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.

Featured Scheduling Optimization Tools

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 ...

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 ...

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

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 ...

Automate Asset Inspection

Mosaic designed and deployed custom computer vision models to automate asset recognition & inform inspection decisions.

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 ...

Automated Cooking Prediction & Optimizer | Deep RL

Mosaic built an automated cooking prediction & optimizer using deep reinforcement learning to improve short term cooking operations.

Automated CPG Sales Force Route Forecasting

Data Science, specifically machine learning forecasting techniques, enables decision makers across a spectrum of industries to set more effective sales targets and plan profitable customer routes that boost top and ...

Forecasting Hotel Room Demand

Mosaic was engaged by a leading hotel chain to assess the best way to predict future demand for hotel rooms across their various properties.

Predicting Patient Volumes

We helped one of the largest Children’s Healthcare Network more accurately forecast patient volume across their hospitals.

Dispatch Routing Optimization

In this whitepaper, we examine how different companies can attack the dispatch routing optimization problem using ML & AI.

Data Science for Manufacturing

Manufacturing companies have a large opportunity to apply data science techniques like AI & ML to optimize their business processes.

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