Planning AI concepts

The need to create plans generally arises from a desire to achieve a goal:

Achieving those goals involves organizing the available resources. To correctly staff a hospital you need enough qualified personnel in a variety of fields and specializations to cover the opening hours of the hospital.

Any plan to deploy resources, whether to staff a hospital shift or to assemble the building materials for a new house, is done under constraints.

Constraints could be laws of the universe; people can’t work two shifts in two separate locations at the same time, and you can’t mount a roof on a house that doesn’t exist. Constraints can also be relevant legislation; employees need a certain number of hours between shifts or are only allowed to work a maximum number of hours per week. Employee preferences can also be considered constraints, such as, certain employees prefer to work specific shift patterns.

Planning problems become harder to solve as the number of resources and constraints increase. Creating an employee shift schedule for a small team of four employees is fairly straightforward. However, if each employee performs a specific function within the business and those functions need to be performed in a specific order, changes that affect one employee quickly cascade and affect everybody on the team. If parts are delivered late and prevent one employee from completing their tasks, subsequent work will also be delayed.

As more employees and different work specializations are added, things become much more complicated.

For a trivial field service routing problem with 4 vehicles and 8 visits, the number of possibilities that a brute algorithm considers is 19,958,418.

What would take a team of planners many hours to schedule can be automatically scheduled by Timefold in a fraction of the time.

Operations Research (OR) is a field of research that is focused on finding optimal (or near optimal) solutions to problems with techniques that improve decision-making.

Constraint satisfaction programming is part of Operations Research that aims to satisfy all the constraints of a problem.

Planning AI is a type of artificial intelligence designed specifically to handle complex planning and scheduling tasks, and to satisfy the constraints of planning problems. Instead of just automating simple, repetitive tasks, it helps you make better decisions by sorting through countless possibilities to find the best solutions—saving you time, reducing costs, and improving efficiency.

Constraints can be considered hard, medium, or soft.

Hard constraints represent rules and limitations of the real world that any planning solution has to respect. For instance, there are only 24 hours in a day and people can only be in one place at a time. Hard constraints also include rules that must be adhered to, for instance, employee contracts and the order in which dependent tasks are completed.

Breaking hard constraints would result in infeasible plans.

Medium constraints help manage plans when resources are limited, (for instance, when there aren’t enough technicians to complete all the customer visits or there aren’t enough employees to work all the available shifts). Medium constraints incentivize Timefold Platform to assign as many entities (visits or shifts) as possible.

Soft constraints help optimize plans based on the business goals, for instance, minimize travel time between customer visits or assign employees to their preferred shifts.

To help determine the quality of the solution, plans are assigned a score with values for hard, medium, and soft constraints.

"0hard/-257medium/-6119520soft"

From this example score we can see zero hard constraints were broken, while both the medium and soft scores have negative values (the scores do not show how many constraints were broken, but values associated with those constraints).

Because breaking hard constraints would result in an infeasible solution, a solution that breaks zero hard constraints and has a soft constraint score of -1,000,000 is better than a solution that breaks one hard constraint and has a soft constraint score of 0.

The weight of constraints can be tweaked to adjust their impact on the solution.

Timefold Platform is Timefold’s managed SaaS that’s built on top of Timefold’s open source Solver technology.

The Platform provides easy access to Timefold’s models through the REST API to integrate our solver technology and models with your apps. Timefold Platform is a fully managed service, removing the need to manage infrastructure yourself. It comes with scalability, performance, and reliability benefits. The platform gives problem owners the right tools and insights to further optimize their planning solutions.

Timefold Platform can also be self-hosted. Please get in touch to discuss your requirements.

Learn more about Timefold Platform.

Plans need to be fluid and changeable as real-world events (people calling in sick or a last minute change of requirements) demand changes to our plans. With Timefold you can replan an existing schedule with minimal disruption to the original plan. Timefold’s recommendation engine can recommend which technicians can be sent to a customer visit and which employee can be assigned to a shift.

Timefold Platform’s planning models are designed for specific use cases:

Each model has a predefined model schema which follows the OpenAPI specification, that you use to define your problem dataset. The problem dataset is submitted to Timefold Platform through the API for solving.

The models include constraints that help optimize the solution.