Search results

This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

The ongoing anthropological transformation urges the rethinking of education, underlining the inadequacy of our schools and universities in dealing with hypercomplexity, that is, with the global extension of all political, social, and cultural processes and with their indeterminacy, interdependence, and interconnection. The idea that educational processes are questions of a purely technical/technological nature, solely a problem of skills and know-how, is the “great mistake” of the hypertechnological society, based on the illusion of being able to measure and quantify everything, to eliminate error and unpredictability, and to achieve total control and rationality. It is necessary to rethink education radically because the extraordinary scientific discoveries and the dynamics of the new technologies have completely overturned the complex interaction between biological and cultural evolution, doing away with the borders between the natural and the artificial. Emergence and emergency themselves are structural features of complex systems (living, social, and human systems), rendered hypercomplex through today’s acceleration and virality, regarding not only education and socialization but also the representations and perceptions of all systemic processes. The merging of fields of knowledge and an epistemology of error become essential for the analysis and interpretation of this hypercomplexity and the unpredictability that distinguishes it.

In recent years, the airline industry has been growing and transforming rapidly in the Asia-Pacific area. This study analyzes and benchmarks the comparative operational efficiencies of the major Asian air carriers. Data envelopment analysis model and disaggregate output efficiency measures are used to evaluate the operational efficiencies of 31 Asian airlines from 2015 to 2019. The findings suggest that nonflag carriers, low-cost carriers, and high-income regions' carriers have significantly higher levels of efficiency than flag carriers, full-service carriers, and low-income regions' carriers in overall, revenue, and passenger traffic efficiencies. The efficiencies between alliance carriers and nonalliance carriers along with those of ASEAN and non-ASEAN carriers are not significantly different.

In February 2022, the Finance Minister of India in the Union Budget 2022 announced that the government proposed to issue sovereign green bonds to mobilize assets for green infrastructure. These bonds are a sort of fixed-income instrument where the money raised from investors is used exclusively to finance projects having a positive environmental impact. The announcement was in sync with India's commitment to achieving net-zero carbon emissions by 2070. However, many issues come with it such as the complexity of green data, and the lack of uniform standards to measure the impact of green investments leading to allegations of “greenwashing,” among others. Its solution lies in the digital tokenization of green bonds using blockchain technology. Foreign investors scout for green bonds issued by growing markets like India, which have attractive valuations and good growth prospects. Marketing and issuing green bonds properly would have a far greater potential to bring investment to the security markets and the much-needed advancement in the sustainable sector. It is much more likely that green bonds will bring investment to the security markets and much-needed advancement to the sustainable sector if they are marketed and issued through digital tokenization. Financial regulators and policymakers can create a global framework for the application of blockchain technology in sustainable finance. This might entail tokenizing eco-friendly assets, issuing eco-friendly bonds, trading renewable energy and 2-2 carbon credits in a decentralized ecosystem, and decentralizing crowdfunding for eco-friendly enterprises.

This chapter seeks to demonstrate how blockchain technology can help issue green bonds and increase the overall efficiency of green finance in the economy. It also aims to scrutinize how such digital tokenization of green bonds would affect the security market and increase the standards of environmental, social, and governance (ESG) worldwide. While discussing how this process is shaping up and impacting the economies of various countries, it also seeks to provide suggestions to be taken into consideration while adopting the digital tokenization of green bonds.