Supplier selection in Telecom supply chain management: a Fuzzy-Rasch based COPRAS-G method

In the past decade, global competition are forcing firms to increase their level of outsourcing for raw or semi-finished products and building long term relationship with their supply chain partners. The objective is to present a wide-ranging decision making technique for ranking supplier alternatives in view of the effect of selected criteria. A proposed method is developed aiming the usage of Fuzzy-Rasch model applying five point Likert scale for criteria weight and Grey based COmplex PRoportional ASsessment (COPRAS-G) method for evaluating and ranking the potential alternatives, as per criteria. The applicability of the induced methodology for supplier selection problem in all environments is shown through a case study in telecommunication sector. A sensitivity analysis is performed based on changing weight patterns of criteria to show the stability in ranking result of the proposed approach. Further, a comparative analysis between the ranking results of proposed method done with existing grey multi-attribute decision-making methods viz. VIKOR-G, ARAS-G and TOPSIS-G using spearman’s correlation coefficient for checking the reliability of the ranking result

Boundary Stabilization of Torsional Vibrations of a Solar Panel

In this paper, we study a boundary stabilization of the torsional vibrations of a solar panel. The panel is held by a rigid hub at one end and is totally free at the other. The dynamics of the overall system leads to hybrid system of equations. It is set to a certain initial vibrations with a control torque as a stabilizer at the hub end only. Taking a non-linear damping as boundary stabilizer, a uniform exponential energy decay rate is obtained directly. Thus an explicit form of uniform stabilization of the system is achieved by means of the exponential energy decay estimate

Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management

A multi-criteria decision making for renewable energy selection using Z-numbers in uncertain environment

In recent era of globalization, the world is perceiving an alarming rise in its energy consumption resulting in shortage of fossil fuels in near future. Developing countries like India, with fast growing population and economy, is planning to explore among its existing renewable energy sources to meet the acute shortage of overall domestic energy supply. For balancing diverse ecological, social, technical and economic features, selection among alternative renewable energy must be addressed in a multi-criteria context considering both subjective and objective criteria weights. In the proposed COPRAS-Z methodology, Z-number model fuzzy numbers with reliability degree to represents imprecise judgment of decision makers’ in evaluating the weights of criteria and selection of renewable energy alternatives. The fuzzy numbers are defuzzified and renewable energy alternatives are prioritized as per COmplex PropoRtional ASsessment (COPRAS) decision making method in terms of significance and utility degree. A sensitivity analysis is done to observe the variation in ranking of the criteria, by altering the coefficient of both subjective and objective weight. Also, the proposed methodology is compared with existing multi-criteria decision making (MCDM) methods for checking validity of the obtained ranking result

Neural network based country wise risk prediction of COVID-19

The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimize and automatically design country-specific networks. The results show that the proposed pipeline outperforms state-of-the-art methods for data of 180 countries and can be a useful tool for such risk categorization. We have also experimented with the trend data and weather data combined for the prediction. The outcome shows that the weather does not have a significant role. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlie