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Random Keys Genetic Algorithms Scheduling And Rescheduling Systems For Common Production Systems, Elkin Rodriguez-Velasquez
Random Keys Genetic Algorithms Scheduling And Rescheduling Systems For Common Production Systems, Elkin Rodriguez-Velasquez
Engineering Management & Systems Engineering Theses & Dissertations
The majority of scheduling research deals with problems in specific production environments with specific objective functions. However, in many cases, more than one problem type and/or objective function exists, resulting in the need for a more generic and flexible system to generate schedules. Furthermore, most of the published scheduling research focuses on creating an optimal or near optimal initial schedule during the planning phase. However, after production processes start, circumstances like machine breakdowns, urgent jobs, and other unplanned events may render the schedule suboptimal, obsolete or even infeasible resulting in a "rescheduling" problem, which is typically also addressed for a …
Probabilistic Models For Patient Scheduling, Adel Alaeddini
Probabilistic Models For Patient Scheduling, Adel Alaeddini
Wayne State University Theses
In spite of the success of theoretical appointment scheduling methods, there have been significant failures in practice primarily due to the rapid increase in the number of no-shows and cancelations from the individuals in recent times. These disruptions not only cause inconvenience to the management but also has a significant impact on the revenue, cost and resource utilization. In this research, we develop a hybrid probabilistic model based on logistic regression and Bayesian inference to predict the probability of no-shows in real-time. We also develop two novel non-sequential and sequential optimization models which can effectively use no-show probabilities for scheduling …
Quality Of Service Based Data-Aware Scheduling, Archit Kulshrestha
Quality Of Service Based Data-Aware Scheduling, Archit Kulshrestha
LSU Doctoral Dissertations
Distributed supercomputers have been widely used for solving complex computational problems and modeling complex phenomena such as black holes, the environment, supply-chain economics, etc. In this work we analyze the use of these distributed supercomputers for time sensitive data-driven applications. We present the scheduling challenges involved in running deadline sensitive applications on shared distributed supercomputers running large parallel jobs and introduce a ``data-aware'' scheduling paradigm that overcomes these challenges by making use of Quality of Service classes for running applications on shared resources. We evaluate the new data-aware scheduling paradigm using an event-driven hurricane simulation framework which attempts to run …