Tuesday, August 23
4:00 PM - 4:30 PM
Production schedule design at Dow is a complex process that involves designing production cycles for multiple production units subject to manufacturing and scheduling constraints. Formulating this problem as a single large mixed integer linear program (MILP) optimization is computationally expensive. To solve this complex problem in reasonable time, we introduce a multi-agent decomposition approach that splits the problem into two separate hierarchical agents. In this talk, we will show how Ray has helped our team significantly reduce the solution time needed to solve this complex problem, leading to significant value for our end users, who can now solve and compare problem scenarios much more quickly, in turn leading to faster decision making for the overall business. Finally, we will highlight how our solution method, leveraging Ray, can be generalized and applied to a wide range of use cases in the MILP space.
Adam Kelloway wears many hats (data science, ML engineering, and data engineering) for Dow's Digital Fulfillment Center as he shapes, grows, and matures Dow's AI/ML/data strategies. When he's building models, those are focused on Dow's supply chain operations. Recent highlights include leveraging Ray Core and Ray Serve to distribute and deploy a hybrid simulation/mixed integer model for production planning and continued work to productionize RL agents for production scheduling. Adam has a Master of Engineering (MEng) degree from Imperial College London and a PhD in chemical engineering from the University of Minnesota.
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