Adam has deployed reinforcement learning trained agents that optimize a production planning problem at Dow, Inc and deliver measurable business impact. Inspired by the successes of AlphaGO, this ambitious project is named AlphaDow. The production scheduling problem at Dow is non-trivial. Dow’s continuous manufacturing plants transition through a product sequence which must minimize the costs of transitions and maximize product availability for customers all whilst minimizing inventory on hand. Human schedulers use experience and heuristics to select the best sequence today. The trained RL agents augment this decision-making process. Adam has used Ray extensively to distribute his training among Azure based compute clusters. This significantly decreased the time needed to train an RL agent capable of producing optimal production schedules. Adam will present several of the challenges faced when training and deploying RL agents for industrial production scheduling and how Ray has helped him to overcome those challenges by facilitating the scale required to solve these complex problems and achieve real-world business impact.
Adam Kelloway works as an Innovation Manager for Dow's Digital Fulfillment Center. (DFC) The DFC is leading the development and deployment of AI/ML technologies within Dow's integrated supply chain. Adam has previously worked on optimized production scheduling using Mixed Integer Programming (MIP) approaches as well as projects using ML approaches to predict customer beheaviour.
Adam has presented his previous works at the AIChE annual conference as well as Aspen Tech's Optmize conference.
Adam graduated with a PhD in Chemical Engineering from the University of Minnesota where his thesis focused the optimization of process systems through MIP techniques. He completed his undergraduate masters at Imperial College in London.