Local Search for Satisfiability: Algorithms and Applications
نویسندگان
چکیده
This report presents a proposal for my PhD studies. First, we motivate the problem of Satisfiability and its importance in computer science and artificial intelligence. We then describe a critical review of relevant literature in this field, including state-of-the-art techniques. We identify some challenges in efficiently solving SAT by local search and its application to real world, including local search for crafted SAT instances and application SAT instances, as well as local search for partial MaxSAT instances. We describe the research plan and methodologies to address these issues. We also present our preliminary works we contributed in this direction. Zahra Shahabi Kargar 30th August 11am N53 0.62 Title: Intelligent Elective Surgery Scheduling Abstract: This study presents a novel approach for solving real world scheduling problems in dynamic and uncertain environments such as hospital operating rooms (OR). The uncertainty associated with different activities, along with conflicting priorities and preferences of the stakeholders, adds additional layers of complexity to the OR scheduling problem, making this one of the most challenging real world scheduling domains. Although, robust scheduling and stochastic scheduling have been shown as two powerful approach in dealing with uncertainty in real world scheduling problems, both of these approaches are highly dependent on the historic data which determines the probability distribution of stochastic variable and predictive information about the future events. Rapid advancements in statistical machine learning techniques provide powerful tools for precise prediction of future events, offering a new stream for tackling uncertainty in dynamic environment through the integration of machine learning and optimization techniques. This idea has been applied with limited successful for addressing uncertainty, but there is significant scope for improvement in current state of the art in this domain. This study presents a novel approach for solving real world scheduling problems in dynamic and uncertain environments such as hospital operating rooms (OR). The uncertainty associated with different activities, along with conflicting priorities and preferences of the stakeholders, adds additional layers of complexity to the OR scheduling problem, making this one of the most challenging real world scheduling domains. Although, robust scheduling and stochastic scheduling have been shown as two powerful approach in dealing with uncertainty in real world scheduling problems, both of these approaches are highly dependent on the historic data which determines the probability distribution of stochastic variable and predictive information about the future events. Rapid advancements in statistical machine learning techniques provide powerful tools for precise prediction of future events, offering a new stream for tackling uncertainty in dynamic environment through the integration of machine learning and optimization techniques. This idea has been applied with limited successful for addressing uncertainty, but there is significant scope for improvement in current state of the art in this domain. In this study we propose an intelligent two stage methodology that integrates predicted information and historical utilization data with optimization techniques to improve current state of the art of real world dynamic scheduling problems. The proposed framework will employ a novel integration of machine learning and stochastic programming to address the underlying uncertainty. We use hospitals’ operating room as an instance of dynamic and uncertain environment and test bed for our proposed approach.
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تاریخ انتشار 2013