Systems and methods for multi-resource scheduling
Systems and methods for multi-resource scheduling are disclosed and described. An example apparatus includes a scheduler engine configured to enable clinical system(s) to operate with the scheduler engine in an analytical mode and an operating mode. When in the analytical mode, the scheduler engine is to dynamically calculate one or more binding constraints on the one or more clinical systems for scheduling. When in the operating mode, the scheduler engine is to manage and output a schedule for the one or more clinical systems based on the one or more binding constraints calculated in the analytical mode. The example scheduler engine is to dynamically switch between the analytical mode and the operating mode based at least in part on a probabilistic determination of delay associated with the schedule.
1. A multi-resource scheduling apparatus comprising:
a processor to implement a scheduler engine in conjunction with one or more clinical systems, wherein the scheduler engine comprises at least programming instructions which, in response to execution by the processor, cause the processor to:
enable the one or more clinical systems to operate with the scheduler engine in an analytical mode and an operating mode, wherein, when in the analytical mode:
the scheduler engine dynamically calculates one or more binding constraints on the one or more clinical systems for scheduling, wherein the calculation of the one or more binding constraints is based on one or more event inputs received from the one or more clinical systems, the event inputs comprising at least a sequence of one or more tasks and sub-tasks, and associated resource locations for the one or more tasks and sub-tasks;
when in the operating mode:
the scheduler engine configures and transmits a schedule to one or more scheduler interfaces of the one or more clinical systems, wherein the schedule is based on the one or more binding constraints calculated in the analytical mode, and wherein the scheduler engine monitors, via a computer network, execution of the schedule by the one or more clinical systems, wherein the monitoring comprises tracking the resource locations during execution of the sequence of the one or more tasks and sub-tasks;
wherein the scheduler engine dynamically switches between the analytical mode and the operating mode based at least in part on a probabilistic determination of delay associated with the schedule, wherein the probabilistic determination of delay is associated with a schedule risk and the scheduler engine continually calculates the probabilistic determination of delay based on the monitoring, the scheduler engine triggered to switch from the operating mode to the analytical mode when the scheduler engine determines, based on the probabilistic determination of delay and the associated schedule risk, that at least one of the one or more binding constraints is not satisfied and to switch from the analytical mode to the operating mode when the scheduler engine has recalculated the one or more binding constraints, and
wherein the scheduler engine transmits a reconfigured schedule to the one or more clinical systems to provide load balancing and dynamic schedule adjustment to the one or more clinical systems.
2. The apparatus of claim 1 , wherein the schedule risk is associated with a probability density function of time for a duration estimation of the one or more tasks in the schedule.
3. The apparatus of claim 2 , wherein the schedule risk is to be determined based on a simulation of task durations and logic for interdependencies between resources.
4. The apparatus of claim 2 , wherein the schedule risk is to be determined based on measures of duration, availability, and reliability for the schedule.
5. The apparatus of claim 1 , wherein the scheduler engine is to dynamically switch between the analytical mode and the operating mode based at least in part on a change in location of resources for adjacent tasks such that an estimation of transport time relative to task duration decreases a probability of task completion below an adjustable level.
6. The apparatus of claim 1 , wherein, when in the analytical mode, the scheduler engine is to calculate slack values for relaxing the binding constraints.
7. The apparatus of claim 1 , wherein, when in the analytical mode, the scheduler engine is to perform at least one of a what-was, what-is, what-if, or forecast analysis with respect to the one or more clinical systems for throughput and resource utilization.
8. The apparatus of claim 1 , wherein the scheduler engine is to determine the schedule based on at least one of a constraint satisfaction problem or heuristics to determine resource availability for the schedule.
9. A non-transitory computer-readable storage medium comprising a set of instructions, which, in response to execution by a processor, cause the processor to implement a scheduler engine operating in conjunction with one or more clinical systems,
the scheduler engine configured to:
enable the one or more clinical systems to operate with the scheduler engine in an analytical mode and an operating mode, wherein, when in the analytical mode;
the scheduler engine dynamically calculates one or more binding constraints on the one or more clinical systems for scheduling, wherein the calculation of the one or more binding constraints is based on one or more event inputs received from the one or more clinical systems, the event inputs comprising one or more tasks and sub-tasks, and associated resource locations for the one or more tasks and sub-tasks; and
when in the operating mode:
the scheduler engine configures and transmits a schedule to one or more scheduler interfaces of the one or more clinical systems, wherein the schedule is based on the one or more binding constraints calculated in the analytical mode, and wherein the schedule engine monitors, via a computer network, execution of the schedule by the one or more clinical systems, wherein the monitoring comprises tracking the resource locations during execution of the one or more tasks and sub-tasks;
wherein the scheduler engine dynamically switches between the analytical mode and the operating mode based at least in part on a probabilistic determination of delay associated with the schedule and the scheduler engine continually calculates the probabilistic determination of delay based on the monitoring, the scheduler engine triggered to switch from the operating mode to the analytical mode when the scheduler engine determines, based on the probabilistic determination of delay, that at least one of the one or more binding constraints is not satisfied and to switch from the analytical mode to the operating mode when the scheduler engine has recalculated the one or more binding constraints; and
wherein the scheduler engine transmits a reconfigured schedule to the one or more clinical systems to provide load balancing and dynamic schedule adjustment to the one or more clinical systems.
10. The computer-readable storage medium of claim 9 , wherein the probabilistic determination of delay associated with the schedule includes a schedule risk associated with a probability density function of time for a duration estimation of a task in the schedule.
11. The computer-readable storage medium of claim 10 , wherein the schedule risk is to be determined based on a simulation of task durations and logic for interdependencies between resources.
12. The computer-readable storage medium of claim 10 , wherein the schedule risk is to be determined based on measures of duration, availability, and reliability for the schedule.
13. The computer-readable storage medium of claim 9 , wherein the scheduler engine is to dynamically switch between the analytical mode and the operating mode based at least in part on a change in location of resources for adjacent tasks such that an estimation of transport time relative to task duration decreases a probability of task completion below an adjustable level.
14. The computer-readable storage medium of claim 9 , wherein, when in the analytical mode, the scheduler engine is to calculate slack values for relaxing the binding constraints.
15. The computer-readable storage medium of claim 9 , wherein, when in the analytical mode, the scheduler engine is to perform at least one of a what-was, what-is, what-if, or forecast analysis with respect to the one or more clinical systems for throughput and resource utilization.
16. The computer-readable storage medium of claim 9 , wherein the scheduler engine is to determine the schedule based on at least one of a constraint satisfaction problem or heuristics to determine resource availability for the schedule.
17. A computer-implemented method for multi-resource scheduling, the method comprising:
configuring, by a scheduler engine including a processor, the scheduler engine to operate with one or more clinical systems in an analytical mode and an operating mode;
when in the analytical mode, dynamically calculating, by the scheduler engine, one or more binding constraints on the one or more clinical systems for scheduling, wherein the calculation of the one or more binding constraints is based on one or more event inputs received from the one or more clinical systems, the event inputs comprising at least one or more tasks and sub-tasks, and associated resource locations for the one or more tasks and sub-tasks;
when in the operating mode, configuring and transmitting a schedule, by the scheduler engine to one or more scheduler interfaces of the one or more clinical systems, wherein the schedule is based on the one or more binding constraints calculated in the analytical mode, and monitoring, via a computer network, execution of the schedule by the one or more clinical systems, wherein the monitoring comprises tracking the resource locations during execution of the one or more tasks and sub-tasks, the schedule to be output to the one or more clinical systems to provide load balancing and dynamic schedule adjustment to the one or more clinical systems; and
dynamically switching, by the scheduler engine, between the analytical mode and the operating mode based at least in part on a probabilistic determination of delay associated with the schedule, wherein the probabilistic determination of delay is associated with a schedule risk and the scheduler engine calculates the probabilistic determination of delay based on the monitoring, the scheduler engine triggered to switch from the operating mode to the analytical mode when the scheduler engine determines, based on the probabilistic determination of delay and the associated schedule risk, that at least one of the one or more binding constraints is not satisfied and to switch from the analytical mode to the operating mode when the scheduler engine has recalculated the one or more binding constraints; and
transmitting a reconfigured schedule to the one or more clinical systems to provide load balancing and dynamic schedule adjustment to the one or more clinical systems.
18. The method of claim 17 , wherein the schedule risk is associated with a probability density function of time for a duration estimation of the one or more tasks in the schedule.
19. The method of claim 17 , wherein the scheduler engine is to dynamically switch between the analytical mode and the operating mode based at least in part on a change in location of resources for adjacent tasks such that an estimation of transport time relative to task duration decreases a probability of task completion below an adjustable level.
20. The method of claim 17 , wherein, when in the analytical mode, the scheduler engine is to calculate slack values for relaxing the binding constraints.