SYSTEM AND METHOD FOR MANAGING MOBILE WORKERS

• US 20090150209A1
• Filed: 12/15/2008
• Published: 06/11/2009
• Est. Priority Date: 09/06/2000
• Status: Active Grant

• Current Assignee: Masterlink Corporation (360Training.com, Inc.)
• Original Assignee: Masterlink Corporation (360Training.com, Inc.)
• Inventors: LEVINE, Kenneth M.
• Granted Patent: US 8,190,463 B2
• Time in Patent Office: Days
• US Class Current: 705/9
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group

Abstract

A system manages mobile workers and includes a plurality of clients and a server in communication with a plurality of clients. The server includes a database having a plurality of target objects that are classified corresponding to facilities assets to be worked on by a mobile worker and attributes of each target object, including any tasks to be performed on target objects. A rule engine is operative with the scheduling algorithm to map from a problem space for partitioned jobs and mobile workers to a solution to schedule jobs and mobile workers in selected different regions. The rule engine is operative to control the scheduling algorithm using heuristics comprising at least one of a tabu search, iterated local search, guided local search, and variable neighborhood search to focus a search space to regions for moving the schedules of high utility. A communications device is operative with the server for communicating any determined schedules and jobs as messages to clients.

Claims

1. A system for managing mobile workers, comprising:

a plurality of clients, each corresponding to a mobile worker to be managed, each client including a communications device for receiving messages detailing work schedules for tasks to be performed as jobs on target objects in a job environment;

a server in communication with each of said plurality of clients, said server including a database having a plurality of target objects that are classified corresponding to facilities assets to be worked on by a mobile worker, and attributes of each target object, including any tasks to be performed on target objects, said server including a rule engine operative with a scheduling algorithm to map from a problem space for partitioned jobs and mobile workers to a solution to schedule jobs and mobile workers in selected different regions, wherein a selected policy for a job environment determines how mobile workers, jobs and work schedules are partitioned, said server further comprising a plurality of system agents that automate supervision including work planning, scheduling, dispatching, stores management, job state management and end-of-shift management, and wherein the rule engine is operative to control the scheduling algorithm using heuristics comprising at least one of a tabu search, iterated local search, guided local search and variable neighborhood search to focus a search space to regions for moving to schedules of high utility; and

a communications device operative with said server for communicating any determined work schedules and jobs as messages to said clients.

2. The system according to claim 1, wherein said tabu search is operative to prevent searching recently explored portions of the search space.

3. The system according to claim 1, wherein said iterated local search perturbates a solution for a new solution as a starting value for another scheduling algorithm.

4. The system according to claim 1, wherein said guided local search perturbates a utility function to penalize sub-optimal components of a solution.

5. The system according to claim 1, wherein the variable neighborhood search perturbates a neighborhood search to be used in a next iteration.

6. The system according to claim 1, wherein the scheduling algorithm is operative with numerical and combinatorial constraint objects allowing searching from a general search to a more specific search.

7. The system according to claim 6, and further comprising a constraint object for maintaining time usage of a constraint object.

8. The system according to claim 1, wherein said server further comprises a simulator database and simulation module that queries the simulator database to determine the effects of a policy change on planning and scheduling of jobs and workers using different algorithms and partitions.

9. The system according to claim 1, wherein said server further comprises an event bus operative with the system agents and database, wherein said system agents communicate across the event bus with the database and rule engine for implementing system agent functions based on events passed over the event bus.

10. The system according to claim 1, wherein said scheduling algorithm comprises a rescheduling algorithm.

11. A system for managing mobile workers, comprising:

a plurality of clients, each corresponding to a mobile worker to be managed, each client including a communications device for receiving messages detailing work schedules for tasks to be performed as jobs on target objects in a job environment;

a server in communication with each of said plurality of clients, said server including a database having a plurality of target objects that are classified corresponding to facilities assets to be worked on by a mobile worker, and attributes of each target object, including any tasks to be performed on target objects, said server including a rule engine operative with a scheduling algorithm for partitioned jobs and mobile workers to schedule jobs and mobile workers in selected different regions, wherein a selected policy for a job environment determines how mobile workers, jobs and work schedules are partitioned, said server further comprising a plurality of system agents that automate supervision including work planning, scheduling, dispatching, stores management, job, state management and end-of-shift management, and wherein the rule engine is operative to control the scheduling algorithm using heuristics comprising at least one a tabu search, iterated local search, guided local search and variable neighborhood search and a policy database for storing data relating to selected policy for a job environment and how the mobile workers, jobs and work schedules are partitioned; and

a communications device operative with said server for communicating any determined work schedules and jobs to said clients.

12. The system according to claim 11, wherein said tabu search is operative to prevent searching recently explored portions of the search space.

13. The system according to claim 11, wherein said iterated local search perturbates a solution for a new solution as a starting value for another scheduling algorithm.

14. The system according to claim 11, wherein said guided local search perturbates a utility function to penalize sub-optimal components of a solution.

15. The system according to claim 11, wherein the variable neighborhood search perturbates a neighborhood search to be used in a next iteration.

16. The system according to claim 11, wherein the scheduling algorithm is operative with numerical and combinatorial constraint objects allowing searching from a general search to a more specific search.

17. The system according to claim 16, and further comprising a constraint object for maintaining time usage of a constraint object.

18. The system according to claim 11, wherein said server further comprises a simulator database and simulator module that queries the simulator database to determine the effects of a policy change on planning and scheduling of jobs and workers using different algorithms and partitions.

19. The system according to claim 11, wherein said server further comprises an event bus operative with the system agents and database, wherein said system agents communicate across the event bus with the database and rule engine for implementing system agent functions based on events passed over the event bus.

20. The system according to claim 11, wherein said scheduling algorithm comprises a rescheduling algorithm.