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.
Mosaic Data Science’s expert team specializes in crafting intelligent algorithms that streamline complex scheduling challenges across industries. From workforce management to production planning, we help customers harness cutting-edge optimization techniques to create schedules that maximize productivity and minimize costs. Whether it’s balancing workloads, minimizing delays, or optimizing routes, we tailor solutions to your specific needs.
This blog discusses the decision process for adjusting flight schedules in response to weather events using advanced analytics.
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.
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.
Optimizing seasonal staffing and resourcing is a key challenge for many industries, especially when the exact timing of high-volume activity can change based on complex factors.