Joshua Ignatius

What is the leanness level of your organisation in lean transformation implementation? An integrated lean index using ANP approach

We develop a lean index to assess the leanness level of the organisation in sustaining lean transformation. This ‘lean index’ is developed from theory, and is quantified using a multi-criteria approach i.e., analytic network process (ANP). This index provides a useful measure for sustainable lean performance because it adopts a holistic approach of performance measurement based on the socio-technical perspective which considers the interdynamics of human, system and technology.

A new method for deriving priority weights by extracting consistent numerical-valued matrices from interval-valued fuzzy judgement matrix

It is important to derive priority weights from interval-valued fuzzy preferences when a pairwise comparative mechanism is used. By focusing on the significance of consistency in the pairwise comparison matrix, two numerical-valued consistent comparison matrices are extracted from an interval fuzzy judgement matrix. Both consistent matrices are derived by solving the linear or nonlinear programming models with the aid of assessments from Decision Makers (DMs). An interval priority weight vector from the extracted consistent matrices is generated. In order to retain more information hidden in the intervals, a new probability-based method for comparison of the interval priority weights is introduced. An algorithm for deriving the final priority interval weights for both consistent and inconsistent interval matrices is proposed. The algorithm is also generalized to handle the pairwise comparison matrix with fuzzy numbers. The comparative results from the five examples reveal that the proposed method, as compared with eight existing methods, exhibits a smaller degree of uncertainty pertaining to the priority weights, and is also more reliable based on the similarity measure.

A bi-objective weighted model for improving the discrimination power in MCDEA

Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. We found GPDEA models to be invalid and demonstrate that our proposed bi-objective multiple criteria DEA (BiO-MCDEA) outperforms the GPDEA models in the aspects of discrimination power and weight dispersion, as well as requiring less computational codes. An application of energy dependency among 25 European Union member countries is further used to describe the efficacy of our approach.

Carbon Efficiency Evaluation : An Analytical Framework Using Fuzzy DEA

Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency of alternatives based on their inputs and outputs. The alternatives can be in the form of countries who attempt to enhance their productivity and environmental efficiencies concurrently. However, when desirable outputs such as productivity increases, undesirable outputs increase as well (e.g. carbon emissions), thus making the performance evaluation questionable. In addition, traditional environmental efficiency has been typically measured by crisp input and output (desirable and undesirable). However, the input and output data, such as CO2 emissions, in real-world evaluation problems are often imprecise or ambiguous. This paper proposes a DEA-based framework where the input and output data are characterized by symmetrical and asymmetrical fuzzy numbers. The proposed method allows the environmental evaluation to be assessed at different levels of certainty. The validity of the proposed model has been tested and its usefulness is illustrated using two numerical examples. An application of energy efficiency among 23 European Union (EU) member countries is further presented to show the applicability and efficacy of the proposed approach under asymmetric fuzzy numbers.

An integrated fuzzy ANP–QFD approach for green building assessment

One of the major concerns in the construction industry is the sustainability of building projects. There are various trade-offs between functionality and design, which often lead to an issue of whether sustainably designed buildings would meet stakeholder requirements. This paper provides a novel integrated structure for assessing green buildings realistically based on stakeholders’ fuzzy preferences. In particular, the paper uses the analytic network approach (ANP) to evaluate the correlation matrices in a quality function deployment (QFD) framework. A case study on green building index assessment in Malaysia illustrates the proposed integrated method. Sensitivity analysis validated the customerstakeholder agreement towards the design of the green building. Cluster analysis was also used to group design specifications prior to the analysis.

A fuzzy expected value approach under generalized data envelopment analysis

Fuzzy data envelopment analysis (DEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units (DMUs). Although several approaches for solving fuzzy DEA models have been developed, there are some drawbacks, ranging from the inability to provide satisfactory discrimination power to simplistic numerical examples that handles only triangular fuzzy numbers or symmetrical fuzzy numbers. To address these drawbacks, this paper proposes using the concept of expected value in generalized DEA (GDEA) model. This allows the unification of three models - fuzzy expected CCR, fuzzy expected BCC, and fuzzy expected FDH models - and the ability of these models to handle both symmetrical and asymmetrical fuzzy numbers. We also explored the role of fuzzy GDEA model as a ranking method and compared it to existing super-efficiency evaluation models. Our proposed model is always feasible, while infeasibility problems remain in certain cases under existing super-efficiency models. In order to illustrate the performance of the proposed method, it is first tested using two established numerical examples and compared with the results obtained from alternative methods. A third example on energy dependency among 23 European Union (EU) member countries is further used to validate and describe the efficacy of our approach under asymmetric fuzzy numbers.

An improved consensus-based group decision making model with heterogeneous information

In group decision making (GDM) problems, it is natural for decision makers (DMs) to provide different preferences and evaluations owing to varying domain knowledge and cultural values. When the number of DMs is large, a higher degree of heterogeneity is expected, and it is difficult to translate heterogeneous information into one unified preference without loss of context. In this aspect, the current GDM models face two main challenges, i.e., handling the complexity pertaining to the unification of heterogeneous information from a large number of DMs, and providing optimal solutions based on unification methods. This paper presents a new consensus-based GDM model to manage heterogeneous information. In the new GDM model, an aggregation of individual priority (AIP)-based aggregation mechanism, which is able to employ flexible methods for deriving each DMs individual priority and to avoid information loss caused by unifying heterogeneous information, is utilized to aggregate the individual preferences. To reach a consensus more efficiently, different revision schemes are employed to reward/penalize the cooperative/non-cooperative DMs, respectively. The temporary collective opinion used to guide the revision process is derived by aggregating only those non-conflicting opinions at each round of revision. In order to measure the consensus in a robust manner, a position-based dissimilarity measure is developed. Compared with the existing GDM models, the proposed GDM model is more effective and flexible in processing heterogeneous information. It can be used to handle different types of information with different degrees of granularity. Six types of information are exemplified in this paper, i.e., ordinal, interval, fuzzy number, linguistic, intuitionistic fuzzy set, and real number. The results indicate that the position-based consensus measure is able to overcome possible distortions of the results in large-scale GDM problems.

Assessing Knowledge Sharing Among Academics: A Validation of the Knowledge Sharing Behavior Scale (KSBS).

Background: There is a belief that academics tend to hold on tightly to their knowledge and intellectual resources. However, not much effort has been put into the creation of a valid and reliable instrument to measure knowledge sharing behavior among the academics. Objectives: To apply and validate the Knowledge Sharing Behavior Scale (KSBS) as a measure of knowledge sharing behavior within the academic community. Subjects: Respondents (N = 447) were academics from arts and science streams in 10 local, public universities in Malaysia. Measures: Data were collected using the 28-item KSBS that assessed four dimensions of knowledge sharing behavior namely written contributions, organizational communications, personal interactions, and communities of practice. Results: The exploratory factor analysis showed that the items loaded on the dimension constructs that they were supposed to represent, thus proving construct validity. A within-factor analysis revealed that each set of items representing their intended dimension loaded on only one construct, therefore establishing convergent validity. All four dimensions were not perfectly correlated with each other or organizational citizenship behavior, thereby proving discriminant validity. However, all four dimensions correlated with organizational commitment, thus confirming predictive validity. Furthermore, all four factors correlated with both tacit and explicit sharing, which confirmed their concurrent validity. All measures also possessed sufficient reliability (α > .70). Conclusion: The KSBS is a valid and reliable instrument that can be used to formally assess the types of knowledge artifacts residing among academics and the degree of knowledge sharing in relation to those artifacts.

A two-stage dynamic group decision making method for processing ordinal information

In group decision making (GDM) problems, ordinal data provide a convenient way of articulating preferences from decision makers (DMs). A number of GDM models have been proposed to aggregate such kind of preferences in the literature. However, most of the GDM models that handle ordinal preferences suffer from two drawbacks: (1) it is difficult for the GDM models to manage conflicting opinions, especially with a large number of DMs; and (2) the relationships between the preferences provided by the DMs are neglected, and all DMs are assumed to be of equal importance, therefore causing the aggregated collective preference not an ideal representative of the groups decision. In order to overcome these problems, a two-stage dynamic group decision making method for aggregating ordinal preferences is proposed in this paper. The method consists of two main processes: (i) a data cleansing process, which aims to reduce the influence of conflicting opinions pertaining to the collective decision prior to the aggregation process; as such an effective solution for undertaking large-scale GDM problems is formulated; and (ii) a support degree oriented consensus-reaching process, where the collective preference is aggregated by using the Power Average (PA) operator; as such, the relationships of the arguments being aggregated are taken into consideration (i.e., allowing the values being aggregated to support each other). A new support function for the PA operator to deal with ordinal information is defined based on the dominance-based rough set approach. The proposed GDM model is compared with the models presented by Herrera-Viedma et al. An application related to controlling the degradation of the hydrographic basin of a river in Brazil is evaluated. The results demonstrate the usefulness of the proposed method in handling GDM problems with ordinal information.

Comparative study of IgA VH3 gene usage in healthy TST− and TST+ population exposed to tuberculosis: deep sequencing analysis

The acquired immune response against tuberculosis is commonly associated with T‐cell responses with little known about the role of B cells or antibodies. There have been suggestions that B cells and humoral immunity can modulate the immune response to Mycobacterium tuberculosis. However, the mechanisms involving B‐cell responses in M. tuberculosis are not fully understood, in particular the antibody gene preferences. We hypothesized that a preferential use of V genes can be seen associated with resistance to infection mainly in the IgA isotype, which is of prominent importance for infection by pathogens via the mucosal route. We studied healthy individuals with long‐term exposure to tuberculosis, infected (TST+) and uninfected TST−) with M. tuberculosis. From a total of 22 V genes analysed, the TST− population preferred the VH3‐23 and Vκ1 genes. The VH3‐23 genes were subsequently subjected to 454 amplicon sequencing. The TST− population showed a higher frequency of the D3‐10 segment compared with the D3‐22 segment for the TST+ population. The J segment usage pattern was similar for both populations with J4 segment being used the most. A preferential pairing of J4 segments to D3‐3 was seen for the TST− population. The antibodyome difference between both populations suggests a preference for antibodies with VH3‐23, D3‐3, JH4 gene usage by the TST− population that could be associated with resistance to infection with M. tuberculosis.