METHOD AND SYSTEM FOR ALLOCATING SPECIFIC APPOINTMENT TIME WINDOWS IN A SERVICE INDUSTRY

• US 20010049619A1
• Filed: 06/07/1999
• Published: 12/06/2001
• Est. Priority Date: 06/07/1999
• Status: Active Grant

• Current Assignee: GeoTab Incorporated
• Original Assignee: Pointserve, Inc. (GeoTab Incorporated)
• Inventors: LANE, MARK T., POWELL, G. EDWARD, INDSETH, RUNAR
• Granted Patent: US 7,127,412 B2
• Time in Patent Office: Days
• US Class Current: 705/9
• CPC Class Codes: G06Q 10/063112 Skill-based matching of a person or a group to a task
• CPC Class Codes: G06Q 10/063116 Schedule adjustment for a person or group
• CPC Class Codes: G06Q 10/06316 Sequencing of tasks or work
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/109 Time management, e.g. calendars, reminders, meetings or time accounting
• CPC Class Codes: G06Q 10/1093 Calendar-based scheduling for persons or groups
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

The present invention provides a method for allocating appointment time windows. The steps of this method include creating a statistical estimate of a daily schedule comprising a series of estimated service orders. An actual service order is then received. This actual service order is inserted into the daily schedule by using a set of scheduling instructions for determining the least cost to employ the available service resources. At this point, this actual service order does not have a system imposed time window. The set of scheduling instructions is used to determine a time window surrounding this insertion point. If the customer accepts this time window, then the closest estimated service order is replaced by this actual service order, and the daily schedule is recomputed based upon the revised set of service orders to yield a revised daily schedule. This process may be repeated for any number of days or time periods from which the customer may choose the time window best meeting the customer'"'"'s availability.

Claims

1. A method for allocating an appointment time window to derive an optimal service schedule for a service provider, comprising the steps of:

generating a statistical service schedule estimate for a predetermined service time period, said statistical service schedule estimate comprising a set of statistically-derived service orders;

(a) receiving an actual service order to fulfill in said predetermined service time period;

(b) imposing a scheduled time on said actual service order to derive an optimal time window in which to fulfill said actual service order;

(c) comparing said actual service order to said set of statistically-derived service orders for determining a most closely related service order from said set of statistically-derived service orders;

(d) replacing said most closely related service order with said actual service order creating a modified service order set;

(e) generating a modified service schedule based on said modified service order set; and

(f) repeating steps (a) through (e) in response to the receipt of additional actual service orders for generating an optimal service schedule.

2. The method of

claim 1

, further comprising the step of:

(a) approving a time window associated with said most closely related service order with a customer prior to said step of replacing said most closely related service order with said actual service order;

(b) determining a next most closely related service order in an instance where said customer does not approve said time window associated with said most closely related service order wherein said next most closely related service order is now treated as said most closely related service order; and

repeating steps (a) and (b) until said customer approves said time window associated with said most closely related statistically-derived service order.

3. The method of

claim 1

, wherein said scheduled time of said actual service order is centered within said optimal time window.

4. The method of

claim 1

, wherein the daily schedule comprises a plurality of individual workforce member schedules.

5. The method of

claim 4

, wherein the correlation score is computed based on a plurality of variables wherein the variables comprise:

a service order skill level required to complete the service order;

a workforce skill level denoting an ability level of the individual workforce members; and

a geographic location denoting where the service order is to be performed.

6. The method of

claim 4

, wherein the step of creating a statistical estimate further comprises the steps of:

creating a database comprising estimated service orders;

updating the database with the first and subsequent actual service orders; and

refining the statistical estimate based upon the updated database.

7. The method of

claim 4

, wherein the step of determining the estimated time window for service orders further comprises determining a plurality of potential estimated time windows and wherein a customer chooses a specific time window from the plurality of potential estimated time windows and wherein the chosen specific time window becomes fixed in the daily schedule.

8. The method of

claim 7

, wherein the daily schedule for a workforce can be recomputed while maintaining the chosen specific time windows as a fixed constraint in the daily schedule, and wherein the daily schedule can be optimized with respect to several variables comprising:

a first skill level required to complete the service order;

a second skill level denoting an ability level of the individual workforce members; and

a geographic location denoting where the service order is to be performed; and

a travel time and distance between the geographic location denoting where the service is to be performed.

9. A method for allocating appointment time windows, using a computer program, and wherein the computer program is operable to execute the method with a scheduler software engine, comprising the steps of:

generating a statistical service schedule estimate for a predetermined service time period, said statistical service schedule estimate comprising a set of statistically-derived service orders;

(a) receiving an actual service order to fulfill in said predetermined service time period;

(b) imposing a scheduled time on said actual service order to derive an optimal time window in which to fulfill said actual service order;

(c) comparing said actual service order to said set of statistically-derived service orders for determining a most closely related service order from said set of statistically-derived service orders;

(d) replacing said most closely related service order with said actual service order creating a modified service order set;

(e) generating a modified service schedule based on said modified service order set; and

(f) repeating steps (a) through (e) in response to the receipt of additional actual service orders for generating an optimal service schedule.

10. The method of

claim 9

, further comprising the steps of:

(a) approving a time window associated with said most closely relaed service order with a customer prior to said step of replacing said most closely related service order with said actual service order;

(b) determining a next most closely related service order in an instance where said customer does not approve said time window associated with said most closely related service order wherein said next most closely related service order is now treated as said most closely related service order; and

repeating steps (a) and (b) until said customer approves said time window associated with said most closely related statistically-derived service order.

11. The method of

claim 9

, wherein said scheduled time of said actual service order is centered within said optimal time window.

12. The method of

claim 9

, wherein the daily schedule comprises a plurality of individual workforce member schedules.

13. The method of

claim 9

, wherein the correlation score is computed based on a plurality of variables wherein the variables comprise:

a first skill level required to complete the service order;

a second skill level denoting an ability level of the individual workforce members; and

a geographic location denoting where the service order is to be performed.

14. The method of

claim 12

, wherein the step of creating a statistical estimate further comprises the steps of:

creating a database comprising estimated service orders;

updating the database with the first and subsequent actual service orders; and

refining the statistical estimate based upon the updated database.

15. The method of

claim 12

, wherein the step of determining the estimated time window for service orders further comprises determining a plurality of potential estimated time windows and wherein a customer chooses a specific time window from the plurality of potential estimated time windows and wherein the chosen specific time window becomes fixed in the daily schedule.

16. The method of

claim 12

, wherein the daily schedule for a workforce can be recomputed while maintaining the chosen specific time windows as a fixed constraint in the daily schedule, and wherein the daily schedule can be optimized with respect to several variables comprising:

a first skill level required to complete the service order;

a second skill level denoting an ability level of the individual workforce members; and

a geographic location denoting where the service order is to be performed; and

a travel time and distance between the geographic location denoting where the service is to be performed.

17. A computer system directed by a computer program operable to schedule a plurality of diverse work assignments, the computer system comprising:

a software engine;

a storage device operable to store program data;

a memory operable to store a computer program;

a processor coupled to the storage device and the memory, the processor operable to execute the computer program such that the computer is directed by the computer program to allocate appointment time windows for service orders performed by a mobile workforce having a plurality of mobile workforce members by performing the steps of;

generating a statistical service schedule estimate for a predetermined service time period, said statistical service schedule estimate comprising a set of statistically-derived service orders;

(a) receiving an actual service order to fulfill in said predetermined service time period;

(b) imposing a scheduled time on said actual service order to derive an optimal time window in which to fulfill said actual service order;

(c) comparing said actual service order to said set of statistically-derived service orders for determining a most closely related service order from said set of statistically-derived service orders;

(d) replacing said most closely related service order with said actual service order creating a modified service order set;

(e) generating a modified service schedule based on said modified service order set; and

(f) repeating steps (a) through (e) in response to the receipt of additional actual service orders for generating an optimal service schedule.

18. The computer system of

claim 17

, wherein the computer program further performs the steps of:

(a) approving a time window associated with said most closely related service order with a customer prior to said step of replacing said most closely related service order with said actual service order;

(b) determining a next most closely related service order in an instance where said customer does not approve said time window associated with said most closely related service order wherein said next most closely related service order is now treated as said most closely related service order; and

repeating steps (a) and (b) until said customer approves said time window associated with said most closely related statistically-derived service order.

19. The computer system of

claim 17

, wherein said scheduled time of said actual service order is centered within said optimal time window.

20. The computer system of

claim 17

, wherein the daily schedule comprises a plurality of individual workforce member schedules.

21. The computer system of

claim 17

, wherein the correlation score is computed based on a plurality of variables wherein the variables comprise:

a first skill level required to complete the service order;

a second skill level denoting an ability level of the individual workforce members; and

a geographic location denoting where the service order is to be performed.

22. The computer system of

claim 17

, wherein the step of creating a statistical estimate further comprises the steps of:

creating a database comprising estimated service orders;

updating the database with the first and subsequent actual service orders; and

refining the statistical estimate based upon the updated database.

23. The computer system of

claim 17

, wherein the step of determining the estimated time window for service orders further comprises determining a plurality of potential estimated time windows and wherein a customer chooses a specific time window from the plurality of potential estimated time windows and wherein the chosen specific time window becomes fixed in the daily schedule.

24. The computer system of

claim 23

, wherein the daily schedule for a workforce can be recomputed while maintaining the chosen specific time windows as a fixed constraint in the daily schedule, and wherein the daily schedule can be optimized with respect to several variables comprising:

a first skill level required to complete the service order;

a second skill level denoting an ability level of the individual workforce members;

a geographic location denoting where the service order is to be performed; and

a travel time and distance between the geographic location denoting where the service is to be performed.