Rules-based Mobile Resource Learner for Field Scheduling Applications
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This document discusses using rule-based machine learning to help optimize field service scheduling. It describes four business scenarios where scheduling challenges arise and how learning could help. The key things it proposes learning are workforce skills and geographical areas, workload patterns, and travel models to improve schedule quality and balance work assignments. Rules are used to learn from historical data and dynamically update the scheduling model over time based on new information.
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Rules-based Mobile Resource Learner for Field Scheduling Applications
- 1. Rule-based Mobile Resource Learner for Field Scheduling Applications Evgeny Selensky Trimble UK November 2013
- 2. Outline Motivational Business Cases Why Use Learning? What Problem Characteristics to Learn? An Example of Learning Infrastructure Using Rules for Learning Further Extensions 2
- 4. Business Scenario I: Best Tech 4
- 5. Business Scenario II: Safest Route 5
- 6. Business Scenario II: Safest Route 6
- 7. Business Scenario III: Building workforce expertise Alice has been assigned boiler maintenance jobs Customer requests boiler repair Tim is expert at repairing this brand but lives far Shall we assign it to Alice or Tim? 7
- 8. Business Scenario IV: an Imperfect World Operator constructed a perfect schedule yesterday Jim is stuck in traffic on his first job Fred asks for sick leave Customers not on premises, workforce is idle Main road is flooded, detour takes a lot of time Spare parts are missing 8
- 9. Field Service Scenarios Large Scale Complex Dynamic Frequent Exceptional Situations Inaccurate Data Changing Business Objectives 9
- 10. Current State Customers have ad hoc solutions involving manual intervention Error-prone Tiresome Time consuming Overly resource intensive Expert knowledge required Poor schedule quality 10
- 11. To Learn or Not to Learn? Minimise time on data build and maintenance Mitigate risks of schedule disruptions Improve actual dispatched schedule quality 11
- 12. What to Learn? Workforce Where they operate geographically What skills/preferences they have Workload Typical job types and durations Skill requirement distributions over time Travel Model Street Level Routing engines provide answers dependent on time of query 12
- 13. Why learn geo areas and skills? From experience: Hardest to set up and maintain Most error-prone Most affected by having to rely on manual intervention Familiarity of workforce with areas Minimize journey/parking times Workforce skill learning Improve job execution success rates 13
- 14. Learning for data setup Semi-static User Inputs: Skills, Working Areas, … UI/APIs + data storage Dynamic user inputs: Dispatcher assignments (historic & real-time) Learn Update Model Learner Automated inputs: Job & tracked Vehicle Stop locations 14 Enterprise Resource Planning Solver Rule Engine
- 15. Rule Based Learning of Geo Areas Capture dynamic nature of the problem Filter noise: notice only significant events Maintain a history of job assignments Maintain resource geo areas based on active historic assignments Run learner periodically (e.g. before working day starts) 15
- 16. History Extension Rules Bootstrapping Mode Condition Condition Conclusion No. All Assignments No. Assignments in a Cluster Current Assignment State < 10 Is >= 10 > >= 10 <= Active 3 Is Active 3 Is Inactive 16
- 17. History State Maintenance Rules Condition Condition Condition Conclusion Current Assignment State No. Other Assignments in Cluster Current Assignment Expired Current Assignment State Is Inactive > 3 Is 17 FALSE Is Active
- 18. History State Maintenance Rules Condition Condition Condition Conclusion Conclusion No. Active Assignments Current Assignment State No. Days Since Current Assignment Current Assignment Expired Current Assignment State ≥ Is 50 Active ≥ 365 18 Is TRUE Is Inactive
- 19. Learning Technicians’ Geo Areas 19
- 20. Learning Technicians’ Geo Areas 20
- 21. Learning Technicians’ Geo Areas 21
- 22. Learning Technicians’ Geo Areas 22
- 23. Learning Resource Skills Installation Maintenance Repair Router Switch Hub Cisco Xerox HP 250 837 1000 Testing Router Skill dependencies 23
- 24. Learning Resource Skills Condition Conclusion No. Installation Assignments Minimum Installation Proficiency Level Within [1,6) Is Inexperienced Within [6,10) Is Moderately Experienced Within [10,20) Is Experienced ≥ [20,40) Is Expert 24
- 25. Learning Resource Skills Condition Condition Conclusion Cisco Repair Proficiency Level Cisco Maintenance Proficiency Level Cisco Minimum Testing Proficiency Level Is Experienced Is Experienced Is Moderately Experienced Is Expert Is Experienced Is Moderately Experienced Is Expert Is Expert Is Experienced 25
- 26. Skills Dynamics Router Installation Skills Router Installation Skills 6 5 5 4 Any Type Levels Levels 6 Cisco 3 HP Xerox 2 1 4 Any Type Cisco 3 HP Xerox 2 1 0 0 1 2 3 4 5 6 7 8 9 10 1 Days 2 3 4 5 6 7 8 9 10 Days With Skill Dependencies Without Skill Dependencies 26
- 27. Learning Aided Scheduling Use learned resource-job associations Directly to make assignments Heuristically to prefer using resources with enough expertise Satisfy job skill requirements to increase job success rate Spare highly qualified resources for jobs demanding high skills 27
- 29. Intuitive Extensions Learning about: Travel data from Street Level Routing data providers Workload temporal patterns Optimum algorithm parameter settings 29
- 30. Wrap up Learning can greatly facilitate data build and maintenance in field service applications Rules can be a clear, easy-to-code and easy-tomaintain interface between the learner and the problem model 30
- 31. Thank you! 31