Track: Machine Learning
Deep Reinforcement Training and Machine Learning Applications for Industry 4.0: Optimization of Field Service Management and Manufacturing Operations
Tuesday, April 13, 3:35-4:15pm EDT
Making the right decision now by considering all possible implications in the future is not an easy task. Digital Twins are useful support tools for exploring the potential impact of findings from a systemic perspective. They leverage simulation modeling techniques and usually rely on heuristics to replicate the behavior and logic of how systems evolve. But when it comes to searching for an optimal solution, especially when the goal is many decision steps in the future, there is a need to explore the solution space. Without an automated approach, this can be nearly impossible; mathematical optimization techniques can be compelling, but they are extremely difficult to implement when dealing with long-term decision-making and an environment rich with uncertainty. In this space, Deep Reinforcement Learning (RL/DRL) is gaining attention. Why? Because it can deliver a policy for sequential decision-making for even the most complex, non-linear environment. The two real cases presented aim to highlight the benefits that a DRL policy can bring with respect to established heuristics. In the first case, we explore the application of DRL for identifying an optimal Operations & Maintenance strategy for a wind farm equipped with Prognostics & Health Management capabilities. In the second case, we explore how the application of DRL methodologies enables a Food & Beverage distributor to make smarter decisions about production order sequencing, reducing processing time by 16%.