Evaluation of e-learning web sites using fuzzy axiomatic design based approach

High quality web site has been generally recognized as a critical enabler to conduct online business. Numerous studies exist in the literature to measure the business performance in relation to web site quality. In this paper, an axiomatic design based approach for fuzzy group decision making is adopted to evaluate the quality of e-learning web sites. Another multi-criteria decision making technique, namely fuzzy TOPSIS, is applied in order to validate the outcome. The methodology proposed in this paper has the advantage of incorporating requirements and enabling reductions in the problem size, as compared to fuzzy TOPSIS. A case study focusing on Turkish e-learning websites is presented, and based on the empirical findings, managerial implications and recommendations for future research are offered

Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework

A New Extended MILP MRP Approach to Production Planning and Its Application in the Jewelry Industry

It is important to manage reverse material flows such as recycling, reusing, and remanufacturing in a production environment. This paper addresses a production planning problem which involves reusing of scrap and recycling of waste that occur in the various stages of the production process and remanufacturing/recycling of returns in a closed-loop supply chain environment. An extended material requirement planning (MRP) is proposed as a mixed integer linear programming (MILP) model which includes-beside forward-these reverse material flows. The proposed model is developed for the jewelry industry in Turkey, which uses gold as the primary resource of production. The aim is to manage these reverse material flows as a part of production planning to utilize resources. Considering the mostly unpredictable nature of reverse material flows, the proposed model is likewise transformed into a fuzzy model to provide a better review of production plan for the decision maker. The suggested model is examined through a case study to test the applicability and efficiency

A combined fuzzy group decision making framework to evaluate agile supply chain enablers

To support an effective agile supply chain management, we examine in this paper agile supply chain enablers in an analytical context We propose a new integrated method combining fuzzy logic, decision making trial and evaluation laboratory and analytic network process to determine the most important factors of agility in the supply chain management We also demonstrate the potentials of the methodology by a case study in an Turkish automotive industr

A Methodology to Investigate Challenges for Digital Twin Technology in Smart Agriculture

The agriculture sector is fundamental for social, economic, and environmental development. It needs novel approaches and technology-integrated processes to preserve its critical importance and survive for the future. Agricultural digitalization is an essential component of agricultural industrialization, focusing on agricultural research, infrastructural improvements, and data services. The combination of the Internet of Things/Everything (IoT/IoE) with RFID, sensors, and high-tech meters makes up smart agriculture (SA). Controlling and monitoring have become more easily applicable thanks to these technological improvements. SA replaces conventional farming methods with effective, rapid, and sustainable ones. It has the power to control water, pesticides, security, the environment, machines, and vehicles. Digital Twin (DT) technology is the mutual use of digital technologies such as remote sensing, IoT, and simulation. With its integrated structure, DT can help farmers to create a virtual twin of their physical entities in the virtual space. Accordingly, generating strategies and planning the production can be controlled by running simulations with the field's collected data. Therefore, this paper aims to investigate challenges to DT adoption in SA. For that purpose, a multicriteria decision-making (MCDM) approach is suggested. DEMATEL technique is provided to prioritize and evaluate causal relationships for DT adoption challenges. The DEMATEL technique is integrated with the 2-Tuple Linguistic (2-TL) model to improve its ability to deal with linguistic variables and create a decision-making process closer to human cognitive processes. A real case study is provided to test the applicability of the suggested methodology, and further discussions are presented

Smart Agriculture Technology Evaluation: A Linguistic-based MCDM Methodology

Agricultural operations have been highly affected by all the industrial revolutions. From ancient times to today, agrarian systems have evolved parallel to technological developments. For a decade, we have been facing a new industrial revolution, Industry 4.0. It is for sure that the existing agrarian systems will be affected by this digital transformation. Since agricultural systems are critical production networks for civilizations, their change should be addressed carefully. For that purpose, this paper focuses on the technology evaluation for Smart Agriculture (SA). The SA area is chosen thanks to its importance for sustainable development and production systems. Thus, the expectations from SA are derived from the SA advantages stated in the academic and industrial literature. Afterward, the technologies are assessed according to their ability to meet these expectations. To obtain the most powerful technology, the expectations are first weighted via the 2-Tuple Linguistic (2-TL) DEMATEL technique, then 2-TL-MARCOS is used to calculate the technology prioritization. To overcome the ambiguity about a newly emerged subject as SA, using linguistic variables via the 2-TL approach is one of the essential contributions of this paper. Moreover, this paper suggests a multi-criteria decision-making (MCDM) approach to create a comprehensive understanding of digital technologies and their use and benefits in agricultural systems. A real case study is presented with a sensitivity analysis to test the proposed methodology's applicability and replicability