SYSTEM AND METHOD FOR MANAGING MOBILE WORKERS

• US 20120203728A1
• Filed: 04/18/2012
• Published: 08/09/2012
• Est. Priority Date: 09/06/2000
• Status: Abandoned Application

• Current Assignee: Masterlink Corporation (360Training.com, Inc.)
• Original Assignee: Masterlink Corporation (360Training.com, Inc.)
• Inventors: LEVINE, Kenneth M.
• Application Number:
  US13/449,565
• Publication Number:
  US 20120203728A1
• Time in Patent Office: Days
• US Class Current: 706/47
• 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. A database includes 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 determines algorithms based on a utility function for partitioned jobs wherein different algorithms are selected and used for different partitions to schedule jobs and mobile workers in selected different regions. An algorithm is selected based on a weighted sum that is calculated from a possible number of work schedules, jobs and mobile workers for each partition. A selected policy for a job environment determines how mobile workers, jobs and work schedules are partitioned. A plurality of system agents automate supervision.

Claims

1-20. -20. (canceled)

21. A system for managing mobile workers, comprising:

a server;

a database associated with the server and 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 operative for determining algorithms based on a utility function for partitioned jobs and mobile workers wherein different algorithms are selected and used for different partitions to schedule jobs and mobile workers in selected different regions, wherein an algorithm is selected based on a weighted sum that is calculated from a possible number of work schedules, jobs and mobile workers for each partition, wherein a selected policy for a job environment determines how mobile workers, jobs and work schedules are partitioned; and

a plurality of system agents that automate supervision including work planning, scheduling, dispatching, stores management, job state management and end-of-shift management.

22. The system according to claim 21, 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.

23. The system according to claim 21, 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.

24. The system according to claim 21, and further comprising a scheduling algorithm configured 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.

25. The system according to claim 24, wherein the scheduling algorithm is operative with numerical and combinatorial constraint objects allowing searching from a general search to a more specific search.

26. The system according to claim 25, and further comprising a constraint object for maintaining time usage of a constraint object.

27. The system according to claim 24, wherein said scheduling algorithm comprises a rescheduling algorithm.

28. The system according to claim 24, 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.

29. The system according to claim 28, wherein said tabu search is operative to prevent searching recently explored portions of the search space.

30. The system according to claim 28, wherein said iterated local search perturbates a solution for a new solution as a starting value for another scheduling algorithm.

31. The system according to claim 28, wherein said guided local search perturbates a utility function to penalize sub-optimal components of a solution.

32. The system according to claim 28, wherein the variable neighborhood search perturbates a neighborhood search to be used in a next iteration.

33. A system for managing mobile workers, comprising:

a server;

a database associated with the server 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 operative for determining algorithms based on a utility function for partitioned jobs and mobile workers wherein different algorithms are selected and used for different partitions to schedule jobs and mobile workers in selected different regions, wherein an algorithm is selected based on a weighted sum that is calculated from a possible number of work schedules, jobs and mobile workers for each partition, wherein a selected policy for a job environment determines how mobile workers, jobs and work schedules are partitioned; and

a plurality of system agents that automate supervision including work planning, scheduling, dispatching, stores management, job, state management and end-of-shift management.

34. The system according to claim 33, 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.

35. The system according to claim 33, 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.

36. The system according to claim 33, and further comprising a scheduling algorithm configured 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.

37. The system according to claim 36, wherein the scheduling algorithm is operative with numerical and combinatorial constraint objects allowing searching from a general search to a more specific search.

38. The system according to claim 37, and further comprising a constraint object for maintaining time usage of a constraint object.

39. The system according to claim 36, wherein said scheduling algorithm comprises a rescheduling algorithm.

40. The system according to claim 36, 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.

41. The system according to claim 40, wherein said tabu search is operative to prevent searching recently explored portions of the search space.

42. The system according to claim 40, wherein said iterated local search perturbates a solution for a new solution as a starting value for another scheduling algorithm.

43. The system according to claim 40, wherein said guided local search perturbates a utility function to penalize sub-optimal components of a solution.

44. The system according to claim 40, wherein the variable neighborhood search perturbates a neighborhood search to be used in a next iteration.