ISCRAM Digital Library -- Query Results

Jutta Hild, Jonathan Ott, Yvonne Fischer, & Christian Glökler. (2010). Markov based decision support for cost-optimal response in security management. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM. Abstract: In this contribution, we introduce a prototype of a decision support tool for cost-optimal response in security management. The threat situation of a closed infrastructure, exposed to multiple threats, and the corresponding response actions are modeled by a continuous-time Markov decision process (CMDP). Since the CMDP cannot be solved exactly for large infrastructures, the response actions are determined from a heuristic, based on an index rule. The decision support tool's user interface displays the infrastructure's current threat state and proposes the heuristic response actions to the decision maker. In this way, global situation awareness can be enhanced and the decision maker is able to initiate an almost cost-optimal response action in short time. Keywords: Continuous time systems; Costs; Decision making; Decision support systems; Industrial management; Information systems; Markov processes; User interfaces; Continuous-time markov decision process; Cost-optimal response; Decision support tools; Security management; Situation awareness; Optimization Track: Poster Session http://idl.iscram.org/show.php?record=580
Zhenyu Yu, Chuanfeng Han, & Ma Ma. (2014). Emergency decision making: A dynamic approach. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 240–244). University Park, PA: The Pennsylvania State University. Abstract: The dynamic nature of emergency decision making exerts difficulty to decision makers for achieving effective management. In this regard, we suggest a dynamic decision making model based on Markov decision process. Our model copes with the dynamic decision problems quantitatively and computationally, and has powerful expression ability to model the emergency decision problems. We use a wildfire scenario to demonstrate the implementation of the model, as well as the solution to the firefighting problem. The advantages of our model in emergency management domain are discussed and concluded in the last. Keywords: Decision theory; Fires; Information systems; Markov processes; Risk management; Decision problems; Dynamic approaches; Dynamic decision making; Dynamic decision problem; Effective management; Emergency decision makings; Emergency management; Markov Decision Processes; Decision making Track: Decision Support Systems http://idl.iscram.org/show.php?record=1134

Abstract: In this contribution, we introduce a prototype of a decision support tool for cost-optimal response in security management. The threat situation of a closed infrastructure, exposed to multiple threats, and the corresponding response actions are modeled by a continuous-time Markov decision process (CMDP). Since the CMDP cannot be solved exactly for large infrastructures, the response actions are determined from a heuristic, based on an index rule. The decision support tool's user interface displays the infrastructure's current threat state and proposes the heuristic response actions to the decision maker. In this way, global situation awareness can be enhanced and the decision maker is able to initiate an almost cost-optimal response action in short time.

Keywords: Continuous time systems; Costs; Decision making; Decision support systems; Industrial management; Information systems; Markov processes; User interfaces; Continuous-time markov decision process; Cost-optimal response; Decision support tools; Security management; Situation awareness; Optimization

Track: Poster Session

http://idl.iscram.org/show.php?record=580

Abstract: The dynamic nature of emergency decision making exerts difficulty to decision makers for achieving effective management. In this regard, we suggest a dynamic decision making model based on Markov decision process. Our model copes with the dynamic decision problems quantitatively and computationally, and has powerful expression ability to model the emergency decision problems. We use a wildfire scenario to demonstrate the implementation of the model, as well as the solution to the firefighting problem. The advantages of our model in emergency management domain are discussed and concluded in the last.