SYSTEMS AND METHODS FOR SCHEDULING MULTI-SKILLED STAFF

• US 20150356514A1
• Filed: 06/10/2014
• Published: 12/10/2015
• Est. Priority Date: 06/10/2014
• Status: Abandoned Application

• Current Assignee: Infrared Integrated Systems, Ltd. (Fortive Corp.)
• Original Assignee: Infrared Integrated Systems, Ltd. (Fortive Corp.)
• Inventors: Snell, William, Holliday, Stuart, Bate, Daniel
• Application Number:
  US14/300,961
• Publication Number:
  US 20150356514A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G06Q 10/063112 Skill-based matching of a person or a group to a task
• CPC Class Codes: G06Q 10/103 Workflow collaboration or project management
• CPC Class Codes: G06Q 10/1093 Calendar-based scheduling for persons or groups
• CPC Class Codes: G06Q 10/1097 Task assignment
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions

Abstract

This disclosure provides systems and methods for generating staffing schedules. Some systems may be configured to generate staffing schedules including a flexible staffing recommendation indicative of a number and a time in which to schedule multi-skilled staff. In some examples, such staffing schedules can be generated based on a demand model generated based on historical demand data and scheduling thresholds.

Claims

1. An automatic facilities scheduling system, comprising:

a plurality of facilities;

one or more databases;

one or more data collectors;

one or more processors; and

one or more computer storage mediums storing computer program modules adapted to execute on the one or more processors, the computer program modules comprising

a data collection module adapted to receive demand data from the one or more data collectors and store the demand data to the one or more databases, and produce a knowledge base including a record of historical demand,

a demand prediction module adapted to generate a demand model indicative of future demand for the facilities over a time period, and

a scheduling module adapted to

receive scheduling parameters including scheduling requirements, a first scheduling threshold, a second scheduling threshold, and a scheduling period,

invoke the demand prediction module to generate a scheduling demand model during the scheduling period,

create a first staffing schedule based on the scheduling requirements, the first scheduling threshold, and the scheduling demand model,

create a second staffing schedule based on the scheduling requirements, the second scheduling threshold, and the scheduling demand model, and

generate a composite staffing schedule based on the first and second staffing schedules, wherein the composite staffing schedule includes a flex-staff scheduling recommendation.

2. The system of claim 1, wherein the demand prediction module generates the demand model based on the knowledge base, including the record of historical demand.

3. The system of claim 1, wherein the scheduling module is further adapted to generate a demand simulation based on the scheduling demand model, the demand simulation including a plurality of simulations each indicative of a probable demand for the facilities over the scheduling period.

4. The system of claim 3, wherein the scheduling module is adapted to create the first and second staffing schedules by

generating the first and second staffing schedules,

generating a first schedule score for the first staffing schedule based on the demand simulation and the scheduling requirements,

iteratively building the first staffing schedule until the first schedule score satisfies the first scheduling threshold,

generating a second schedule score for the second staffing schedule based on the demand simulation and the scheduling requirements, and

iteratively building the second staffing schedule until the second schedule score satisfies the second scheduling threshold.

5. The system of claim 4, wherein the first and second schedule scores are indicative of a percentage of the simulations of the demand simulation that meet the scheduling requirements for the first and second staffing schedules, respectively.

6. The system of claim 1, wherein the scheduling requirements include a maximum queue length for each of the facilities.

7. The system of claim 3, wherein the demand simulation is randomized.

8. The system of claim 7, wherein the demand simulation is a Monte Carlo simulation.

9. The system of claim 1, wherein the data collection module is configured to update the knowledge base at specified time intervals.

10. A method for automatically generating a staffing schedule, the method comprising:

collecting demand data from one or more data collectors, the demand data associated with a demand for a plurality of facilities;

generating a knowledge base based on the demand data, the knowledge base including a record of historical demand;

receiving the demand data and storing the demand data in one or more databases;

receiving scheduling parameters including scheduling requirements, a first scheduling threshold, a second scheduling threshold, and a scheduling period;

generating a scheduling demand model indicative of future demand for the facilities during the scheduling period;

creating a first staffing schedule based on the scheduling requirements, the first scheduling threshold, and the scheduling demand model;

creating a second staffing schedule based on the scheduling requirements, the second scheduling threshold, and the scheduling demand model; and

generating a composite staffing schedule based on the first and second staffing schedules, wherein the composite staffing schedule includes a flex-staff scheduling recommendation.

11. The method of claim 10, wherein the scheduling demand model is generated based on the knowledge base, including the record of historical demand.

12. The method of claim 10, further comprising generating a demand simulation based on the scheduling demand model, the demand simulation including a plurality of simulations each indicative of a probable demand for the facilities over the scheduling period.

13. The method of claim 12, wherein creating the first staffing schedule comprises:

generating the first staffing schedule,

generating a first schedule score for the first staffing schedule based on the demand simulation and the scheduling requirements, and

iteratively building the first staffing schedule until the first schedule score satisfies the first scheduling threshold.

14. The method of claim 12, wherein creating the first staffing schedule comprises:

generating the first staffing schedule,

generating a first schedule score for the first staffing schedule based on the demand simulation and the scheduling requirements,

iteratively building the first staffing schedule until the first schedule score satisfies the first scheduling threshold, and

wherein creating the second staffing schedule comprises;

generating the second staffing schedule

generating a second schedule score for the second staffing schedule based on the demand simulation and the scheduling requirements, and

iteratively building the second staffing schedule until the second schedule score satisfies the second scheduling threshold.

15. The method of claim 14, wherein the first and second schedule scores are indicative of a percentage of the simulations of the demand simulation that meet the scheduling requirements for the first and second staffing schedules, respectively.

16. The method of claim 10, wherein the scheduling requirements include a maximum queue length for each of the facilities.

17. The method of claim 12, wherein the demand simulation is randomized.

18. The method of claim 17, wherein the demand simulation is a Monte Carlo simulation.

19. The method of claim 10, further comprising updating the knowledge base at specified time intervals.

20. A method for automatically generating a staffing schedule, the method embodied in a set of machine-readable instructions executed on a processor and stored on a tangible medium, the method comprising:

collecting demand data from one or more data collectors, the demand data associated with a demand for a plurality of facilities;

generating a knowledge base based on the demand data, the knowledge base including a record of historical demand;

receiving the demand data and storing the demand data in one or more databases;

receiving scheduling parameters including scheduling requirements, a first scheduling threshold, a second scheduling threshold, and a scheduling period;

generating a scheduling demand model indicative of future demand for the facilities during the scheduling period;

creating a first staffing schedule based on the scheduling requirements, the first scheduling threshold, and the scheduling demand model;

creating a second staffing schedule based on the scheduling requirements, the second scheduling threshold, and the scheduling demand model; and

generating a composite staffing schedule based on the first and second staffing schedules, wherein the composite staffing schedule includes a flex-staff scheduling recommendation.