Providing a mathematical model for the development of sustainable investment in blockchain with a focus on renewable energies (evaluation and operational case study)
Subject Areas : Industrial ManagementAkram Alikazemi 1 , Akbar Alamtabriz 2 , Ali Rezaeian 3
1 - PhD Candidate of Industrial Management, Faculty of Management and Accounting, Qazvin Islamic Azad University, Qazvin, Iran
2 - Professor, Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
3 - Professor, Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
Keywords: Digital currency, multi-criteria decision making, mathematical optimization, green investment,
Abstract :
With the advancement of digital technologies as the axis of development of Industry 4.0, the need to develop equipment energy supply infrastructure has created a management challenge. This challenge in connection with blockchain technology as the cornerstone of technology development in the economic, industrial and investment sectors, which requires huge sources of sustainable energy supply, has a double importance. Few researches have investigated this issue from the perspective of decision-making science, and the existing solutions The need of managers is not to provide transparent and reliable strategies. This research has developed a hybrid approach based on decision-making methods and multi-objective mathematical optimization, in which types of renewable energy sources are prioritized with the aim of developing green investment in digital currencies. In the first step, the most important criteria The investment management and development of renewable energies are weighted with the help of fuzzy Swara method and in the next step, all the available options including solar, wind, heat recovery, biomass and electric power etc. are prioritized with the help of Idas and Vicor fuzzy methods. Then, by developing a multi-objective optimization model, the amount of investment in each option is optimally determined based on economic, environmental and social functions. In order to provide all achievable Pareto responses, the enhanced epsilon constraint method is used. According to the numerical results, it can be seen that solar, wind, heat recovery, biomass and electricity are selected as priority renewable energies.