ABSTRACT
Decision Support Systems (DSS) have emerged as important tools for enhancing community resilience due to their ability to provide timely and efficient solutions to disaster-related problems while reflecting the perspectives of different stakeholders and utilizing multiple data sources. This paper provides a comprehensive summary of DSS applications to community resilience, emphasizing how the different modeling techniques are used in different disaster phases. We found that optimization techniques are the most frequently used methods for building DSS. Furthermore, we found that DSS tend to focus more on the preparedness and response phases of disaster management, rather than the recovery and mitigation phases. Moreover, the study highlights the main challenges in developing and implementing DSS for resilience, such as data availability, the uncertainty of the disaster context, and the need for cross-disciplinary collaboration. Based on the reviewed papers, we provide some guidelines to practitioners to select the most suitable decision-support tools for the needs of their community. The study aims to help decision-makers and researchers build effective decision support systems for enhancing community resilience, considering the current challenges.
ABSTRACT
The fourth industrial revolution has brought several risks to factories along with its plethora of benefits. The convergence of new technologies, legacy technologies, information technologies and operational technologies in the same network generates a wide attack surface. At the same time, factories need continuous production to meet their customers' demand, so any stopped production can have harsh effects on a factory's economy. This makes cyber resilience a key requirement in factories nowadays. However, it is difficult for managers to define effective cyber resilience strategies, especially considering the difficulty of estimating adequate investment in cyber resilience policies before the company has suffered cyber incidents. In this sense, the purpose of this article is to define and model an effective cyber resilience strategy. To achieve this, the system dynamics methodology was followed in order to get five experts' opinions on the best strategy to invest in cyber resilience. Interviews were conducted with these experts; their reasoning was put into behavior over time graphs and a system dynamics model was built from these findings. The main conclusion is that a cyber resilience investment strategy should be dynamic, investing in both technical security and personnel training, but at first with an emphasis on technical security and later shifting to have an emphasis on training.
