Abstract: Techniques for constraint programming-based periodic task scheduling are disclosed, including: determining a set of tasks to be scheduled across a set of shared resources, the set of tasks including multiple periodic tasks; filtering out one or more high-utilization tasks from the set of tasks to be scheduled; generating a constraint programming (CP) model based on the set of tasks, the CP model including a set of constrained variables, a set of constraints, and a search directive; applying a CP solver to the CP model, to obtain a CP solution for scheduling the set of tasks across the set of shared resources; where the CP solution assigns two or more of the periodic tasks to a same resource in the set of shared resources, based at least on the two or more periodic tasks having periods that are harmonically compatible.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Using constraint programming (CP) to obtain a machine maintenance schedule is described. A data model generator generates a CP data model, including a set of task elements representing a set of maintenance tasks, and a set of time elements representing a set of time windows. The CP data model further includes a constraint that requires each task element to be assigned a time window from a respective domain, such that each time element is assigned a task count from a respective domain. A search directive generator generates a CP search directive, including applying different time window prioritization methods to maintenance tasks for machines having different failure probabilities. One prioritization method may prioritize time windows based on delinquency costs. Another prioritization method may prioritize time windows based on a current maintenance schedule. A CP solver determines a proposed maintenance schedule based on the CP data model and the CP search directive.

Abstract: Using constraint programming (CP) to obtain a machine maintenance schedule is described. A data model generator generates a CP data model, including a set of task elements representing a set of maintenance tasks, and a set of time elements representing a set of time windows. The CP data model further includes a constraint that requires each task element to be assigned a time window from a respective domain, such that each time element is assigned a task count from a respective domain. A search directive generator generates a CP search directive, including applying different time window prioritization methods to maintenance tasks for machines having different failure probabilities. One prioritization method may prioritize time windows based on delinquency costs. Another prioritization method may prioritize time windows based on a current maintenance schedule. A CP solver determines a proposed maintenance schedule based on the CP data model and the CP search directive.