diff --git a/docs/src/modules/ROOT/nav.adoc b/docs/src/modules/ROOT/nav.adoc index d6b3c01c62..4357f24700 100644 --- a/docs/src/modules/ROOT/nav.adoc +++ b/docs/src/modules/ROOT/nav.adoc @@ -1,5 +1,4 @@ * xref:introduction.adoc[leveloffset=+1] -* xref:planning-ai-concepts.adoc[leveloffset=+1] * Getting started ** xref:quickstart/overview.adoc[Overview] ** xref:quickstart/service/getting-started.adoc[Build as a service] diff --git a/docs/src/modules/ROOT/pages/.index.adoc b/docs/src/modules/ROOT/pages/.index.adoc index 6004a4c4d1..d53fcb5035 100644 --- a/docs/src/modules/ROOT/pages/.index.adoc +++ b/docs/src/modules/ROOT/pages/.index.adoc @@ -23,7 +23,6 @@ endif::[] include::introduction.adoc[leveloffset=+1] include::quickstart/.quickstart.adoc[leveloffset=+1] -include::planning-ai-concepts.adoc[leveloffset=+1] include::running-timefold-solver/overview.adoc[leveloffset=+1] include::running-timefold-solver/configuration.adoc[leveloffset=+1] include::running-timefold-solver/library/library-integration.adoc[leveloffset=+1] diff --git a/docs/src/modules/ROOT/pages/frequently-asked-questions.adoc b/docs/src/modules/ROOT/pages/frequently-asked-questions.adoc index 1dfa9a238a..0a0e6428ea 100644 --- a/docs/src/modules/ROOT/pages/frequently-asked-questions.adoc +++ b/docs/src/modules/ROOT/pages/frequently-asked-questions.adoc @@ -16,7 +16,7 @@ To find out more, see xref:commercial-editions/commercial-editions.adoc[Plus / E == Does Timefold offer pre-built models? -Timefold offers a suite of pre-built PlanningAI models designed to expedite development by addressing complex scheduling and routing challenges across various industries. +Timefold offers a suite of pre-built planning models designed to expedite development by addressing complex scheduling and routing challenges across various industries. These models are built upon _Timefold Solver Enterprise Edition_ technology and are accessible through a REST API, facilitating seamless integration into your applications. See all available models on https://app.timefold.ai/[our platform]. diff --git a/docs/src/modules/ROOT/pages/introduction.adoc b/docs/src/modules/ROOT/pages/introduction.adoc index 99d7316544..e248b2043c 100644 --- a/docs/src/modules/ROOT/pages/introduction.adoc +++ b/docs/src/modules/ROOT/pages/introduction.adoc @@ -1,5 +1,5 @@ [#introduction] -:page-aliases: ../index.adoc +:page-aliases: ../index.adoc, planning-ai-concepts.adoc :doctype: book :sectnums: :icons: font @@ -8,7 +8,7 @@ = Introduction Every organization faces planning problems: providing products or services with a limited set of _constrained_ resources (employees, assets, time, and money). -Timefold Solver’s xref:planning-ai-concepts.adoc[PlanningAI] optimizes these problems to do more business with fewer resources using Constraint Satisfaction Programming. +https://timefold.ai[Timefold Solver] is a lightweight, embeddable constraint satisfaction engine that optimizes these problems to do more business with fewer resources. [TIP] ==== @@ -18,22 +18,43 @@ For common planning problems, we also offer ready-made models that can be seamle Explore our documentation and available models https://docs.timefold.ai/[here] ==== -https://timefold.ai[Timefold Solver] is a lightweight, embeddable constraint satisfaction engine which optimizes planning problems. -Example usecases include: - .Timefold Solver's use cases include vehicle routing, employee scheduling, rostering, bin packing, and equipment scheduling. image::introduction/useCaseOverview.png[align="center"] -Timefold Solver is 100% pure Java^TM^ and runs on Java {java-version} or higher. -It xref:running-timefold-solver/library/integration.adoc#integration[integrates very easily] with other technologies. -Timefold Solver works on any Java Virtual Machine and is compatible with the major JVM languages and all major platforms. -It also supports Kotlin. +== Planning problems + +A planning problem arises from a need to achieve a *goal* by organizing available *resources* under a set of *constraints*: + +* Building a house, where workers and materials are the resources, and construction order and site hours are the constraints. +* Staffing a hospital shift, where qualified personnel are the resources, and working hour rules and required specializations are the constraints. +* Routing delivery vehicles, where vehicles and drivers are the resources, and time windows, capacity, and distance are the constraints. + +Planning problems grow combinatorially as resources and constraints are added. +Even a small field service problem with 4 vehicles and 8 visits produces over 19 million possible assignments for a brute-force algorithm to consider. +Timefold Solver evaluates this space efficiently, finding good solutions in a fraction of the time it would take to do manually. + +== Constraints + +Constraints can be classified as *hard*, *medium*, or *soft*: + +* **Hard constraints** are rules that must not be broken — for example, an employee can only be in one place at a time, or dependent tasks must complete in order. + Violating a hard constraint produces an infeasible plan. +* **Medium constraints** apply when resources are scarce — for instance, assign as many customer visits as possible when there aren't enough technicians to cover all of them. +* **Soft constraints** express business preferences — minimize travel time, assign employees to preferred shifts, and similar goals. + +Solutions are scored to reflect how well they satisfy these constraints. +For example: `0hard/-257medium/-6119520soft`. +A solution with zero hard constraint violations is always considered better than one that breaks even a single hard constraint, regardless of the soft score. + +== Technology + +Timefold Solver is 100% pure Java^TM^ and runs on Java {java-version} or higher, on any JVM platform. +It integrates natively with xref:running-timefold-solver/library/quarkus.adoc[Quarkus] and xref:running-timefold-solver/library/spring-boot.adoc[Spring Boot], and supports Kotlin. -You can use Timefold Solver to build an xref:quickstart/overview.adoc#runAsAService[optimization service], +You can use it to build an xref:quickstart/overview.adoc#runAsAService[optimization service], or embed it into your software as a xref:quickstart/overview.adoc#embedAsALibrary[library]. :!sectnums: == Next -* Follow the xref:quickstart/overview.adoc[Quickstart Example] to tackle your first planning problem. -* Learn about some important concepts used in the realm of xref:planning-ai-concepts.adoc[PlanningAI]. +* Follow the xref:quickstart/overview.adoc[Quickstart] to tackle your first planning problem. diff --git a/docs/src/modules/ROOT/pages/planning-ai-concepts.adoc b/docs/src/modules/ROOT/pages/planning-ai-concepts.adoc deleted file mode 100644 index 8af0ce994d..0000000000 --- a/docs/src/modules/ROOT/pages/planning-ai-concepts.adoc +++ /dev/null @@ -1,93 +0,0 @@ -= PlanningAI concepts -:doctype: book -:sectnums: -:icons: font - -== Planning - -The need to create plans generally arises from a desire to achieve a *goal*: - -* Build a house. -* Correctly staff a hospital shift. -* Complete work at all customer locations. - -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 *testconstraint*. - -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. - -[#feasiblePlans] -=== Feasible plans - -Any plan needs to consider all three elements, goals, resources, and constraints, in balance to be a feasible plan. -A plan that fails to account for all the elements of the problem is an infeasible plan. -For instance, if a hospital staff roster covers all shifts, but assigns employees back-to-back shifts with no breaks for sleep or life outside work, -it is not a valid plan. - -== Planning problems are hard to solve - -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 Solver in a fraction of the time. - -[#operationsResearch] -== Operations Research - -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. - -[#planningAI] -== Planning AI - -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. - -=== Why Planning AI is different -Traditional methods of planning often involve manually sifting through options or relying on basic tools that can’t keep up with the complexity of real-world problems. -Planning AI, on the other hand, uses advanced strategies to quickly focus on the most promising solutions, even when the situation is extremely complicated. -Planning AI also makes it possible to understand the final solution with a breakdown of which constraints have been violated and scores for individual constraints and an overall score. -This makes Planning AI incredibly valuable in industries where getting the right plan is crucial—whether that’s scheduling workers, routing deliveries, or managing resources in a factory. - -Planning AI is designed to be accessible, so you can start improving your planning process right away. - -[#aPlanningProblemHasConstraints] -== Constraints - -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.