Mosaic Data Science’s optimization services leverage the latest advancements in artificial intelligence with traditional optimization techniques, such as mathematical optimization and scheduling optimization, to drive unparalleled efficiency and performance. Our expert team helps customers fine-tune processes, resources, and strategies through data-driven insights and predictive modeling. From supply chain streamlining to resource allocation, we harness the power of math to enhance decision-making and achieve optimal outcomes.
Using AI to analyze sensor data, Mosaic helped a renewable energy producer optimize furnace temperature to improve wood pellet production efficiency by reducing slag production and pressure buildup and providing control room operators with easy-to-use recommendations for setting furnace controls.
Mosaic applied our robust data analytics expertise across disciplines to develop novel, data-driven models that identify anomalous behavior for a leading aerospace government agency.
Wielding the power of computer vision human action extraction to gain insights from video data presents an opportunity for logistics companies to make improvements for new and current customers.
Mosaic helped a leading hospital optimize the scheduling of elective surgery and created a better daily rhythm for surgeons.
For the past several years, ML has exploded in popularity, while the excitement for MO has mostly plateaued. Why this has occurred is very much up for debate. One might surmise that ML is simply a better tool than MO, and therefore it replaced it in terms of popularity. This, however, is wrong-headed. ML and MO are typically used to solve very different problems. One might also think that problems MO has historically solved no longer exist.
Decision processes in support of jobs that either cannot be or are very difficult to automate are frequently overlooked by out of the box software providers. One such process is the creation of optimal staffing plans for outbound teams loading cartons onto trucks.
Mosaic built an automated cooking prediction & optimizer using deep reinforcement learning to improve short term cooking operations.
In this whitepaper, we examine how different companies can attack the dispatch routing optimization problem using ML & AI.