Constrained black box optimization for radioisotope thermal generator manufacturing

This thesis aims to optimize the conditions and policies used at Los Alamos National Laboratory for the manufacturing of Radioisotope Thermal Generators used for deep space exploration. This manufacturing faces unique and stringent constraints on their operations as well as extraordinarily rigorous quality control measures to ensure that products will work when deployed. Furthermore, this manufacturing process is new, and no historical data exists to prove the capability of the manufacturing system and what the expected operating costs will be. Through this analysis, a theoretical model is constructed to understand the system dynamics to arrive at a theoretical product throughput. A base case of the manufacturing system is created using values for the system as it is currently envisioned. From this case, the total cost, average total time per product, and the number of products completed are optimized. This optimization is achieved by changing the policies on how batches are formed and when operators should work to use resources most efficiently and ensure that no resource is under or over-utilized. It was discovered that the most efficient policy is to add a half working day on Saturdays which significantly reduces the cost by about $700,000 when compared to having operators work 24/7. Furthermore, this policy can produce more products in less time than the case in which people work a standard shift Monday through Friday and decreases costs by about ~$30,000 utilizing the optimized values. Finally, using cost estimation techniques, the total manufacturing cost including fringe benefits, maintenance, operating supplies, and supervisory labor is estimated to be around $6,000 per product and total cost of$3M per year. The results presented in this thesis can inform Los Alamos National Laboratory on the direction and policies that must be implemented to meet manufacturing targets. Furthermore, the methodology developed can be expanded and applied to other product lines throughout the lab to analyze throughput and stay cost efficient while meeting national security requirements.Mechanical Engineerin

Scatter Search Applied to the Inference of a Development Gene Network

Efficient network inference is one of the challenges of current-day biology. Its application to the study of development has seen noteworthy success, yet a multicellular context, tissue growth, and cellular rearrangements impose additional computational costs and prohibit a wide application of current methods. Therefore, reducing computational cost and providing quick feedback at intermediate stages are desirable features for network inference. Here we propose a hybrid approach composed of two stages: exploration with scatter search and exploitation of intermediate solutions with low temperature simulated annealing. We test the approach on the well-understood process of early body plan development in flies, focusing on the gap gene network. We compare the hybrid approach to simulated annealing, a method of network inference with a proven track record. We find that scatter search performs well at exploring parameter space and that low temperature simulated annealing refines the intermediate results into excellent model fits. From this we conclude that for poorly-studied developmental systems, scatter search is a valuable tool for exploration and accelerates the elucidation of gene regulatory networks

Scatter search applied to the inference of a development gene network

Efficient network inference is one of the challenges of current-day biology. Its application to the study of development has seen noteworthy success, yet a multicellular context, tissue growth, and cellular rearrangements impose additional computational costs and prohibit a wide application of current methods. Therefore, reducing computational cost and providing quick feedback at intermediate stages are desirable features for network inference. Here we propose a hybrid approach composed of two stages: exploration with scatter search and exploitation of intermediate solutions with low temperature simulated annealing. We test the approach on the well-understood process of early body plan development in flies, focusing on the gap gene network. We compare the hybrid approach to simulated annealing, a method of network inference with a proven track record. We find that scatter search performs well at exploring parameter space and that low temperature simulated annealing refines the intermediate results into excellent model fits. From this we conclude that for poorly-studied developmental systems, scatter search is a valuable tool for exploration and accelerates the elucidation of gene regulatory networks.We thank Johannes Jaeger for critical feedback and scientific advice. We thankfully acknowledge the computer resources, technical expertise and assistance provided by the Barcelona Supercomputing Center, which is part of the Red Española de Supercomputación. We thank SURFsara (www.surfsara.nl) for the support in using the Lisa Compute Cluster. The Centre for Genomic Regulation (CRG) acknowledges support from the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013-2017’, SEV-2012-0208. AC kindly acknowledges Fondation Bettencourt Schueller