American Journal of Business and Operations Research

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2692-2967ISSN (Online) 2770-0216ISSN (Print)

An Innovative Multi-Criteria Decision-Making (MCDM) Framework for Picking the Right Used Chemical Tankers: A Classified Model-Based Discussion

Abedallah Z. Abualkishik , Rasha Almajed , William Thompson

Because chemical tanker boats are so expensive to build and maintain, shipping firms may not be able to supply their clients with fair transportation pricing. As a result, shipping businesses may find various benefits and chances by purchasing second-hand chemical tanker vessels. But picking a chemical tanker is a hard task that requires overcoming numerous misunderstandings and weighing several conflicting factors.  A novel MCDM technique has been proposed in this study for this aim. EDAS approach is used in the proposed model, to handle uncertainty. In order to demonstrate efficacy, relevance, and robustness, the model was used to address decision-making issues involving the selection of suitable second-hand chemical tankers from a pool of 10 (alternatives). The chemical tanker boats were evaluated using 14 distinct choice criteria in the present article. The findings show that the most important factor is "CTC6′′ Maintenance cost," and the best and most preferred chemical tanker is "CTA6"

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Vol. 7 Issue. 2 PP. 08-18, (2022)

Forecasting crude oil prices based on machine learning statistics methods and random sparse Bayesian learning

Irina V. Pustokhin , Denis A. Pustokhin

Oil price forecasting has received a great deal of interest from both professionals and scholars because of the unique characteristics of the oil price and its enormous impact on a wide range of economic sectors. In response to this problem, the authors set out to develop a strong model for accurately predicting the Brent crude oil price. We employed the Linear Regression and Random Forest models to examine the market interrelationships present in the oil price time series. Next, the models are given weights such that the experimental time series can be accurately predicted. These errors are quantified in terms of root mean squared errors (RMSE), average errors (MAE), and average percentage errors (MAPE). Results and forecast accuracy of the model as compared to the other model. To maximize their output and order levels and reduce the negative impact of potential shocks, countries that produce and import crude oil benefit greatly from accurate crude oil price forecasts.

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Vol. 7 Issue. 2 PP. 19-31, (2022)

Statistical Machine Learning Model and Commodity Futures Volatility Information for Financial Stock Market Forecasting

Denis A. Pustokhin , Irina V. Pustokhina

A country's economy and social structure are greatly influenced by the stock market. It is extremely difficult for investors, expert analysts, and scholars in the financial industry to forecast the stock market accurately because of the pretty unstable, parametric, non-linear dynamical, and unstable character of stock price time series. In the financial sector, stock market forecasting is a critical activity and a prominent study subject because stock market investments carry greater risk. It's conceivable, however, to reduce most of the risk through the development of computationally intelligent approaches. This paper introduces the support vector machine regression to make a model forecasting the stock market financial.

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Vol. 7 Issue. 2 PP. 32-40, (2022)

Multi-Criteria Decision-Making Approach based on Neutrosophic Sets for Evaluating Sustainable Supplier Selection in the Industrial 4.0

Mahmoud Ismail , Mahmoud Ibrahiem

Sustainability in supply chain management can be achieved by integrating its applications with Industry 4.0 platforms.  Considering the Sustainability and Industry 4.0 criteria for supplier selection, this research creates a new integrated model to improve the performance of the applicatios.  The choice of suppliers is evaluated using a two-stage neutrosophic sets and the EDAS method.  The first step of this research is to define all of the terms associated with Industry 4.0 and Sustainability.  The neutrosophic EDAS determines the relative relevance of each criterion.  The neutrosophic VIKOR method is used to rank the suppliers.  The suppliers' performance in meeting the sustainability and Industry 4.0 standards is then nominated in a two-stage neutrosophic sets.  A case study of a textile firm is offered to illustrate the usefulness of our integrated approach.  The effectiveness of the suggested integrated method is then evaluated via a series of sensitivity assessments.  Of the things we learned was that it's best to build a decision-making framework that uses Industry 4.0 and sustainability criteria to assess suppliers individually rather than in a relative fashion in a hazy setting.

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Vol. 7 Issue. 2 PP. 41-55, (2022)

An Analysis Framework to Study the Effects of Green Finance on Sustainable Smart Cities

Noura Metawa , Nawazish Mirza

Despite the fact that many countries around the world have adopted green growth as their strategy for economic development and those studies have investigated the factors that influence green growth from a variety of perspectives in sustainable smart cities, there is a paucity of literature that focuses on the impact that fintech and green finance have on green growth. This study attempts to design a complete index to assess the green growth of regional economies from the point of view of the development of fintech. The index will be based on an in-depth examination of the effect mechanism that green finance has on green growth. Additional study reveals that innovations in fintech mostly foster green economic development by means of green lending and green investment. As a result, advancements in fintech have the potential to foster green productivity expansion by elevating the development degree of green finance. This is a field that has a great deal of reference importance for most nations. The development of financial technology has the opposite effect on the building of sustainable smart cities. This is mostly because both the development and commoditization of fintech need more resources, and these resources also come at a greater price.

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Vol. 7 Issue. 2 PP. 56-67, (2022)