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Found 2 results

  1. I am building a model where I need to use relational data tables to create objects and elements due to the large size of the model. I found helpful resources to guide me when it comes to auto-creating objects and elements (Simio Help Guide, SimBits, and Examples). But for some reasons I couldn't find resources that address directly the topic of process logic design in data-driven models. There is one particular example that's very relevant to my case. This example is MultiEchelonSupplyChain that comes with Simio (in Simio: Support > Examples > MultiEchelonSupplyChain). This example is solved using two approaches. The second one (the data-driven approach) is the one I'm interested in. In the accompanying PDF file that explains the technical details of the example, it does not explain the differences in process logic design. For example, how do I define triggering events? Some of the processes in this example have no triggering events. If you know of a resource that explains designing processes for data-driven models, please share. Thanks.
  2. Simio does not have a specific machine learning feature, but many people still implement a form of machine learning in one of two ways: 1) Data-driven models, and particularly data-generated models, will improve their operation with better data. As you system runs, and the data improves, the model will automatically improve. 2) Particularly in scheduling applications - a model is never static. Every time your run a model and discover the possibility of creating a better solution (better schedule) you manually improve the built-in heuristics to it generates a better solution the next time.
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