Kwai-Sang Chin

Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean

Failure mode and effects analysis (FMEA) has been extensively used for examining potential failures in products, processes, designs and services. An important issue of FMEA is the determination of risk priorities of the failure modes that have been identified. The traditional FMEA determines the risk priorities of failure modes using the so-called risk priority numbers (RPNs), which require the risk factors like the occurrence (O), severity (S) and detection (D) of each failure mode to be precisely evaluated. This may not be realistic in real applications. In this paper we treat the risk factors O, S and D as fuzzy variables and evaluate them using fuzzy linguistic terms and fuzzy ratings. As a result, fuzzy risk priority numbers (FRPNs) are proposed for prioritization of failure modes. The FRPNs are defined as fuzzy weighted geometric means of the fuzzy ratings for O, S and D, and can be computed using alpha-level sets and linear programming models. For ranking purpose, the FRPNs are defuzzified using centroid defuzzification method, in which a new centroid defuzzification formula based on alpha-level sets is derived. A numerical example is provided to illustrate the potential applications of the proposed fuzzy FMEA and the detailed computational process of the FRPNs.

The Evidential Reasoning Approach for MADA Under both Probabilistic and Fuzzy Uncertainties

Abstract Many multiple attribute decision analysis (MADA) problems are characterised by both quantitative and qualitative attributes with various types of uncertainties. Incompleteness (or ignorance) and vagueness (or fuzziness) are among the most common uncertainties in decision analysis. The evidential reasoning (ER) approach has been developed in the 1990s and in the recent years to support the solution of MADA problems with ignorance, a kind of probabilistic uncertainty. In this paper, the ER approach is further developed to deal with MADA problems with both probabilistic and fuzzy uncertainties. In this newly developed ER approach, precise data, ignorance and fuzziness are all modelled under the unified framework of a distributed fuzzy belief structure, leading to a fuzzy belief decision matrix. A utility-based grade match method is proposed to transform both numerical data and qualitative (fuzzy) assessment information of various formats into the fuzzy belief structure. A new fuzzy ER algorithm is developed to aggregate multiple attributes using the information contained in the fuzzy belief matrix, resulting in an aggregated fuzzy distributed assessment for each alternative. Different from the existing ER algorithm that is of a recursive nature, the new fuzzy ER algorithm provides an analytical means for combining all attributes without iteration, thus providing scope and flexibility for sensitivity analysis and optimisation. A numerical example is provided to illustrate the detailed implementation process of the new ER approach and its validity and wide applicability.

A NEW METHOD FOR MEASURING UNCERTAINTY AND FUZZINESS IN ROUGH SET THEORY

Based on the complement behavior of information gain, a new definition of information entropy is proposed along with its justification in rough set theory. Some properties of this definition imply those of Shannons entropy. Based on the new information entropy, conditional entropy and mutual information are then introduced and applied to knowledge bases. The new information entropy is proved to also be a fuzzy entropy.

On the centroids of fuzzy numbers

In a paper by Cheng [A new approach for ranking fuzzy numbers by distance method, Fuzzy Sets and Systems 95 (1998) 307-317], a centroid-based distance method was suggested for ranking fuzzy numbers, both normal and non-normal, where the fuzzy numbers are compared and ranked in terms of their Euclidean distances from their centroid points to the origin. It is found that the centroid formulae provided by the above paper are incorrect and have led to some misapplications. In this paper we present the correct centroid formulae for fuzzy numbers and justify them from the viewpoint of analytical geometry. A numerical example demonstrates that Chengs formulae can significantly alter the result of the ranking procedure.

The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees

Abstract Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modeled using the evidential reasoning (ER) approach. Several types of uncertainties such as ignorance and fuzziness can be modeled in the ER framework. In this paper, the ER approach will be extended to model new types of uncertainties including interval belief degrees and interval data that could be incurred in decision situations such as group decision making. The Dempster–Shafer (D–S) theory of evidence is first extended, which is one of the bases of the ER approach. The analytical ER algorithm is used to combine all evidence simultaneously. Two pairs of nonlinear optimization models are constructed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities of each alternative, respectively. Interval data are equivalently transformed to interval belief degrees and are incorporated into the nonlinear optimization models. A cargo ship selection problem is examined to show the implementation process of the proposed approach.

Identifying and prioritizing critical success factors for coopetition strategy

Purpose – Coopetition is a revolutionary mindset that combines competition and cooperation. This paper aims to determine and to examine success factors critical to coopetition strategy management and to explore the identified factors in Hong Kong manufacturing.Design/methodology/approach – Based on a literature review and expert interviews following the analytic hierarchy process, this paper identifies and prioritizes seven critical success factors and 17 critical success sub‐factors comprising three success factor categories: management commitment, relationship development, and communication management.Findings – The results show that management leadership and development of trust are the most important success factors. Based on the factors identified, the authors propose a hierarchical model for coopetition strategy management, which has been validated in Hong Kong industry to facilitate the formulation of action plans for better coopetition management.Practical implications – The prioritization of crit...

A neutral DEA model for cross-efficiency evaluation and its extension

Cross-efficiency evaluation has long been suggested as an alternative method for ranking decision making units (DMUs) in data envelopment analysis (DEA). This paper proposes a neutral DEA model for cross-efficiency evaluation. Unlike the aggressive and benevolent formulations in cross-efficiency evaluation, the neutral DEA model determines one set of input and output weights for each DMU from its own point of view without being aggressive or benevolent to the other DMUs. As a result, the cross-efficiencies computed in this way are more neutral, neither aggressive nor benevolent. The neutral DEA model is then extended to a cross-weight evaluation, which seeks a common set of weights for all the DMUs. Numerical examples are provided to illustrate the applications of the neutral DEA model and the cross-weight evaluation in DEA ranking.

Fuzzy analytic hierarchy process

Fuzzy analytic hierarchy process (AHP) proves to be a very useful methodology for multiple criteria decision-making in fuzzy environments, which has found substantial applications in recent years. The vast majority of the applications use a crisp point estimate method such as the extent analysis or the fuzzy preference programming (FPP) based nonlinear method for fuzzy AHP priority derivation. The extent analysis has been revealed to be invalid and the weights derived by this method do not represent the relative importance of decision criteria or alternatives. The FPP-based nonlinear priority method also turns out to be subject to significant drawbacks, one of which is that it may produce multiple, even conflict priority vectors for a fuzzy pairwise comparison matrix, leading to entirely different conclusions. To address these drawbacks and provide a valid yet practical priority method for fuzzy AHP, this paper proposes a logarithmic fuzzy preference programming (LFPP) based methodology for fuzzy AHP priority derivation, which formulates the priorities of a fuzzy pairwise comparison matrix as a logarithmic nonlinear programming and derives crisp priorities from fuzzy pairwise comparison matrices. Numerical examples are tested to show the advantages of the proposed methodology and its potential applications in fuzzy AHP decision-making.

A proposed framework for implementing TQM in Chinese organizations

With ever‐increasing competition in today’s business environment, organizations are seeking every opportunity to improve their business results. Attaining continuous performance improvement and business excellence is the common goal that ties with the concepts of total quality management (TQM). This paper presents a review of the literature concerning the difficulties regarding TQM implementation. It is followed by a description of the UMIST‐TQM implementation framework. Further primary research has attempted to identify unique difficulties, which may exist in Chinese organizations with respect to TQM implementation and adoption of the UMIST framework. A case study approach of six Chinese companies was used to validate the framework to establish the current state of TQM implementation and achievements over a 12‐month period. The main findings conclude that while TQM can help organizations to achieve improvements, this is often delayed due to the lack of attention to cultural implications and systems. The paper proposes that the UMIST framework can enable Chinese organizations to select an appropriate starting point for their TQM efforts, and develop an improvement process at a pace suitable to their business environment.

Failure mode and effects analysis by data envelopment analysis

Failure mode and effects analysis (FMEA) is a method that examines potential failures in products or processes and has been used in many quality management systems. One important issue of FMEA is the determination of the risk priorities of failure modes. In this paper we propose an FMEA which uses data envelopment analysis (DEA), a well-known performance measurement tool, to determine the risk priorities of failure modes. The proposed FMEA measures the maximum and minimum risks of each failure mode. The two risks are then geometrically averaged to measure the overall risks of failure modes. The risk priorities are determined in terms of overall risks rather than maximum or minimum risks only. Two numerical examples are provided and examined using the proposed FMEA to show its potential applications and benefits.