Oracle International Corporation

Gao Chen

Claire M Bagley

Michael Patrick Colena

Joyce Ng

Martin P Plotkin

Joshua Deen Griffin

David J Kulik

Resource allocation limitations include resource limits and resource guarantees. A consumer is vulnerable to interruption by other consumers if using more resources than guaranteed. Resources are designated and/or assigned consumers based on resource limits and resource guarantees. A constraint programming (CP) solver determines resource limits and resource guarantees that minimize vulnerability and/or vulnerability cost based on resource usage data. A CP data model includes limit elements, guarantee elements, and vulnerability elements. The CP data model further includes guarantee-vulnerability constraints, which relies on exceedance distributions generated from resource usage data for the consumers. The CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

Using constraint programming to set resource allocation limitations for allocating resources to consumers

Information about a set of maintenance tasks and time windows includes a cost value per task per time window. Based on the information, a data model generator generates a data model, including task elements; time elements; cost elements; a total cost element; a constraint that requires each task element be assigned a time window from a respective domain, such that each time element is assigned a task count from a respective domain; a constraint that requires each cost element be assigned a cost value associated with a time window assigned (or to be assigned) to a task element corresponding to the cost element; and a constraint that requires the total cost element be assigned a total cost value that is a sum of the cost values assigned (or to be assigned) to the cost elements. Based on the data model, a CP solver determines a proposed maintenance schedule.

Using constraint programming to obtain a machine maintenance schedule for maintenance tasks

Resource allocation limitations include resource limits and resource guarantees. A consumer is vulnerable to interruption by other consumers if using more resources than guaranteed. Resources are designated and/or assigned to consumers based on resource limits and resource guarantees. A constraint programming (CP) solver determines resource limits and resource guarantees that minimize vulnerability and/or vulnerability cost based on resource usage data. A CP data model includes limit elements, guarantee elements, and vulnerability elements. The CP data model further includes guarantee-vulnerability constraints, which relies on exceedance distributions generated from resource usage data for the consumers. The CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

A product configurator comprises a constraint network having a plurality of nodes. The configurator allows a modeler to designate that one of more nodes of a configuration are input-required nodes. During an interactive session, the configurator may receive a request for an auto-completion of the configuration. The configurator will determine if an input has been received for the input-required nodes, and prohibit the auto-completion if the input has not been received.

Interactive product configurator with automatic selections

A solver solves constraint satisfaction problems. The solver receives a user decision and propagates the user decision over a constraint satisfaction problem network. The solver then determines if there is a conflict on the network based on the propagation. If there is a conflict, the solver allows a user to resolve the conflict interactively by either canceling the user decision or dropping one or more previously made user decisions.

Constraint satisfaction problem solver with interactive conflict resolution

A constraint based product configurator determines a structure that is adapted to be associated with one or more components and determines if an arbitrary association is possible for the structure. If an arbitrary association is possible, the configurator associates a component with the structure and generates a component association choice for the association. The component association choice is an identity of the structure and the corresponding first component.

Interactive product configurator with persistent component association

A constraint solver for solving a constraint satisfaction problem model which includes at least one part-whole hierarchy assigns variables to each part of the part-whole hierarchy. The variables include at least one of selected, quantity, and relative quantity, wherein each part has a parent. The solver automatically applies at least one part-whole hierarchy constraint to the part and solves the model and generates a solution.

Dynamic constraint satisfaction problem solver with part-whole hierarchy constraints

A system for solving a dynamic constraint satisfaction problem comprises a constraint network of variables and constraints. The system creates a first sub-problem model that includes a first model type, one or more first variables and zero or more first constraints. The system propagates the first constraints through the constraint network and determines if a first conflict is detected from propagating the first constraints. If the first conflict is detected, the system restores the constraint network variables to a first previous state before the first constraints were propagated. The system creates a first sub-problem set that includes a second model type and one or more sub-problem models. The system connects the first sub-problem model to the first sub-problem set via a second constraint and propagates the second constraint through the constraint network.

Dynamic constraint satisfaction problem solver

A product configurator includes a constraint based network that includes a plurality of nodes. The configurator receives one or more user selections and generates one or more automatic selections to form a complete configuration. The configurator receives a request to change the configuration after forming the complete configuration, and retracts the automatic selections. For each of the retracted automatic selections, the configurator determines if the retracted automatic selection is a configuration-defining selection and reasserts the configuration-defining selections.

Interactive product configurator that allows modification to automated selections

A constraint solver for solving a constraint satisfaction problem network that comprises a plurality of nodes and a plurality of constraints. The solver receives a request to remove an inferred problem association and determines one or more user decisions that triggered the inferred problem association.

Dynamic constraint satisfaction problem solver with inferred problem association removal

A solver for a constraint satisfaction problem includes a plurality of variables and a plurality of constraints. A floating point variable has a domain and is assigned a value by first determining if a predetermined value can be assigned to the floating point variable if the predetermined value is in the domain. If not, the solver determines if a bound of the domain can be assigned to the floating point variable. If the predetermined value can not be assigned to the floating point variable and the bound of the domain can not be assigned to the floating point variable, the solver assigns a value to the floating point variable using domain splitting.

Constraint based system with domain splitting

A constraint solver solves a constraint satisfaction problem ('CSP') that includes a network of variables and constraints. The solver receives a sub-expression and determines if the sub-expression is an internal constraint of the CSP. The internal constraint has a supporting operator expression. If the sub-expression is an internal constraint, the solver determines any or all top constraint expressions associated with the supporting operator expression.

Constraint based system that identifies top constraint expressions

A dynamic constraint solver solves a constraint satisfaction problem that includes a problem having zero or more ports, and zero or more sub-problems coupled to the zero or more ports. The solver stores a cross problem constraint template for cross problem constraints. The solver receives a request to connect a first sub-problem to a first port of a first problem. The solver then determines if the first sub-problem forms a participating problem combination for the cross problem constraint template. The participating problem combination includes all participating problems of the cross problem constraint. The solver then generates the cross problem constraint for the participating problem combination if the first sub-problem forms the participating problem combination.

Dynamic constraint solver with cross problem constraints

A dynamic constraint solver system for solving a constraint satisfaction problem model includes a plurality of ports. The system defines a hierarchical union that includes all problems in a lower port that is in a problem under another port in the model. The system generates a constraint that computes a cardinality of the hierarchical union and determines an included set and an excluded set for the hierarchical union. The system then propagates the included set and excluded set to participating ports of the hierarchical union.

Dynamic constraint satisfaction problem solver with hierarchical union constraints

A solver solves a dynamic constraint satisfaction problem that is modeled as a constraint network of variables and constraints. The solver generates a dynamic sub-problem facility that includes one or more sub-problem pools that each comprises a sub-problem type, one or more sub-problems for the sub-problem type, and one or more clients for the sub-problem type. The solver generates a sub-problem set domain that comprises zero or more included sub-problems and zero or more excluded sub-problems. The solver synchronizes the sub-problem set domain with the dynamic sub-problem facility.

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Gao Chen