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Supporting Decision Making During a Pandemic: Influence of Stress, Analytics, Experts, and Decision Aids
Published in Jay Liebowitz, The Business of Pandemics, 2020
Gloria Phillips-Wren, Jean-Charles Pomerol, Karen Neville, Frédéric Adam
Decision support technologies take many forms, from traditional and advanced intelligent decision support systems to modern concepts of analytics. Traditional decision support takes inputs from the environment and human users, processes those inputs through mathematical and statistical models, and provides outputs in terms of one or more alternatives. The user interacts with the system to provide additional data or information, modify the selection of models, and accept or reject alternatives. Intelligent decision support systems augment the system with various types of artificial intelligence and machine learning so that the system can be smarter, comprehensive, adaptive, and anticipatory in support of the decision maker. All of these systems intend to improve the quality of semi-structured and unstructured decisions.
Green Healthcare for Smart Cities
Published in Pradeep Tomar, Gurjit Kaur, Green and Smart Technologies for Smart Cities, 2019
Prabhjot Singh, Varun Dixit, Jaspreet Kaur
Intelligent decision support systems (IDSS) can be defined as a system that helps in decision-making by the use of AI and machine-learning technologies (Chang et al. 1994). These systems generally use a hybrid approach, utilizing simple rule-based engines, knowledge-based engines and neural network engines. It performs an interpretive analysis of large-scale data with intelligent and knowledge-based methods.
Recommended layer thickness to the powder-based additive manufacturing using multi-attribute decision support
Published in International Journal of Computer Integrated Manufacturing, 2021
Previous mentioned methods and approaches present comprehensive knowledge from DfAM field. That knowledge needs to be collected, studied and organised into functional units to be ready for implementation. The latest engineering and industry trends are encouraging the integration of intelligent decision support systems and advanced information technologies to the production systems. Additive manufacturing is considered as an essential part of that integration (Dilberoglu et al. 2017). Intelligent systems are used to store collected knowledge into organised units to help users in making decisions. An intelligent system’s application to additive manufacturing support makes the design process more efficient and productive.
A generic knowledge management approach towards the development of a decision support system
Published in International Journal of Production Research, 2021
Oussama Meski, Farouk Belkadi, Florent Laroche, Mathieu Ritou, Benoit Furet
Along the technological evolutions, and the development of research on DSS, this field greatly evolved and several approaches and subfields emerged over time. Arnott and Pervan (2005) deals with DSS and defines the main subfields and their evolution over time. It is hence possible to describe some types of DSS: Personal Decision Support Systems: as the name implies, these are often developed for a single manager or a group of independent managers.Group Support Systems: are developed to improve the work and collaboration among a working group. The main objective of this system is to encourage decision-making based on all the suggestions proposed by the various members of the group.Negotiation Support Systems: this type of system uses computer technologies to facilitate negotiation. There are two categories of these systems: problem-oriented and process-oriented.Intelligent Decision Support Systems: this category of system introduces the use of artificial intelligence in decision support. They are classified into two generations: the first is rule-based systems and the second generation includes genetic algorithms, fuzzy logic and neural networks.Knowledge Management-Based DSS: these systems allow the taking based on the capitalisation of knowledge and the development of knowledge repositories. Also the manipulation, transfer and reuse of knowledge, for the purpose of decision-making and thus the creation of new knowledge.