A Robust Grey Multi-Criteria Decision-Making Framework for Prioritization under Uncertainty: Application to Smart Transformation Systems

In the quiet, data-driven world of scientific institutions, the challenge of modernization is rarely about finding the single best new gadget. It is about navigating a fog of uncertainty. When a government agency responsible for monitoring the earth's tremors considers upgrading its systems, it faces a complex web of choices. Should it invest in artificial intelligence to detect earthquakes faster, or build a network of smart sensors? Should it secure its data with blockchain technology or move everything to the cloud? The answers are rarely clear-cut. Costs are estimates, timelines are guesses, and the future performance of these technologies is often unknown. In such situations, decision-makers cannot rely on precise numbers because those numbers simply do not exist yet. Instead, they must work with ranges of possibility, weighing the benefits of speed and security against the constraints of budget and time. This is the realm of decision-making under uncertainty, where the goal is not to find a perfect answer, but to find the most robust path forward when information is incomplete.

A team of researchers at the University of Tehran tackled this exact problem by developing a new way to help the Iranian Seismological Center prioritize its digital transformation. They recognized that traditional methods of choosing technology often fail in these high-stakes environments because they demand precise data that organizations simply do not have. To solve this, the researchers created a framework that embraces uncertainty rather than trying to eliminate it. They gathered a panel of ten senior experts, including former directors and current managers, to evaluate five specific smart projects: an AI system for detecting seismic events, a network of IoT-enabled smart stations, a blockchain system for securing data, a cloud platform for managing information, and a mobile app for public reporting. Instead of asking the experts to give a single, exact score for each project, the researchers asked them to provide ranges. This approach, known as grey system theory, allows for the expression of vague or incomplete information, such as "the cost will be between X and Y," rather than forcing a guess at a specific number.

The researchers then used a specialized mathematical process to weigh the importance of thirteen different criteria, ranging from cybersecurity and performance to cost and implementation time. They found that the experts placed the highest value on performance efficiency, cybersecurity, and usability, while also giving significant weight to the acquisition cost. This revealed a clear preference for solutions that are reliable and secure, even if they require careful financial planning. When the team applied their framework to rank the five projects, a clear leader emerged. The AI-based seismic event detection system was identified as the top priority, scoring significantly higher than the other options. This system was chosen because it offered the best balance of high performance, strong security, and manageable costs, directly addressing the most critical needs of the seismological center.

Following the AI system, the researchers ranked the IoT-based smart stations and the mobile reporting platform as the next most valuable investments. These two options scored very closely, suggesting they are both strong candidates for the next phase of development. The blockchain system for data integrity came in fourth, and the cloud-based data management platform ranked last. The lower ranking for the cloud and blockchain options did not mean they were bad technologies; rather, it indicated that under the current constraints of budget and organizational readiness, they were less feasible as immediate priorities compared to the AI and IoT solutions. The study explicitly showed that while blockchain offered excellent security, its high cost and complexity made it a less attractive starting point. Similarly, the cloud platform was held back by its substantial acquisition costs and weaker performance in organizational readiness criteria.

To ensure these results were not just a fluke of the specific experts' opinions, the researchers ran thousands of computer simulations to test the stability of their rankings. They varied the importance of the criteria slightly in each simulation to see if the order of the projects would change. The results were reassuring: the AI system remained the top choice in nearly 70 percent of the simulated scenarios, proving that the recommendation was robust and not overly sensitive to small changes in judgment. The study also highlighted that the mobile app and IoT stations were stable choices for the second and third spots, while the blockchain and cloud options showed more variability, reinforcing their lower priority.

The final outcome of this research is not just a list of winners and losers, but a practical roadmap for action. The researchers suggest that the seismological center should begin its transformation by implementing the AI detection system. Once that foundation is in place, the organization can move on to upgrading its sensor network with IoT technology and launching the mobile app for public communication. The more complex and expensive technologies, like blockchain and full-scale cloud migration, can be introduced later, once the organization has built up the necessary resources and experience. This step-by-step approach allows the institution to manage risk and spread out its investments over time. By using a method that accepts uncertainty as a natural part of the process, the researchers provided a clear, evidence-based guide for how a scientific government agency can modernize its operations without gambling on the unknown.