Next-generation supercomputers containing millions of processors will require weather prediction models to be designed and developed by scientists and software experts to ensure portability and efficiency on increasingly diverse HPC systems. Intel, Cray, PGI, and NVIDIA who were responsible for fixing bugs and providing access to the latest hardware and compilers. Thanks also to the staff at ORNL and TACC for providing system resources and helping to resolve system issues. This work was also supported in part by the Disaster Relief Appropriations Act of 2013 and the NOAA HPCC program.
In this paper, we address a lot splitting and scheduling problem existent in a textile factory. The factory we study produces a set of products that are made of, or assembled from, a list of components. During production, each component can be split into one or several lots of different sizes and each lot will be produced independently on one of a group of identical parallel machines. We formulate the problem into a mixed integer programming model and develop a heuristic method to solve the model. The heuristic method is based
In this paper we address a lot splitting and scheduling problem of a Textile factory that produces garment pieces. Each garment piece is made of a set of components that are produced on the knitting section of the company. The problem consists of finding a weekly production plan for the knitting section, establishing the quantities to produce of each component (organized in one or several lots), and where and when (starting/completion times) to produce them. The main contribution of this work is the development of a constructive heuristic that generates automated knitting scheduling plans. The heuristic produces solutions very fast for a set of randomly generated instances based on real world data.
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