Looking to compare Timefold against OptaPlanner and other top solvers? This detailed feature analysis will help you evaluate the options
Planning optimization is key to efficiently run operations in various domains like logistics, manufacturing, healthcare and more. Solving optimization problems like vehicle routing, nurse rostering, task assignment etc can lead to significant cost savings and better operational efficiency for any organization. This is where planning optimization solvers like Timefold and OptaPlanner come in.
Timefold and OptaPlanner are open source AI solvers for automating planning optimization. They take in your business data, rules and constraints and find the optimal plan or schedule to meet your business goals.
Both solvers have their roots in constraint satisfaction problems and use advanced algorithms like tabu search, simulated annealing and late acceptance hill climbing to explore the search space and find the best solution.
While OptaPlanner has been around for over a decade, Timefold is a newer optimization engine - a fork of OptaPlanner created by the original OptaPlanner team at Red Hat. It builds on top of the capabilities of OptaPlanner while improving performance and offering new features.
Let's take a detailed look at how the two solvers compare across various parameters:
Timefold claims to be twice as fast as OptaPlanner out of the box. The core Timefold solver is very lightweight and has fewer dependencies than OptaPlanner.
As per tests done by the Timefold team, Timefold 1.0.0 delivers upto 15% better performance compared to OptaPlanner for problems modeled using Constraint Streams.
The OptaPlanner documentation however notes that performance can be significantly improved by tweaking the solver configuration. It provides an inbuilt benchmarking tool to identify the best configuration for your specific problem.
So while Timefold seems faster out of the box, OptaPlanner offers ways to customize and tune the engine for your use case. For simpler problems, Timefold may have an edge.
Timefold supports all the core capabilities of OptaPlanner - modeling planning problems, defining constraints, configuring the solver, and generating optimal solutions.
It also brings some additional enhancements:
Focused on cloud-native development - lightweight footprint, integrates well with Kubernetes, Quarkus etc.
Supports latest versions of Java, Spring Boot, Quarkus, Jakarta EE - ensures compatibility with modern tech stacks.
Improved documentation - more extensive docs to help new users get started.
Constraint Streams API - optimize across a stream of planning entities rather than a collection.
Benchmarker - helps evaluate and compare different solver configurations.
So in terms of features, Timefold builds on top of the OptaPlanner foundation and adds capabilities like cloud-native support, Constraint Streams and Benchmarker.
Ease of Use
Both OptaPlanner and Timefold are designed to make optimization solvers accessible to a wider programming community, beyond just OR experts.
They provide intuitive APIs and UIs to model your planning problem, constraints and business goals without getting into mathematical optimization. Constraints and objectives can be coded in natural language for expressiveness.
Out of the two, Timefold offers more comprehensive documentation and guides for new users. OptaPlanner documentation, while extensive, can be still complex for beginners.
Timefold's quickstarts and tutorials gently introduce key concepts like constraints, score calculation etc. Making it easier for new developers to get started.
Community and Support
OptaPlanner has a much bigger community backing it, given its decade-long presence. Red Hat actively maintains and supports it.
It also has a larger collection of examples and integrations spanning different industries. showing how to apply OptaPlanner to common planning problems.
Timefold is relatively new and its community is still evolving. However, it is actively developed by the original OptaPlanner team and they bring their extensive experience in building optimization solvers.
Quality support is available through Timefold's forum and Slack community. Though not as big as OptaPlanner's user base, timely responses can be expected.
Licensing and Pricing
A key advantage of both OptaPlanner and Timefold is that they are available under the Apache License 2.0. This allows them to be used in commercial applications without any licensing fees.
OptaPlanner offers a cloud-hosted solution OptaPlanner Online which provides a managed, highly scalable deployment of the solver. This comes under a paid subscription model.
Timefold currently does not offer a cloud-hosted solution. But the core solver can be freely used on-premise or hosted on a cloud infrastructure of your choice.
For commercial support and features, OptaPlanner has more options currently. Timefold provides a community edition that is freely usable.
Case Studies and Adoption
OptaPlanner has seen broader adoption across industries like:
- Logistics: Route optimization engines for UPS, DHL
- Manufacturing: Production scheduling systems for manufacturers like Renault, Airbus
- Healthcare: Hospital bed scheduling, surgery scheduling
- Retail: Workforce scheduling solutions
- Transportation: Airline crew scheduling, cargo loading optimization
Timefold is relatively new and user stories are still emerging. Some early adopters span industries like medical systems, engineering services, energy sector, retail etc.
But given the core team's experience, Timefold has potential for extensive real-world usage like OptaPlanner. Initial signs are promising.
Summary and Recommendation
|Performance||Very fast out-of-the-box||Requires tuning for best performance|
|Features||Latest Java, Quarkus, Spring Boot support. Benchmarker, Constraint Streams.||More examples and integrations|
|Ease of Use||Better docs for new users||Steeper learning curve|
|Community||Small but active||Large user community|
|Licensing||Free under Apache 2.0||extra features under paid subscription|
|Adoption||Early adopters across industries||Broad adoption in logistics, manufacturing etc.|
Both Timefold and OptaPlanner are great choices for integrating optimization capabilities into your systems and workflows.
Timefold brings significant improvements in speed, documentation and cloud-native support. It makes an excellent choice if you use modern Java stacks and want quick ramp-up.
OptaPlanner has the benefit of maturity - proven across many real-world use cases, extensive community support and advanced features like OptaPlanner Cloud. Great if you need enterprise-scale production capabilities.
Evaluate both solutions against your specific needs in terms of problem complexity, team skills, and scalability needs. This will help decide the right optimization solver for your requirements.
Real-World Use Cases
To better understand how these solvers work, let's look at some real-world examples and use cases where OptaPlanner and Timefold drive optimization.
OptaPlanner Use Cases
Vehicle Routing for Food Delivery Company
A food delivery company uses OptaPlanner to optimize the routes for its fleet of delivery vehicles across a city. The solver considers factors like restaurant locations, order volumes, vehicle capacity, traffic patterns and more to create routes that minimize total distance travelled and delivery times. This provides faster deliveries using fewer vehicles.
Nurse Rostering for Healthcare Organization
A health system uses OptaPlanner to assign nurses to shifts at its hospitals and clinics. The solver takes into account nurse skills, shift preferences, staffing requirements, patient demand and other constraints to create weekly schedules. This improves nurse satisfaction while ensuring adequate cover for high-quality patient care.
Task Assignment for Call Center
A call center uses OptaPlanner to assign customer service agents to incoming support tickets. It factors in agent skills, experience, workload and availability to route tickets to the right agents. This provides quick resolution of issues and improves customer satisfaction.
Workforce Scheduling for Retail Store
A retail chain uses OptaPlanner to create staff schedules across hundreds of store locations. The solver optimizes assignments based on store sizes, staff skills, individual preferences, labor regulations and expected store traffic. The result is optimized labor cost and service levels across the store network.
Timefold Use Cases
Delivery Route Optimization for Logistic Company
A logistics company uses Timefold to plan optimal delivery routes across multiple cities. The solver considers factors like drop-off deadlines, vehicle capacity, road network, driver breaks and real-time traffic to create efficient routes. This reduces kilometers driven, cuts fuel usage and meets service commitments.
Job Shop Scheduling for Manufacturing Plant
A manufacturing unit uses Timefold to schedule production jobs across different machines. It sequences the jobs to maximize machine utilization, minimize changeovers and meet production targets. This improves plant throughput and on-time delivery performance.
Conference Scheduling for Event Organizer
An event management firm uses Timefold to schedule talks and workshops across multiple tracks and rooms at a conference venue. The solver assigns sessions based on speaker preferences, room capacities, attendee demand and back-to-back constraints. This creates an engaging conference program within venue and time limits.
Cloud Cost Optimization for SaaS Company
A SaaS company uses Timefold to optimize their spend across cloud infrastructure providers like AWS, Azure and GCP. It takes into account compute needs, data storage, network costs, discounts and more to get the best price-performance mix. This reduces overall cloud expenses.
These examples showcase the diversity of problems that Timefold and OptaPlanner can help solve. Whether it's vehicles, nurses, retailers or cloud servers, optimizing planning and scheduling unlocks significant productivity and cost advantages.
Integrating Timefold and OptaPlanner
Let's look at how Timefold and OptaPlanner can be integrated into business applications and workflows:
The planning problem is defined in terms of decision variables, constraints and optimization objectives. For a delivery company, these could be routes, drop-offs, vehicle capacity, distance minimization etc.
Key business data like orders, nurses, stores etc. is modeled as planning entities and fed into the solver's data structures.
Constraints and goals are coded in Java or Python based on natural language business rules. No need for math modeling.
The solver is configured e.g. local search type, termination conditions. Benchmarker tools help pick optimal settings.
The optimized plan or schedule is generated by the solver based on configured termination criteria.
The plan can be exported and integrated with other apps e.g. route maps sent to driver mobile devices.
User interfaces allow tweaking constraints and objectives to continuously improve plans.
APIs and cloud services allow embedding solver capabilities into web/mobile apps.
In essence, the solvers encapsulate complex optimization algorithms behind simple APIs and constraint modeling interfaces. This makes it easy to bake in optimization intelligence with minimal coding.
The Future of Planning Optimization
Optimization solvers like Timefold and OptaPlanner are becoming fundamental components of modern digital platforms and AI solutions.
Here are some promising directions for this technology:
Hybrid cloud-edge deployment: Combining centralized cloud optimization with localized edge solvers close to assets and users
More industries: Expanding beyond core logistics and manufacturing into emerging areas like energy, digital advertising etc.
Integrated end-to-end solutions: Embedding optimization into full-stack solutions for supply chain, healthcare, transportation etc. rather than just providing point solver capabilities.
AI engine integration: Complementing solver algorithms with large AI models that learn patterns from industry data.
Quantum computing: Leveraging quantum algorithms in solvers for complex problems not tractable with classical computing.
As problems get more complex and global, purpose-built optimization solutions will play an even more critical role in building efficient businesses and societies.
Planning optimization solvers like Timefold and OptaPlanner provide immense value by finding the best schedules, routes, resource allocations etc. for complex operational problems.
Timefold brings speed and next-gen capabilities as a successor to the proven OptaPlanner solver. Both are great choices based on your specific needs and constraints.
The future is bright for this technology to power smarter decisions and drive continuous efficiency improvements across industries.