Blogs
Why Airlines Need to Look at Holistic Machine Learning & Optimization Solutions to Improve Scheduling
This blog discusses the decision process for adjusting flight schedules in response to weather events using advanced analytics.
Blogs
Making Time for Scheduling Optimization: Q&A With Evan Lynch, Principal Data Scientist
We scheduled some time for this Q&A with Principal Data Scientist Evan Lynch, where we explored the many cross-industry challenges that scheduling optimization aims to solve, and the robust scheduling techniques that lie at the junction between mathematical optimization and machine learning.
White Papers
Deploying ML & Optimization In Risk Assessment Analytics for Robustness and Resilience
We explore how ML & optimization can aid the risk assessment and management process with an eye towards robustness and resiliency.
White Papers
Grid Inspection & Repair Scheduling Optimization
This white paper explores how we can integrate third-party weather data and utility-specific asset prioritization requirements to build more relevant optimization tools for scheduling the inspection and potential repair of grid anomalies.
Case Studies
Surgeon OR Scheduling Optimization
Mosaic helped a leading hospital optimize the scheduling of elective surgery and created a better daily rhythm for surgeons.
White Papers
Integrated Machine Learning and Mathematical Optimization
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.
Case Studies
Automate Asset Inspection
Mosaic designed and deployed custom computer vision models to automate asset recognition & inform inspection decisions.
White Papers
Loading Dock Staffing Optimization
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.
Case Studies
Automated Cooking Prediction & Optimizer | Deep RL
Mosaic built an automated cooking prediction & optimizer using deep reinforcement learning to improve short term cooking operations.
Blogs
Combat Supply Chain Disruptions with Data Science
Global external shocks are going to continue to happen, that is a fact of operating a business in today’s environment. As companies embrace data science in their decision-making processes, they are better positioned to deal with these disruptions, allowing them to manage a risk-optimized supply chain.