs the decision maker to establish a pairwise comparison matrix requires n(n–1)/2 judgments for a level with n criteria (or alternatives). In some instances, the decision maker may have to deal with the problems in which only partial information and uncertain preference relation is available. Consequently, the decision maker may provide interval fuzzy preference relation with incomplete information. In this paper, we focus our attention on the investigation of incomplete interval fuzzy preference relation. We first extend a characterization to the interval fuzzy preference relation which is based on the additive transitivity property. Using the characterization, we propose a method to construct interval additive consistent fuzzy preference relations from a set of n–1 preference data. The study reveals that the proposed method can not only alleviate the comparisons, but also ensure interval preference relations with the additive consistent property. We also develop a novel procedure to deal with the analytic hierarchy problem for group decision making with incomplete interval fuzzy preference relations. Finally, a numerical example is illustrated and a supplier selection case in supply chain management is investigated using the proposed method.

Incomplete preference relations: An upper bound condition

In decision making, consistency in fuzzy preference relations is associated with the study of transitivity property. While using additive consistency property to complete incomplete preference relations, the preference values found may lie outside the interval [0, 1] or the resultant relation may itself be inconsistent. This paper proposes a method that avoids inconsistency and completes an incomplete preference relation using an upper bound condition. Additionally, the paper extends the upper bound condition for multiplicative reciprocal preference relations. The proposed methods ensure that if n-1 preference values are provided by an expert, such that they satisfy the upper bound condition, then the preference relation is completed such that the estimated values lie inside the unit interval [0, 1] in the case of preference relations and [1/9, 9] in the case of multiplicative preference relation. Moreover, the resultant preference relation obtained using the proposed method is transitive.

Development of Management Support System for Property Management Enterprises

With the information technology developing, it makes the enterprises to use long-distance operation, video conference, intelligence management, electronic secretary, buildings automation, residents’ communication platform and customer service system and so on. Application of these systems will also be the electronic trend for property management enterprises (PMEs). Owing to the competition in real estate market and large-scale buildings construction, the services and managements of the property are crucial to the future survival in this industry. Therefore, PMEs need to adopt advanced information technology such as facility management systems to promote their services quality, improve operation efficiency, reduce personnel cost and create professional image for achieving high competitive ability. This study is devoted to the service items for the development of a management support system (MSS). Based on the service items of property management identified through the literature review, a hierarchical structure of three dimensions and twenty sub-items is constructed, and a systematic approach with consistent fuzzy preference relations (CFPR) was employed to assess the relative importance and ranking of these items. Discussion of the results is made and a brief conclusion is proposed.

Comparison analysis for subjective and objective weights of financial positions of container shipping companies

This paper explores the positions of financial ratios identified utilizing both subjective and objective weighting methods—the consistent fuzzy preference relation (CFPR) and entropy—based on questionnaires collected from experts and the financial statements of major shipping companies in Taiwan and Korea. The comparison of perceptions of financial ratios by country and by expert group illuminates some financial aspects that experts may have ignored or not paid close attention to during the business operating process, especially in today’s turbulent world shipping market. The major results of this paper show that experts in the two countries have different perceptions of the debt-to-equity ratio and that by combining the results of objective and subjective weights, categorization of financial ratios by quadrant can help decision-makers find business options and strategies to improve the financial performance of their companies. This research approach may be applied to other business entities, improving on the direct rating method used in previous studies.

Selection of the optimal configuration for a flexible surface mount assembly system based on the interrelationships among the flexibility elements

Flexibility has been widely recognized as a key competitive factor for manufacturing firms coping with the increasingly turbulent manufacturing environment today. Establishing a flexible surface mount assembly (SMA) system is critical for organizations to achieve a cost-effective assembly and gain competitive advantages in the printed circuit board (PCB) assembly industry. To find a practicable operating strategy, a multi-criteria decision-making framework that combines the analytic network process (ANP) with the consistent fuzzy preference relations (CFPR) is proposed to explore the interrelationships among the six dimensions and twenty-one criteria of manufacturing flexibility and resolve the uncertainty and divergence between decision-makers with the least amount of pairwise comparisons. Based on the results of prior analysis, an optimum configuration of a flexible SMA system is suggested for the capacity investment intended by a Taiwan electronics manufacturing service (EMS) provider to quickly respond to dynamic production requirements.

Consistency test and weight generation for additive interval fuzzy preference relations

Some simple yet pragmatic methods of consistency test are developed to check whether an interval fuzzy preference relation is consistent. Based on the definition of additive consistent fuzzy preference relations proposed by Tanino (Fuzzy Sets Syst 12:117---131, 1984), a study is carried out to examine the correspondence between the element and weight vector of a fuzzy preference relation. Then, a revised approach is proposed to obtain priority weights from a fuzzy preference relation. A revised definition is put forward for additive consistent interval fuzzy preference relations. Subsequently, linear programming models are established to generate interval priority weights for additive interval fuzzy preference relations. A practical procedure is proposed to solve group decision problems with additive interval fuzzy preference relations. Theoretic analysis and numerical examples demonstrate that the proposed methods are more accurate than those in Xu and Chen (Eur J Oper Res 184:266---280, 2008b).