What are the Innovations and Advancements in Decision Support Systems?4 answersInnovations and advancements in Decision Support Systems (DSS) have been significant, reflecting the evolving landscape of information technology and the increasing complexity of decision-making processes across various sectors. A key innovation is the integration of DSS with other advancements in IT, such as organizational computing and e-commerce, enhancing their utility in business and organizational decision-making. The interdisciplinary nature of management science has further enriched DSS by incorporating mathematical modeling, statistics, and data analysis to improve rational and accurate management decisions.
The development of a decision support system that combines multi-criteria analysis with Geographic Information Systems (DGIS) represents a significant advancement in planning urban development, particularly in energy efficiency and the use of renewable energy sources. The digital disruption of the 2010s introduced big data, analytics, and computational intelligence into DSS, leading to the development of digital coaching systems that adapt to users' cognitive levels, thereby enhancing decision-making in real-time scenarios.
Artificial Intelligence (AI)-based DSS have seen considerable growth, especially in improving construction project sustainability by leveraging artificial neural networks and fuzzy logic. This reflects a trend towards considering economic, environmental, and social goals of sustainability. The advent of analytics has revolutionized DSS by utilizing AI to derive value from large volumes of diverse data, thereby supporting decision-making across various industries.
Intelligent Decision Support Systems (IDSS) have evolved to employ AI and Computational Intelligence techniques for human-like decision-making, significantly impacting industrial activity and enhancing societal well-being. Innovations also include the application of interval neural networks for handling underdetermined data, offering a new structure for DSS that can perform high-speed calculations and decision-making.
The practical application of systems-based decision support tools, as demonstrated by Tampa Bay Water, highlights the importance of these innovations in addressing complex decision-making environments. Finally, the evolution of DSS technologies emphasizes the impact of advancements in computer, mobile, and internet technologies on the design and capabilities of modern DSS, offering insights for competitive advantage. Collectively, these advancements underscore the dynamic nature of DSS innovations, catering to the growing needs of efficient and effective decision-making in various domains.
How can i propose a Marine operations assistance system?4 answersTo propose a Marine Operations Assistance System, you can develop a decision support system based on a mobile terminal that provides real-time information and support for ship marine operations. This system can utilize weather forecasts, shipborne sensors, and ocean environment data to obtain information about the sea area where the ship is located. Additionally, you can design a system that divides the ship's route into segments and calculates the optimal vessel speed for each segment, taking into account factors such as fuel consumption and constraining conditions. Furthermore, integrating operation zone detections, online crew declaration systems, distress calls, and trajectory recording can enhance the system's capabilities for fisheries activities. For winter navigation in ice-covered waters, a data-driven assistance operation identification model can be used to understand ship behaviors and determine the need for icebreaker assistance. Finally, a marine traffic supporting system can be developed to assess navigation difficulties of vessels and provide real-time information to VTS operators, enhancing safe navigation in ports and approach channels.
What are the challenges to using artificial intelligence for decision support in maritime operations?5 answersArtificial intelligence (AI) presents both opportunities and challenges for decision support in maritime operations. One challenge is the potential security threats posed by AI in autonomous surface ships (MASSs). These threats include clean-label poisoning attacks on object detection models, which can lead to misclassification of objects and inaccurate decision making. Another challenge is the robustness of machine learning (ML) models to environmental variations in the maritime domain. ML models trained on limited data may struggle to adapt to different environmental conditions, such as variations in satellite imagery. Additionally, the enormous amount of navigation data gathered from marine traffic poses a challenge in creating a vessel decision support system. Deep reinforcement learning techniques have been proposed to address this challenge and enable safe navigation in high-density maritime traffic. Overall, the challenges to using AI for decision support in maritime operations include security threats, robustness to environmental variations, and handling large amounts of navigation data.
How can AI-driven decision support systems be used to improve safety and efficiency in maritime operations?5 answersAI-driven decision support systems can be used to improve safety and efficiency in maritime operations by providing navigators with intuitive and reliable solutions for collision avoidance and situation awareness in real time. These systems utilize advanced tools and techniques of artificial intelligence (AI) to gather and analyze data, identify and diagnose problems, and propose courses of action, mimicking human cognitive capabilities. By combining the power of computers and humans, AI-enabled decision support systems can alleviate the limitations of sheer autonomy and provide comprehensive and trustworthy procedures and results. They can assist navigators in making fast and competent decisions, optimizing ship trajectories, and minimizing energy consumption. Additionally, these systems can aid in the collection and analysis of large quantities of ship data, enabling better decision making based on historical and real-time information. Overall, AI-driven decision support systems have the potential to significantly reduce collision risk and improve the safety and efficiency of maritime operations.
How Artificial Intelligence is changing the Maritime Shipping Industry?5 answersArtificial intelligence (AI) is rapidly transforming the maritime industry by introducing intelligent ships and autonomous solutions. The integration of AI technologies in the maritime sector offers increased safety, efficiency, and performance. AI enables the processing and analysis of large amounts of data generated by maritime vessels, leading to improved decision-making and optimized solutions for various stakeholders. Deep learning-based models, such as stacked autoencoders, have been developed to classify the operating states of ships, allowing for better monitoring and control. However, the lack of transparency and interpretability in AI systems poses challenges in building trust and confidence among end-users. The emerging field of eXplainable AI (XAI) aims to address this issue by developing techniques to explain the predictions and decisions made by AI systems. Initiatives like the VesselAI project demonstrate the potential of combining AI, big data, and high-performance computing to create a unified system for advanced analytics and optimization in maritime applications. Overall, AI is revolutionizing the maritime shipping industry by enhancing safety, efficiency, and decision-making processes.
Why are decision support systems no longer used for natural disasters?5 answersDecision support systems are still used for natural disasters, as they play a crucial role in improving disaster management efforts and increasing the efficiency of search and rescue teams. These systems help in allocating the right resources at the right time, enhancing situational awareness, and facilitating decision-making processes. However, there are challenges in designing decision support systems for disaster management. The complexity of tasks in disaster response requires more than just adequate resources, and there is a need for new task management techniques to increase efficiency. Additionally, decision support systems need to address deep uncertainty, reflect field conditions and constraints, and enable rapid humanitarian logistics modeling. Research in this field has focused on individual problems and optimization techniques, but there is a need to consider integrated decision problems and simulation as alternatives to better support decision-makers in disaster relief logistics.