Optimizing schedules is hard. Custom algorithms that leverage ML and Mathematical Optimization can help make it easy.

Blended ML & Optimization Should Solve Scheduling Problems

Staffing and scheduling optimization are crucial for many industries, significantly when the exact timing of high-volume activity can change based on complex factors. Augmenting the workforce and assets too early means diverting profit to unnecessary costs while waiting until high-volume demand is already underway, risking operating below capacity and decreasing customer satisfaction (e.g., if there are long wait times).

Machine learning can be deployed to forecast operational demand and predict event duration. Mathematical optimization can augment these ML inputs to recommend a schedule that optimizes against your unique scheduling objectives. 

Mosaic Data Science has deep expertise in developing custom algorithms to solve highly complex scheduling problems.

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.

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. 

Surgeon OR Scheduling Optimization

Mosaic helped a leading hospital optimize the scheduling of elective surgery and created a better daily rhythm for surgeons.

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.

Workforce Planning Prediction

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.

Scheduling Problems Persist Despite Advancements in ML

Scheduling problems, in their most basic form, are:

  • An assignment problem, assigning resources to entities that require them,
  • A schedule is a sequence, an assignment problem that involves order,
  • A schedule almost always has a start time and duration.

From a data scientist’s standpoint, it would be nice to capture all dimensions in a model (people, rooms, resources, asset features, etc.) As you start to imagine all the features that go into a schedule, finding an optimal time allocation becomes quite a challenge.

Scheduling problems remain some of the most difficult problems to solve. Because scheduling can be complicated, schedulers often rely heavily on simple rules or over-generalized, out-of-the-box software to create feasible schedules. However, this doesn’t need to be the case. Scheduling problems are an excellent candidate for custom-built algorithms that use machine learning and mathematical optimization to find significantly better schedules at clicking a button.

Mosaic built a custom AI scheduling tool to optimize surgery scheduling for a hospital system.

Explore Some of Our Scheduling Optimization Success Stories & Curated Thought Leadership

Explore Mosaic’s Efficient Engagement Process

Download our Scheduling Optimization Tools Sheet

Ready to see how Mosaic can design and deploy ROI-driven Scheduling Optimization Tools?