Academic articles

In this work, the researchers undertake a root-cause enabling Vendor Managed Inventory performance measurement approach to assign responsibilities for poor performance. Additionally, the work proposes a solution methodology based on reinforcement learning for determining optimal replenishment policy in a VMI setting. Using a simulation model as a training environment, different demand scenarios are generated based on real data from Infineon Technologies AG and compared based on key performance indicators.

Especially in complex manufacturing systems and uncertain conditions, sequencing operations in a machines’ queue can pose a difficult problem. To solve the problem, intelligent, adaptable, and autonomous systems have been developed using machine learning and simulation to sequence operations under uncertainty within large manufacturing systems.

Operations and maintenance (O&M) expenses can vary greatly from one energy solution to another. While a solar farm or geothermal system may need minimal ongoing maintenance, wind turbines require a skilled crew to keep them operating efficiently.

In this research, the authors use a scaled-down wind farm case study to demonstrate the potential of Reinforcement Learning (RL) in identifying an optimal O&M policy and to show the ease of use of AnyLogic simulation software and Pathmind reinforcement learning tool.

There has been an increased interest in resilient supplier selection in recent years, much of it focusing on forecasting the disruption probabilities. The results of this study advance our understanding about how and when machine learning and simulation can be combined to create digital supply chain twins, and through these twins improve resilience. The proposed data-driven decision-making model for resilient supplier selection can be further exploited for design of risk mitigation strategies in supply chain disruption management models, redesigning the supplier base or investing in most important and risky suppliers.

This paper presents preliminary analysis of the Panama Canal Expansion from the viewpoint of salinity in the Gatun Lake and the utilization of neural networks. This analysis utilized simulation modeling and artificial intelligence.