1. Mining Resource-Scheduling Protocols Arik Senderovich, Matthias Weidlich, Avigdor Gal, and Avishai Mandelbaum Technion – Israel Institute of Technology Imperial College London

2. Services are economic interactions between customers and service providers that create added value in return for customer’s time, money and effort. Service management – operations, strategy, and information technology (Fitzsimmons and Fitzsimmons, 2006) Our Playground: Services 2

3. Services in Call Centers 3

4. Services in Emergency Departments 4

5. Services in Transportation 5

6. Operational Data-Driven Analysis of Services  Capacity analysis (e.g. utilization of resources)  Time analysis (e.g. predicting delays)  Sensitivity analysis (directions for process improvement)  Optimization with respect to some goal 6

7. Service Characteristics  Services require participation (of both customers and resources), perish if not handled online and cannot be stored (lost business)  Scarce resources and uncertainty in demand formulate Queues in front of service activities: 7 Service

8. Data Mining Queue Mining Service Modeling and Analysis via the queueing perspective 8 Queue mining – predicting delays in service processes (S. et al., 2014)

9. Queue Mining 9  How can it be done? By discovering: o Analytical models (e.g. from Queueing Theory) o Simulation models  Discovery requires: 1. Building blocks (arrival rates, service times,…) 2. Structure (control-flow) 3. Scheduling protocols (rules by which customers and resources are matched for service)

10. Why do protocols matter? 10 N Q: How long will the red customer wait? A: Depends on the scheduling protocol!

11. Protocol: First-Come First-Served 11 N Q: How long will the red customer wait? A: At least two service times…

12. Protocol: Strict Priorities 12 N Q: How long will the red customer wait? A: At most one service time Emergency Regular

13. Outline  Introduction: o Services and Queues o Motivation  Problem Definition  Proposed Solution  Empirical Evaluation  Future Work 13

14. Mining Resource Scheduling Protocols  A resource becomes available and “observes” the pool of waiting customers of various types  Mining Resource-Scheduling Protocols Problem: Predict the next customer-type that will enter service  Intra-queueing policy (within types) is assumed to be First-Come First-Served (FCFS) 14

15. Mining Protocols as a Classification Problem  Protocol mining can be viewed as the following classification problem: o Given a feature vector (that includes resource type and queueing parameters) o Provide a decision on the customer class to enter service 15

16. Solution Overview 1. Selecting a use-case: Call Center 2. Extracting features from service event logs 3. Mining the Resource-Scheduling Protocol: o Data mining techniques for classification o Protocol approximation via queueing heuristics 16

17. Back to Call Centers… 17

18. Service From a Customer Perspective 18

19. Customer-Resource Choreography 19

20. Service from a Resource perspective 20 “Pick customer” follows Resource Scheduling Protocols; In call centers, the selection is often predefined and automatic

21. W-Queue Architecture 21 If a resource becomes available, which customer is picked for service? Red/Blue/Green?

22. Solution Overview 1. Selecting a use-case: Call Center 2. Extracting features from service event logs 3. Mining the Resource-Scheduling Protocol: o Data mining techniques for classification o Protocol approximation via queueing heuristics 22

23. Goals of Resource-Scheduling Protocols  We assume that there are two competing goals: o Reducing Delays: supported by research on the relation of delays to customer satisfaction in services (Larson, 1987) o Optimizing Quality of Service: customers are to be served by the most suitable resource (e.g. senior physicians for complex patients)  These goals define relevant features for protocol mining (e.g. resource skills, queue- length) 23

24. Event Logs: Customer-Resource Duality  For protocol mining both customer and resource event logs are required: o Queueing features and customer types come from the customer log o Decisions (outcomes) and resource skills come from the resource log 24

25. Customer S-Log 25

26.  Queue-Length (customers that had qEntry only)  Head-of-line delay (the time in queue for customers that had qEntry only) Queueing Features 26 N QL=1 QL=2 HOL = 3 minutes HOL = 2 minutes

27. Resource S-Log: Skill and Decision 27

28. Solution Overview 1. Selecting a use-case: Call Center 2. Extracting features from service event logs 3. Mining the Resource-Scheduling Protocol: o Data mining techniques for classification o Protocol approximation via queueing heuristics 28

29. DM Methods  Linear models: o Linear Discriminant Analysis (LDA) o Multinomial Logistic Regression (MLR)  Tree-based models: o Classification (or Decision) Trees o Random Forests 29 The Elements of Statistical Learning (Hastie, Tibshirani, Friedman, 2014)

30. Queueing Heuristics  The heuristics originate in protocols that minimize delays in overloaded queues  Two simple rules that can be used to approximate real (complex) protocols: o Longest-Queue First o Most-Delayed First 30

31. Longest-Queue First (LQF) 31 N

32. Most-Delayed First (MDF) 32 N Wait of head-of-line: 3 minutes 2 minutes

33. Evaluation 33

34. Data Set  The data comes from a large Israeli telecommunication company: o 50000 service requests per weekday o 700 agent positions per day o Multiple services: Private, Business, Content,…  We focus on the Private sector that follows the W architecture: 34

35. Experiment Setting  Feature selection imposed the scenarios: 1. Resource type only 2. Queue lengths + resource types 3. Head-of-line delays + resource types 4. All the above  Dependent variable = misclassification rate (due to a 0-1 loss function) 35

36. Misclassification Rate: Linear Models 36

37. Misclassification Rate: Tree Methods 37

38. Exploring Protocol via Decision Tree 38 Regular VIP Q_1 – Low Q_2 – Regular Q_3 - VIP

39. Delay Time Distribution: VIP 39

40. Misclassification Rate: Queueing Heuristics 40

41. Value of Queueing Heuristics Simple Approximations to Complex Protocols  Both queueing heuristics are easily calculated online (no learning phase)  The Longest-Queue-First heuristic is comparable to Decision Methods  Tree-based methods require offline learning, online adjustments (concept drift) and are more difficult to understand 41

42. Results Overview  Resource-Scheduling Protocols can be accurately deciphered via Decision Trees (and their extensions)  Simple queueing heuristics can serve as good approximations for complex Decision Trees 42

43. Future Work 43  Decomposing complex networks of services into queueing architectures (e.g. the W architecture)

44. Future Work 44  Decomposing complex networks of services into queueing architectures (e.g. the W-Queue architecture)  Extending delay prediction techniques by considering the mined resource protocols

45. Thank you! sariks@tx.technion.ac.il 45