Multi-agent simulations are some of the most complex for a simulation modeler to optimize. Most optimization solutions are suited for a single agent or a small number of agents, but quickly lose their effectiveness as more agents are introduced. Simulations of more...
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...
When simulation models become the basis of decision making in operational settings, they often face time constraints. Machine scheduling in factories is one situation where equipment needs to decide quickly how to respond to its environment. Traditional simulation...
Companies using simulation to optimize their operations need to be able to account for variability in data. Businesses don’t operate in static environments, and their simulation models should be able to adapt accordingly. Even minor fluctuations can have a major...
There’s something eerie about deep reinforcement learning that sets it apart from other kinds of AI. Deep reinforcement learning is “goal-oriented” AI. That is, it learns which actions to perform in order to reach a goal. It was used in the early DeepMind demos that...
Every business wants to make smart decisions amid uncertainty, ward off risks, and seize opportunities. Every manager wants to make sure their operations hit performance goals while keeping costs down. Using reinforcement learning to make better operational decisions...
