uncertainty
Iman Ebrahimi; Hadi Mokhtari; Mohammad Taghi Rezvan
Abstract
The cryptocurrency market, known for being one of the most volatile financial markets, has recently attracted significant attention. While some investors avoid entering this market due to fear of losses, others are eager to pursue substantial profits. This research employs scientific analysis and advanced ...
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The cryptocurrency market, known for being one of the most volatile financial markets, has recently attracted significant attention. While some investors avoid entering this market due to fear of losses, others are eager to pursue substantial profits. This research employs scientific analysis and advanced models to examine the characteristics of this market. In the first phase, the focus is on portfolio formation in the cryptocurrency market based on relevant indicators. After evaluating the created portfolio, Markowitz, Sharpe and Sortino models are utilized for portfolio optimization. This approach allows for the creation of a portfolio that aligns with individual risk preferences, and passive portfolio management is also considered. In the second phase of the research, equal-weighted portfolios were formed alongside a market portfolio, and nine other selected portfolios were chosen based on criteria such as highest market value and best risk-return ratio. A particle swam optimization algorithm was employed to assess the performance of these portfolios. The results indicate that portfolios containing cryptocurrencies with the highest market value exhibit lower risk, and the Sharpe optimization model outperforms other models. Additionally, selecting a portfolio based on the standard deviation of return-to-risk ratio yields more favorable outcomes. This study also presents an innovative method for analyzing the coefficient of variation, leading to a better understanding of the relationship between return and risk. Ultimately, the findings emphasize that utilizing scientific trading strategies can facilitate risk management and enhance returns.
multiple-criteria decision-making
Ali Memarpour Ghiaci; morteza abbasi; Jafar Gheidar-Kheljani
Abstract
Suppliers evaluation plays a pivotal role in the success of modular megaprojects, as these projects require capable suppliers due to the necessity for complex coordination among various subsystems and the precise integration of modules. This study proposes an integrated framework for the evaluation of ...
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Suppliers evaluation plays a pivotal role in the success of modular megaprojects, as these projects require capable suppliers due to the necessity for complex coordination among various subsystems and the precise integration of modules. This study proposes an integrated framework for the evaluation of suppliers in modular megaprojects. For the first time, this research applies a novel integrated approach based on the LOPCOW and ARTASI methods, extended using spherical fuzzy sets (SF-LOPCOW and SF-ARTASI) for supplier evaluation. Based on this approach, 31 sustainability-oriented criteria have been identified for evaluating suppliers in modular megaprojects. The criteria are first weighted using the SF-LOPCOW method. Subsequently, in a case study, 12 suppliers identified for a modular megaproject are evaluated and prioritized using the SF-ARTASI method. A comparison of the SF-ARTASI results with other existing multi-criteria decision-making methods in the literature, along with a sensitivity analysis, demonstrates the effectiveness of the proposed approach and the robustness of its results under different scenarios.
modeling and simulation
Faezeh baliagha; Zeinolabedin Sadeghi; Sayyed Abdolmajid Jalaee
Abstract
In recent decades, the decline in total factor productivity (TFP) has become an economic challenge in many countries.One of the main drivers of this decline is the misallocation of resources among firms, which has a significant impact on economic performance. This research aims to investigate the extent ...
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In recent decades, the decline in total factor productivity (TFP) has become an economic challenge in many countries.One of the main drivers of this decline is the misallocation of resources among firms, which has a significant impact on economic performance. This research aims to investigate the extent of profit or loss resulting from the misallocation of production factors, using data from the Statistical Center of Iran for workshops with ten or more employees from 2011 to 2020. The research methodology is based on the analysis of microeconomic data and, by calculating physical productivity and value added, the status of various industries has been evaluated. The findings show that the chemical, non-metallic mineral, and petroleum products industries have had the highest productivity and value added, while the leather, clothing, and machinery repair industries have recorded the lowest. Also, the elimination of production and capital deviations has created the most improvement in high-yield industries and has shown the need for resource redistribution. In this regard, supporting low-yield industries, investing in research and development, improving energy and transportation infrastructure, and providing tax incentives are proposed. These measures can increase the productivity and competitiveness of industries and contribute to sustainable economic growth
production and operations management
Mohammad Rahim Ramazanian; Marjan Khodaparast Nodehei; Reza Sheikh
Abstract
Digital transformation and Industry 4.0 have emerged as key drivers for enhancing competitiveness and improving product quality across various industries, particularly in the automotive parts sector. This research focuses on Gilan Province, examining the extent of Industry 4.0 technologies' implementation ...
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Digital transformation and Industry 4.0 have emerged as key drivers for enhancing competitiveness and improving product quality across various industries, particularly in the automotive parts sector. This research focuses on Gilan Province, examining the extent of Industry 4.0 technologies' implementation aimed at increasing product longevity. Initially, through qualitative content analysis of 35 articles published between 2016 and 2024, 16 sub-criteria were identified within four main groups. Subsequently, using the novel OPLO-POCOD method (Opportunity Lost Assessment Based on Distance in Polar Coordinate Space) and surveying 14 experts from 10 parts manufacturing companies, the performance of these companies was analyzed.The results indicated that criteria such as automated warehousing systems, inventory management automation, and blockchain-based tracking had the highest impact on increasing product lifespan, with the lowest opportunity loss values of 0.0231, 0.0242, and 0.0253, respectively. On the other hand, the physical-information integration of the supply chain using cloud computing is still in the early stages of implementation.This research uniquely combines qualitative analyses with the innovative OPLO-POCOD method, enabling precise ranking of companies and identification of execution gaps. The findings emphasize the importance of focusing on smart technologies to achieve more sustainable and competitive production, assisting managers and policymakers in prioritizing Industry 4.0 strategies. Overall, while automotive parts industries in Gilan have made progress in areas such as automated warehousing, there is a need to accelerate the implementation of new technologies like cloud computing to fully realize the benefits of Industry 4.0 and complete the digital transformation process.
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