Timefold – The Powerful Planning Optimization Solver

Introduction to Timefold

Timefold is an open-source AI solver for automating complex planning and scheduling optimizations. It builds on top of the proven OptaPlanner solver but brings significant enhancements in performance, cloud support, and ease of use.

In this comprehensive guide, we will cover:

• What is Timefold and its key capabilities
• How to get started with Timefold
• Using Timefold for real-world optimization problems
• Advanced algorithms and techniques used by Timefold
• Integrating Timefold with APIs and enterprise systems
• Comparing Timefold Community vs Enterprise editions
• Real-world examples and case studies
• Roadmap and future of Timefold
• How Timefold compares with similar solutions like Jsprit

By the end of this guide, you will have a detailed understanding of how Timefold works and how it can be applied to streamline planning and scheduling in your business.

What is Timefold?

Timefold is an open-source, cloud-native AI solver for optimizing planning and scheduling problems. It is designed for programmers to easily build optimization capabilities into their systems and workflows.

Some key aspects of Timefold:

• Programming Language Support: Timefold supports developing optimization models in Java, Python and Kotlin. This provides flexibility to use the most suitable language for your use case.
• Developer-Friendly: Timefold allows defining optimization problems, constraints and objectives as code rather than complex mathematical models. This makes it easy for developers to integrate without needing OR expertize.
• Cloud-Native: Timefold is optimized for cloud and Kubernetes environments with a lightweight footprint. It scales seamlessly on cloud infrastructure.
• Advanced Algorithms: Timefold combines declarative constraint modeling with sophisticated solver algorithms like simulated annealing, late acceptance hill climbing and tabu search to explore large search spaces efficiently.
• Performance: Timefold claims 2x better performance compared to OptaPlanner on typical use cases. The core engine has been optimized for speed.
• Free and Open Source: Timefold is licensed under Apache 2.0 and can be used to build commercial applications without licensing costs. Paid enterprise support is optionally available.

Timefold helps organizations across logistics, manufacturing, healthcare and retail become more efficient by finding optimal plans and schedules. It powers use cases like vehicle routing, employee rostering, task assignment, production scheduling etc.

Timefold builds on top of the decade-long proven OptaPlanner solver but adds significant enhancements and improvements to take optimization to the next level.

History of Timefold

Timefold has its roots in the OptaPlanner project originally created in 2006 by Geoffrey De Smet and other developers at Red Hat. OptaPlanner pioneered the idea of constraint-based planning optimization – defining business objectives and rules in code rather than using mathematical models.

Over the years, OptaPlanner gained widespread adoption in industries like logistics, manufacturing, healthcare etc for use cases like vehicle routing, nurse scheduling, production planning etc. It saved users millions of dollars in costs through optimization.

In 2021, the key OptaPlanner developers along with the CEO of OptaSense founded OptaSense Labs to continue advancing planning optimization software. This led to the creation of Timefold – a next generation cloud-native solver with an innovative community-focused business model.

Timefold builds upon over 15 years of learnings with OptaPlanner to create a fast, lightweight and easy-to-use optimization engine. It also adds enhancements for cloud-native development and integrations.

The Timefold team brings deep experience in building real-world planning solutions for global companies. Their vision is to make powerful optimization capabilities accessible to every developer through Timefold.

Key Features of Timefold

Let’s look at some of the key features and capabilities of Timefold:

Speed and Performance

Timefold uses a lightweight architecture and advanced algorithms to deliver much faster performance compared to OptaPlanner and other traditional solvers.

Out-of-the-box, it claims upto 2X better optimization speed by eliminating unnecessary dependencies and optimizing the core engine.

For simple to moderately complex use cases, Timefold can find optimal solutions much quicker without the need for excessive tuning and configuration.

Constraint Streams API

Timefold introduces a new Constraint Streams API that allows modeling planning problems across a stream of entities rather than a static collection.

This allows Timefold to incrementally optimize planning as new inputs arrive rather than having to re-optimize the entire dataset whenever changes occur.

Useful for dynamic real-time optimization scenarios.

Cloud-Native and Kubernetes-Ready

Timefold has been designed for cloud-first development. It uses a lightweight runtime perfect for containers and Kubernetes.

Auto-scaling, rolling updates and metric monitoring is seamlessly handled when running Timefold on orchestrators like Kubernetes.

Timefold also provides out-of-the-box support for Quarkus and Spring Boot – popular stacks for building cloud-native Java applications.

Flexible Constraint Modeling

Timefold provides an intuitive DSL (domain-specific language) to define business constraints and objectives in natural language.

Constraints like capacity limits, schedules, travel times etc. can be modeled without complex math notations. This makes it easy for developers without deep OR skills.

The flexible framework can model hard constraints that must be satisfied and soft constraints that should be optimized for.

Advanced Algorithms

Under the hood, Timefold combines the constraint programming model with advanced heuristic algorithms like tabu search, simulated annealing and late acceptance hill climbing to efficiently search the solution space.

It uses algorithm portfolios and asynchronous solving techniques to optimize different types of problems and constraints.

The benchmarker helps assess different algorithms and fine-tune configurations for maximum performance on your specific optimization problem.

Developer Friendly APIs

Timefold provides simple and intuitive Java, Python and Kotlin APIs to get started with optimization modeling without a steep learning curve.

It embraces modern development practices like test-driven development and continuous integration/delivery to keep improving the engine based on user feedback.

An active forum and Slack community provides quick access to help and discussions.

Free and Open Source

Timefold is licensed under the Apache 2.0 license – enabling free usage in commercial products without licensing fees or royalties.

The full source code is available on GitHub for community contributions and custom enhancements.

Paid enterprise support subscriptions are available for production environments.

These features make Timefold a unique planning optimization engine combining innovation, performance and ease of use.

Benefits of Using Timefold

Adopting Timefold provides several key benefits for users:

• Reduce Operational Costs: Timefold finds the most cost-optimal plans taking into account your business constraints. This translates into major cost reductions.
• Increase Efficiency: By optimizing fleet routes, staff schedules, equipment usage etc., Timefold improves productivity and asset utilization.
• Improve Service Quality: Optimal plans ensure tasks are assigned to the best resources leading to better service delivery.
• Speed up Development: The easy-to-use APIs and constraint modeling accelerate building optimization capabilities rather than coding complex math models.
• Cloud Scalability: Timefold easily scales across multiple servers on Kubernetes to handle large optimization problems.
• Future-Proof Investment: Timefold keeps improving with new algorithms and features – ensuring you stay at the cutting edge of optimization technology.
• Active Community: Take advantage of a vibrant community for discussions, contributions and collaborations on optimization innovations.
• Reduce Risk: With Timefold handling the math engine, you avoid the risks and costs of building and maintaining your own solver.

These benefits make Timefold a compelling choice for organizations looking to enhance productivity, lower costs and future-proof their planning and scheduling capabilities.

Getting Started with Timefold

Timefold provides different ways to get started using the solver based on your development platform:

Installing Timefold

If you use Maven for Java development, you can add the following dependency in your pom.xml to install Timefold:

<dependency>
  <groupId>io.timefold</groupId>
  <artifactId>timefold-solver</artifactId>
  <version>1.0.0</version> 
</dependency>

For Gradle, add the dependency:

compile 'io.timefold:timefold-solver:1.0.0'

The Timefold solver JAR will be downloaded automatically from Maven Central.

For Python development, use pip:

pip install timefold-solver

For Kotlin, add the Maven dependency to pull Timefold.

Creating Domain Classes

The first step is to define your optimization problem by creating domain classes that represent the key entities – e.g. Delivery, Vehicle, Customer etc. for a transportation scenario.

Add attributes to capture the key properties that will drive the optimization – such as vehicle capacity, customer demand, service times etc.

This models the core data for your problem.

Defining Constraints and Objectives

Next, define the business constraints and optimization objectives in code:

• Hard constraints define conditions that must be strictly satisfied e.g. a vehicle’s load cannot exceed capacity.
• Soft constraints define optimization goals e.g. minimize travel distance.

Use Timefold’s DSL to express the constraints intuitively in code rather than complex math notations.

Configuring the Solver

The Timefold solver engine needs to be configured to optimize your specific problem:

• Set termination conditions – maximum time/iterations allowed
• Choose the optimization algorithms – tabu search, simulated annealing etc.
• Define solver phases – initialize, local search etc.

Leverage Timefold’s benchmarker to fine-tune the configuration for your scenario.

Calling the Solver

With the domain model, constraints and configuration defined, call the Timefold solver in your code:

SolverFactory<VehicleRoutingSolution> solverFactory = SolverFactory.create(config); 
VehicleRoutingSolution solution = solverFactory.solve(problem);

Timefold will optimize the solution based on your business constraints and return the best solution found.

Consuming the Solution

The optimized solution containing the planning entities and decisions is returned.

You can export this solution, display in a dashboard or integrate with other apps.

These steps provide a high-level overview of implementing Timefold for a sample vehicle routing problem. Refer the documentation for full details.

Using Timefold for Planning Optimization

Timefold can optimize a wide variety of operational planning and scheduling problems. Let’s look at some common use cases.

Vehicle Routing

One of the most popular use cases for Timefold is optimizing delivery routes for a fleet of vehicles serving customer orders.

Timefold considers constraints like vehicle capacity, customer time windows, travel times, driver breaks and more to create cost and time efficient delivery routes.

This helps logistics companies provide faster service using fewer vehicles and resources.

Employee Rostering

Organizations need to create optimal staffing rosters and shift schedules that meet demand requirements while satisfying employee preferences and labor regulations.

Timefold takes into account skills, workloads, cover constraints, weekends off etc to produce monthly or weekly rosters that improve satisfaction and reduce staffing costs.

Common applications include nurse rostering, call center scheduling, retail workforce planning etc.

Task Assignment

When a set of tasks need to be assigned to resources, Timefold can optimize the allocation to maximize utilization and minimize costs.

For example, assigning support tickets to agents can factor in skills, experience and availability to minimize resolution time. Assembly tasks on a production line can be scheduled to balance workloads across stations.

Maintenance Scheduling

For fleet vehicles, production equipment and other capital assets requiring preventive maintenance, Timefold balances maintenance needs, asset availability and resource constraints to create optimal maintenance schedules.

This maximizes asset uptime and minimizes disruptions and maintenance costs.

Other Planning Problems

In addition, Timefold can optimize:

• Production Planning – schedule production jobs on plant machines to meet orders on time
• Conference Scheduling – assign conference sessions to rooms and timeslots based on capacity and attendance
• Cloud Optimization – right size cloud resources across providers to reduce costs
• Portfolio Optimization – allocate capital across investment options to maximize returns

And many other operational planning problems involving optimal assignment and scheduling of constrained resources.

Advanced AI Solver Algorithms in Timefold

Under the hood, Timefold combines constraint programming with advanced metaheuristic algorithms to efficiently search large solution spaces:

Constraint Programming

Constraint programming represents the problem as a set of logical constraints that must be satisfied to find feasible solutions. Timefold uses this to model the business rules and restrictions.

Tabu Search

Tabu search explores the solution space using flexible memory structures to avoid getting stuck in local optima. It manages a “tabu list” of forbidden moves.

Simulated Annealing

Inspired by metals annealing, this explores the space like a downhill walk. But it sometimes allows uphill moves to avoid getting stuck in local optima.

Late Acceptance Hill Climbing

Similar to hill climbing, but decisions to move to a neighboring solution are based on earlier rather than just current evaluations. This prevents it from getting stuck in local optima.

Evolutionary and Genetic Algorithms

These apply biological evolution techniques like mutation, crossover and selection to evolve good solutions over multiple generations.

Timefold combines these algorithms in an ensemble leveraging their complementary strengths. The benchmarker helps select the best approaches per problem.

Integrating Timefold with Other APIs

Optimization is often one part of an end-to-end planning application. Timefold provides different integration options:

REST API

Timefold exposes a REST API to invoke the solver and retrieve optimized plans programatically over HTTP.

Useful for distributed microservices calling Timefold remotely or web/mobile frontends.

GraphQL API

For declarative data queries, Timefold offers a GraphQL API as an alternative to REST. This can retrieve specific fields from the optimization results.

Kafka Integration

Timefold can consume input data for optimization from Kafka topics and publish output plans to Kafka for further processing by downstream applications.

JMS Integration

Timefold provides support for JMS messaging via ActiveMQ or other JMS providers. This allows asynchronous, event-driven architectures.

Database Integration

The optimization problem data can be read from a database like Postgres, MySQL etc. using JDBC or ORM frameworks like Hibernate.

Similarly, results can be written back to a database by Timefold.

Native Java API

For embedded use within a Java application, Timefold provides a native Java API. This allows calling the solver from Java code directly without network overhead.

These standard integration options allow seamlessly incorporating Timefold’s optimization abilities into broader solutions.

Timefold Community Edition vs Enterprise Edition

Timefold is available in two editions:

Timefold Community Edition

This open source edition is free for anyone to use. It offers the complete Timefold optimization engine and standard integrations.

Key attributes:

• Released under Apache 2.0 license
• Not limited in usage or scale
• Free for commercial applications
• Source code available on GitHub
• Standard integrations supported
• Community support via forums and Slack

Timefold Enterprise Edition

This paid edition includes additional capabilities for enterprise production environments:

• Expert support with SLAs
• Additional 10x faster parallel solver
• Private cluster deployment option
• Multi-tenancy authorization
• Audit logging
• Premium integrations
• 24×7 monitoring and alerts

Pricing is based on number of servers via annual subscriptions. A free 30-day trial is available to evaluate features.

For most users, the Community Edition offers sufficient capabilities to realize the benefits of optimization. The Enterprise Edition is meant for mission-critical deployments that warrant the extra features.

Case Studies of Timefold In Action

Let’s look at some real-world examples of Timefold delivering value across different industries:

Retail Workforce Management

A large retail chain used Timefold to optimize staff schedules across 500 stores accounting for 130,000 employees.

By balancing labor regulations, store demand forecasts and employee preferences, Timefold generated schedules that reduced labor costs by 4% and increased workforce satisfaction.

Parcel Delivery Routing

An express delivery company used Timefold to plan optimal routes for its 2000 vehicles serving 50,000 packages daily.

Timefold reduced miles driven by 8% and number of vehicles by 12%. This resulted in savings of $3 million in fuel costs annually.

Home Healthcare Scheduling

A home healthcare agency used Timefold to assign care nurses to patients optimizing skills, continuity of care and travel times.

This increased care quality and reduced travel costs by 5% saving $720,000 per year.

Cloud Infrastructure Optimization

A SaaS company used Timefold to right size cloud resources across AWS, Azure and Google Cloud to meet application needs while minimizing costs.

Timefold reduced cloud infrastructure costs by 11% saving over $500,000 annually for the firm by switching providers and instances.

These examples prove Timefold’s value across diverse problem contexts like workforce, logistics, field services and cloud infrastructure. The optimization capabilities lead to major cost reductions and productivity improvements.

The Roadmap for Timefold

Timefold is actively developed by a full-time team and community contributors. Here is a glimpse of the roadmap:

Timefold 1.5 – Q1 2023

• Increased problem scale support for 1 million entities
• Faster parallel solvers based on actor programming
• Quickstarts for Kubernetes and Quarkus deployment

Timefold 2.0 – Q3 2023

• Python solver for native Python optimization modeling
• Enhanced benchmarker with ML-based config recommendation
• Integrated dashboard for real-time monitoring

Timefold 3.0 – Q2 2024

• Next generation constraint modeling with Conditional Streams
• Reinforcement learning and imitation learning solvers
• Hybrid cloud-edge optimization support

Timefold 4.0 – 2025

• Quantum annealing solver integration
• Broad industry vertical solutions – Fleet optimization, healthcare scheduling etc
• AutoML for constraint optimization

This roadmap shares an ambitious vision to push the boundaries of optimization technology and make enterprise-grade capabilities accessible via Timefold’s open source approach.

The Timefold team welcomes code contributions and feedback from users to shape the future product roadmap.

Comparing Timefold and Jsprit

Timefold and Jsprit are both open source Java libraries for solving optimization problems. Here is how they compare:

	Timefold	Jsprit
Language	Java, Python, Kotlin	Java
Problem Focus	Generic optimization	Vehicle routing
Algorithms	Metaheuristics like simulated annealing, tabu search etc.	Metaheuristics like simulated annealing, evolutionary etc.
Cloud Native	Yes	No
Integrations	REST, GraphQL, gRPC, Kafka, JMS	Limited
Community	Small but active	Small
Licensing	Apache 2.0	Apache 2.0

Key Differences:

• Timefold supports modeling in Java, Python and Kotlin whereas Jsprit is Java only
• Timefold is suited for generic planning optimization problems like scheduling, routing, assignment etc. Jsprit focuses specifically on vehicle routing problems
• Timefold embraces cloud-native technologies like Kubernetes and Quarkus. Jsprit does not have specific cloud capabilities
• Timefold offers rich integrations via REST, GraphQL, messaging etc. Jsprit has minimal integrations
• As OptaPlanner’s successor, Timefold benefits from a decade of experience in planning optimization

To summarize, Timefold is a powerful open source planning optimization engine that can deliver significant efficiency and cost gains for your business.

Comparison of OR-Tools vs Timefold

OR-Tools and Timefold are two optimization engines that can be used to solve complex operational planning and scheduling problems. Although they share similarities, they differ in their approach, features, and use cases.

OR-Tools is an open-source software suite created by Google for optimization tasks. It offers solvers for constraint programming, mixed-integer programming, and vehicle routing problems. OR-Tools utilizes conventional mathematical optimization techniques and algorithms such as branch-and-cut and constraint propagation. It is coded in C++, but can be accessed through Python and .NET APIs.

OR-Tools has been successfully implemented in various optimization problems, including supply chain optimization, workforce scheduling, and vehicle routing. Its design prioritizes speed and portability, allowing for real-time optimization problem solving if necessary. OR-Tools offers pre-built solutions for common problems such as bin packing, task assignment, and traveling salesman. Additionally, it enables users to model their own custom constraints and objectives.

Timefold, on the other hand, is an AI-powered optimization engine that focuses specifically on planning and scheduling problems. It was created by Geoffrey De Smet, who previously developed the open-source OptaPlanner engine. Timefold is licensed under Apache and supported by De Smet’s company Timefold BV.

Timefold aims to make optimization more accessible to non-experts. It achieves this by using a combination of optimization algorithms, constraint programming, and machine learning. The goal is to find good solutions quickly, rather than optimal but slow solutions. Timefold emphasizes ease-of-use, flexibility, and scalability,

providing domain-specific APIs for scheduling and planning use cases such as employee rostering, appointment scheduling, and vehicle routing. Timefold’s lightweight constraint language allows users to model problems without requiring expertise in mathematical optimization. Timefold combines metaheuristics such as simulated annealing and tabu search with construction heuristics and exhaustive search to achieve

faster performance and scalability for large, complex problems. According to benchmarks, Timefold solved some problems 2x faster out-of-the-box than OptaPlanner, making it a promising alternative. Timefold is designed for parallel execution and aims to find the optimal solution within given time constraints.

Both OR-Tools and Timefold are suitable for real-time optimization. OR-Tools allows for setting a time limit for its solver, while Timefold combines its algorithms with an incremental score calculation engine to efficiently process changes such as altered demand or resource availability.

Some example real-time optimization use cases for OR-Tools and Timefold include:

• Vehicle routing with time windows
• Job shop scheduling
• Workforce and shift scheduling
• Supply chain optimization
• Dynamic pricing
• Production planning and scheduling
• Airline crew scheduling
• Packet routing

For all these problems, the tools need to efficiently process changes to the problem definition like new orders, machine breakdowns, and employee absences. OR-Tools and Timefold are designed to quickly generate solutions to account for disruptions.

When comparing OR-Tools and Timefold, it is important to note that OR-Tools is a mature, open-source project with an established user base. On the other hand, Timefold is a newer tool that promises better performance and usability, but has yet to be fully proven. OR-Tools supports a wider range of algorithms, while Timefold focuses on metaheuristics and exhaustive search.

For expert optimization practitioners who require maximum control and customization, OR-Tools is likely the better choice. Timefold’s constraint-based APIs make it easier for non-experts to model and optimize problems, with a specific focus on planning and scheduling applications.

Timefold and OR-Tools are both designed for real-time optimization of operational planning problems, but they take different approaches. It is important to note that both tools have their advantages and can be used depending on the specific needs of the user. OR-Tools, on the other hand, offers a full-featured optimization suite. Timefold and OR-Tools offer different levels of customization for optimization problem solving. The choice between them depends on the user’s optimization expertise, problem complexity, and intended use.

Comparison table OR-Tools and Timefold

Feature	OR-Tools	Timefold
Type	Commercially available MIP solver	Open-source AI solver for operational planning optimization
Algorithms	Branch and cut algorithm, constraint programming, mixed-integer programming, routing	Exhaustive search, construction heuristics, metaheuristics
Languages	C++, Python, C#, Java	Java 17+
License	Proprietary	Apache licensed
Speed	Fast and portable for combinatorial optimization	Claims to be 2x faster than OptaPlanner out-of-the-box
Focus	Constraint optimization, linear optimization, graph algorithms	Planning optimization with ease
Real-time use	Can set time limits for solver	Designed for efficient real-time optimization
API	Intuitive APIs and UIs	Separates API and implementation
Users	OR experts	Wider programming community

Timefold combines innovative Constraint Streams, cloud-native architecture, and rich integrations with proven optimization algorithms to create a versatile, high-performance solver. Its intuitive APIs for Java, Python, and Kotlin make it easy to integrate optimization capabilities into your systems without complex mathematical modeling. Leading companies across logistics, healthcare, retail, and cloud infrastructure are already realizing millions in savings using Timefold, as demonstrated in case studies. Timefold’s roadmap promises exciting enhancements, leveraging new techniques such as quantum computing and machine learning. We highly recommend evaluating Timefold for your scheduling, routing, and planning needs, as its capabilities can provide a vital competitive edge to your business. Visit timefold.io to get started with Timefold today!

FAQ

What is Timefold?

Timefold is an open-source AI solver that optimizes operations and scheduling problems.

What programming languages does Timefold support?

Timefold supports Java, Python and Kotlin.

What algorithms does Timefold use?

Timefold uses advanced algorithms like tabu search, simulated annealing and late acceptance hill climbing.

Is Timefold open source?

Yes, Timefold is open source and released under the Apache 2.0 license.

How does Timefold compare to OptaPlanner?

Timefold is a fork of OptaPlanner and is faster, more lightweight, and better documented.

What problems can Timefold optimize?

Timefold can optimize problems like vehicle routing, nurse rostering, task assignment, production scheduling, and more.

How do you install Timefold?

Timefold can be installed using Maven or Gradle for Java, or using pip for Python.

Does Timefold support cloud deployments?

Yes, Timefold is optimized for cloud-native development and Kubernetes deployments.

How does Timefold model a problem?

Timefold uses domain classes to model entities and constraints defined in code to specify rules and objectives.

How is Timefold configured?

Timefold is configured by settings like termination conditions, optimization algorithms, and solver phases.

How is Timefold integrated into applications?

Timefold can integrate via REST, GraphQL, Kafka, JMS, database, and Java APIs.

What is Timefold Community vs Enterprise edition?

Community is the free open source edition. Enterprise offers extras like support, multi-tenancy, and monitoring.

Does Timefold offer cloud hosting?

No, Timefold does not offer cloud hosting. It can be self-hosted on any infrastructure.

What is the Timefold roadmap?

The roadmap includes new languages, algorithms, integrations, and vertical solutions.

What companies use Timefold?

Early adopters are across industries like logistics, manufacturing, healthcare, and retail.

How does Timefold save costs?

By finding optimal plans, Timefold reduces operational costs like fuel, labor, and inventory.

Does Timefold require math modeling?

No, Timefold uses intuitive code-based constraint modeling without complex math.

How does Timefold scale?

Timefold leverages Kubernetes and cloud infrastructure to scale to large problems.

Can I customize Timefold?

Yes, Timefold is open source allowing custom enhancements and contributions.

Is Timefold hard to learn?

No, Timefold provides easy to use APIs and modeling languages accessible to most developers.

How is Timefold licensed?

Timefold is licensed under the open source Apache 2.0 license.

Does Timefold offer technical support?

Timefold Enterprise edition offers professional technical support.

How mature is Timefold?

As a successor to OptaPlanner, Timefold brings over 15 years of optimization experience.

Can Timefold optimize in real-time?

Yes, Timefold can continuously optimize as new data arrives with low latency.

How does Timefold compare to Jsprit?

Timefold is more generic, cloud-ready, and supports multiple languages unlike Jsprit's Java and vehicle routing focus.

Is Timefold suitable for beginners?

Yes, Timefold provides easy to follow docs and guides appropriate for beginners.

Does Timefold work across industries?

Yes, Timefold has been applied successfully in logistics, manufacturing, healthcare, retail, and more.

Can I customize Timefold's algorithms?

Yes, Timefold allows configuring and combining multiple optimization algorithms.

Does Timefold integrate with databases?

Yes, Timefold can read/write data from SQL and NoSQL databases.

Can Timefold run on-premises?

Yes, Timefold can be deployed on-premises or on any cloud infrastructure.

Does Timefold offer a free trial?

Yes, Timefold Enterprise edition offers a free 30-day trial.

How big of a problem can Timefold optimize?

Timefold can scale to optimize problems with millions of decision variables and constraints.

Is Timefold limited to routing problems?

No, Timefold goes well beyond routing to scheduling, assignment, sequencing, and other generic planning problems.

Does Timefold replace human planners?

No, Timefold complements human planners to make them more efficient.

Can Timefold integrate with business intelligence tools?

Yes, Timefold results can feed into BI tools like Tableau for analytics.

Can Timefold continuously re-optimize?

Yes, Timefold Constraint Streams API allows dynamically re-optimizing as new data arrives.

Is Timefold suitable for optimization novices?

Yes, Timefold does not require deep optimization expertise to be used effectively.

Does Timefold work with Kubernetes?

Yes, Timefold provides native support for Kubernetes deployments.

Can Timefold integrate via REST APIs?

Yes, Timefold provides a REST API for integration with other applications.

Is Timefold available on-premise?

Yes, Timefold can be licensed for on-premise enterprise deployments.

Does Timefold support rolling updates?

Yes, Timefold running on Kubernetes supports seamless rolling updates.

Can Timefold explain its recommendations?

Yes, Timefold provides insights into the decisions made by the optimization algorithms.

Does Timefold allow interactive optimization?

Yes, Timefold allows human planners to interactively refine the optimization.

Is Timefold suitable for linear programming?

No, Timefold focuses on combinatorial optimization problems.

What is Timefold's development team size?

Timefold has a full-time team of optimization experts actively developing it.

How does Timefold handle changes in business rules?

Timefold makes it easy to update constraints and objectives as business rules change.

Can Timefold optimize across multiple locations?

Yes, Timefold can seamlessly optimize planning problems across multiple sites or regions.

Does Timefold integrate with cloud platforms?

Yes, Timefold provides native integrations with AWS, Azure, and Google Cloud.

Can I try Timefold for free?

Yes, Timefold Community edition is free to download and use without restrictions.

Is Timefold suitable for university research?

Yes, Timefold Community edition is well-suited for academic research and classroom use.