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Pharmaceutical Scheduling Using Simulated Annealing And Steepest Descent Method, Bryant Jamison Spencer
Pharmaceutical Scheduling Using Simulated Annealing And Steepest Descent Method, Bryant Jamison Spencer
Graduate Theses, Dissertations, and Problem Reports
In the pharmaceutical manufacturing world, a deadline could be the difference between losing a multimillion-dollar contract or extending it. This, among many other reasons, is why good scheduling methods are vital. This problem report addresses Flexible Flowshop (FF) scheduling using Simulated Annealing (SA) in conjunction with the Steepest Descent heuristic (SD).
FF is a generalized version of the flowshop problem, where each product goes through S number of stages, where each stage has M number of machines. As opposed to a normal flowshop problem, all ‘jobs’ do not have to flow in the same sequence from stage to stage. The …
Trade-Off Balancing For Stable And Sustainable Operating Room Scheduling, Amin Abedini
Trade-Off Balancing For Stable And Sustainable Operating Room Scheduling, Amin Abedini
Theses and Dissertations--Mechanical Engineering
The implementation of the mandatory alternative payment model (APM) guarantees savings for Medicare regardless of participant hospitals ability for reducing spending that shifts the cost minimization burden from insurers onto the hospital administrators. Surgical interventions account for more than 30% and 40% of hospitals total cost and total revenue, respectively, with a cost structure consisting of nearly 56% direct cost, thus, large cost reduction is possible through efficient operation management. However, optimizing operating rooms (ORs) schedules is extraordinarily challenging due to the complexities involved in the process. We present new algorithms and managerial guidelines to address the problem of OR …
Sky Surveys Scheduling Using Reinforcement Learning, Andres Felipe Alba Hernandez
Sky Surveys Scheduling Using Reinforcement Learning, Andres Felipe Alba Hernandez
Graduate Research Theses & Dissertations
Modern cosmic sky surveys (e.g., CMB S4, DES, LSST) collect a complex diversity of astronomical objects. Each of class of objects presents different requirements for observation time and sensitivity. For determining the best sequence of exposures for mapping the sky systematically, conventional scheduling methods do not optimize the use of survey time and resources. Dynamic sky survey scheduling is an NP-hard problem that has been therefore treated primarily with heuristic methods. We present an alternative scheduling method based on reinforcement learning (RL) that aims to optimize the use of telescope resources for scheduling sky surveys.
We present an exploration of …