Sharif Digital Repository

In the last few decades due to technological progress and more attention to cost reduction of production, production systems have become more automated than before. By increasing the role of machines in manufacturing systems maintenance systems have become more important. One of the factors that influence the complexity of maintenance problem in real world is the interdependency of components in a system. Interacting cooperation between these components intensifies the complexity of the problem’s modeling as well as its optimizing. This complexity result in few studies that have been performed in this field. Thus these works can be broken into two main subjects. In the first case a system... 

The emergence of online social networks (OSN) has encouraged scientists to investigate them more deeply due to availability of excessive data and increased computational power. Diffusion is a fundamental process taking place in such networks. The spread of news, ideas, influence and diseases are examples of diffusion in social networks. The importance of information diffusion in different disciplines such as economics, politics and social behavior has motivated us to study diffusion networks in this thesis. Diffusion’s behavior is strongly influenced by the underlying network in social networks. Despite this fact most research done in this area ignores the dynamics of the underlying network.... 

This thesis proposes a probabilistic modeling framework for networked infrastructure systems in community resilience analysis. In particular, the framework is presented for the electric power infrastructure. Models are proposed at two levels of refinement. The basic model only determines the connectivity of different elements in the network, whereas the refined model also quantifies the amount of the power transmitted through the network elements. In a community resilience analysis, the proposed basic model provides a rather simple tool to determine those buildings and infrastructure systems that suffer from power outage when the supplying power station loses connectivity with the network.... 

This research develops a probabilistic framework for evaluating community resilience subject to earthquake events by modeling the water distribution system and its prevailing inter- and intra-dependencies with other infrastructure systems of the community. This framework is entitled “Rtx,” which is under continuous development at the Center for Infrastructure Sustainability and Resilience Research (INSURER) at Sharif University of Technology, Tehran, Iran. It quantifies the community resilience by integrating hazard models, risk models, and recovery models within an agent-based simulation. Comprehensive modeling of the water distribution system requires a library of probabilistic models,... 

This thesis proposes a probabilistic framework to increase situational awareness about serviceability of infrastructure systems, and extend and distribution of losses in the posthazard state of communities. Maintaining situational awareness is the first and foremost step in prioritizing search and rescue operations and organizing resources. This is a challenging task due to the uncertainties that exist in infrastructure systems’ performance after occurrence of a hazardous event. These uncertainties gradually diminish as further information is received. Therefore, a probabilistic framework is the necessary element for disaster management. This framework must be able to update initial beliefs... 

This research proposes a probabilistic framework for decision support in emergency response. Currently, decision-making is traditionally based on data from reconnaissance teams, while there are other information sources that assist decision-making. These sources of information include social sensors, physical sensors, aerial imaging, and crowdsourcing. The information obtained from these sources is heterogeneous and uncertain, requires a probabilistic data fusion framework to be used in decision making. This research employs Bayesian Networks for this purpose. The outstanding feature of the Bayesian Network is its real-time nature, which set apart it from other methods. This feature makes...