Most optimization problems in industrial operations and supply chain take multiple goals, or objectives, into account. For example, a director of operations in a factory may seek to maximize throughput while minimizing costs and collisions. While simultaneously optimizing for those goals may be easy in simple environments, creating multi-objective heuristics for optimization in complex situations is hard, and sometimes impossible.
Pathmind’s AI is able to optimize along multiple dimensions automatically. Deep reinforcement learning is more robust than other optimization methods for multiple objectives. Reinforcement learning agents are able to encode nuanced responses to the vast array of situations that they may encounter in simulations that accurately emulate real conditions.
By optimizing for several metrics while responding to near-real complexity, reinforcement learning is able to offer decisions that make sense for actual business operations.
The Applied Intelligence team at Accenture in Argentina decided to expand AnyLogic’s product delivery simulation by modeling carbon emissions tied to various routes and vehicles. The goal was to optimize product delivery in order to maximize efficiency AND minimize emissions and delivery time.
Read the full case study here.
Businesses operating in complex environments need simulation optimization tools that will ensure they meet several goals at once. Pathmind reinforcement learning is the ideal solution for applications where multiple objectives need to be optimized to meet real-world demands.
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