A prototype knowledge based fuzzy analytic network process system for sustainable manufacturing indicator

Sustainable manufacturing is a relatively new but a very complex manufacturing paradigm. The complexity arises as this paradigm covers three interdependent yet mutually supporting sustainability dimensions of economic, environmental and social. In a further step to embark on the essence of sustainable manufacturing, the development of appropriate indicators needs to be emphasized as compared to other efforts. Regrettably, the existing indicators have several drawbacks that may hamper the accuracy of sustainability performance assessment of an organization. As such, there are only a few standardized indicator mechanisms which can suit specific requirements of various manufacturing organizations. Hence, this study suggests a novel Knowledge-Based Fuzzy Analytic Network Process (KBFANP) system which can assist the decision making process of sustainable manufacturing by developing a new indicator mechanism. The KBFANP system comprises of four major phases, namely Initialization, Selection, Evaluation and Prioritization. The system incorporates the advantages of Knowledge-Based System Fuzzy Set Theory and Analytic Network Process into a single unified approach as a standardized indicator, which is applicable to all types of problem setting. A prototype of KBFANP system was developed, tested and analyzed on three experimental data sets and two real manufacturing settings. The system was able to provide solutions on the areas that need improvement with different levels of priority. This study also supports the notion of lean and green manufacturing as the elementary foundation of sustainable manufacturing implementation. The proposed KBFANP system can act as an advisory Decision Support System which is beneficial to both academia and industrial practitioners

Structuring e-learning multi-criteria decision making problems

Problem structuring is one of the most critical phases of decision making process. A well-posed problem has direct impact on effective decision making, especially when we use the multi-criteria decision making methods. There are different decision making methods that have been used for decision making on e-learning issues in higher education, but the most suitable method for this kind of problems is the Analytic Network Process (ANP). ANP meets all the theoretical requirements of decision making in higher education, but policy makers use it very rarely in practice because of its implementation weaknesses. One of the weaknesses is a lack of support in structuring problem in the form of a network. This paper brings an overview of several problem structuring methods and approaches, such as simple top-down and bottom-up approaches, the PrOACT approach, ISM (Interpretative Structural Modelling), DEMATEL (Decision Making Trial and Evaluation Laboratory) and the PAPRIKA structuring method. It also brings analysis of how those structuring methods and approaches help overcome some of the ANP weaknesses. Finally, we provide some recommendations of how to design a new problem structuring method that fits the ANP needs

A review of two decades of research on the application of multicriteria decision making techniques to evaluate e-learning’s effectiveness

Multicriteria decision-making (MCDM) techniques have been widely adopted to evaluate the effectiveness of e-learning. However, the literature review has not kept pace with the rapid accumulation of knowledge in this field. This study systematically reviews the MCDM techniques applied in e-learning issues. In total, we reviewed 77 published studies selected from the Web of Science and Scopus databases. We classified the selected studies by the publication year, the authors’ nationality, and the type of MCDM techniques examined. We further discovered that the majority of previous studies adopted Information System Success Model (D&M model) which was proposed by Delone and McLean in 1992. Due to limited features that were provided by the e-learning system back in the mid-2000, the original D&M model might not consider some significant factors such as students’ characteristics, instructors’ characteristics, user interface and learning community. The goals of this systematic literature review are to understand if the original D&M model is addressed in modern e-learning and to determine if new factors have emerged to evaluate e-learning’s effectiveness but not captured by the original D&M model. This review contributes three new theoretical perspectives. First, reclassification of the D&M model is conducted to include learners’ characteristics, instructors’ characteristics, user interface, and learning community. Second, this study discovers that there is a need to perform reclassification of overlapping subfactors. Third, this review also identifies that the dependencies among the factors in the D&M model are inadequately examined in previous studies. Researchers can utilize the findings of this study as a foundation to formulate their research frameworks, and practitioners can integrate the significant factors identified in this study into their decision-making processes when developing e-learning at their institutions