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

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

Services in Call Centers 3

Services in Emergency Departments 4

Services in Transportation 5

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

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

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

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)

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

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

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

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

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

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

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

Back to Call Centers… 17

Service From a Customer Perspective 18

Customer-Resource Choreography 19

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

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

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

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

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

Customer S-Log 25

 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

Resource S-Log: Skill and Decision 27

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

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)

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

Longest-Queue First (LQF) 31 N

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

Evaluation 33

Data Set  The data comes from a large Israeli telecommunication company: o 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

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

Misclassification Rate: Linear Models 36

Misclassification Rate: Tree Methods 37

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

Delay Time Distribution: VIP 39

Misclassification Rate: Queueing Heuristics 40

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

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

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

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

Thank you! 45