Beyond Spam: OR/MS Modeling Opportunities for Email Response Management Ramesh Sharda Robert A....

Beyond Spam: OR/MS Beyond Spam: OR/MS Modeling Modeling

Opportunities for Opportunities for Email Response Email Response

ManagementManagement

Ramesh ShardaRamesh Sharda Robert A. Greve, Ashish Gupta, Robert A. Greve, Ashish Gupta, Manjunath Kamath, Mohan R. Manjunath Kamath, Mohan R.

ChinnaswamyChinnaswamy

Oklahoma State UniversityOklahoma State University

[email protected]@okstate.edu

APMOD2004 Sharda/OSU 2

Managing EmailManaging Email

• Pull the plug!Pull the plug!• Spam controlSpam control• Email filtering and organizationEmail filtering and organization• Effective management policies and Effective management policies and

strategiesstrategies– Organizational and Individual levelOrganizational and Individual level– Modeling opportunitiesModeling opportunities


Our ProjectsOur Projects

• Routing and priority decisions in an Routing and priority decisions in an email contact centeremail contact center– Simulation and queuing theorySimulation and queuing theory

• How often should we process our How often should we process our emailsemails– SimulationSimulation

• Which email messages to process Which email messages to process – Stochastic Programming with RecourseStochastic Programming with Recourse


Queuing and Simulation Queuing and Simulation Models For Analyzing Models For Analyzing

Customer Contact Center Customer Contact Center OperationsOperations


Inbound E-mail Contact Center Inbound E-mail Contact Center IssuesIssues

• Operational planningOperational planning

– Number of agentsNumber of agents

– Agents’ scheduleAgents’ schedule

• Routing policies and prioritiesRouting policies and priorities

– Routing to agentsRouting to agents

– Processing orderProcessing order

• Performance measuresPerformance measures

– Response/Resolution timeResponse/Resolution time

– Agent utilizationAgent utilization

• Organizational behavior Organizational behavior

– Human factorsHuman factors


Call/Contact Center LiteratureCall/Contact Center Literature

• Koole and Talim [2000]Koole and Talim [2000]

– Exponential approximation in the design of call centersExponential approximation in the design of call centers

• Koole and Mandelbaum [2002]Koole and Mandelbaum [2002]

– Queueing models of call centersQueueing models of call centers

• Koole, Pot and Talim [2003]Koole, Pot and Talim [2003]

– Performance of call center with skill-based routingPerformance of call center with skill-based routing

• Armony and Maglaras [2002, 2003]Armony and Maglaras [2002, 2003]

– Optimal staffing policyOptimal staffing policy

– Estimation scheme for the response timeEstimation scheme for the response time

• Whitt [2002]Whitt [2002]

– Challenges and research directions in the design of customer Challenges and research directions in the design of customer

contact centerscontact centers


CContact Center Descriptionontact Center Description

START

END

RECEIVE E-MAIL

IDENTIFY E-MAIL TYPE USING

SOFTWARE

DIRECT E- MAILTO AN AGENT

PRE-PROCESSE-MAIL

FORWARDE-MAIL ?

PROCESS E-MAIL

DELAY

RESOLVED ?

NO

NO YES

YES

E-MAIL HANDLING LOGIC


Model DetailsModel Details

• Poisson arrivalsPoisson arrivals• Agents process e-mails according to a FCFS Agents process e-mails according to a FCFS

disciplinediscipline• For an unresolved problem, the e-mail enters the For an unresolved problem, the e-mail enters the

system with a delay independent of the prior system with a delay independent of the prior processingprocessing

• The pre-processing time follows a The pre-processing time follows a uniform uniform distributiondistribution

• The processing time follows a The processing time follows a generalgeneral distribution distribution– Erlang, Exponential, and HyperexponentialErlang, Exponential, and Hyperexponential

• 2 types of e-mail and 3 agents2 types of e-mail and 3 agents


Open Queuing Network ModelOpen Queuing Network Model

• Nodes represent agentsNodes represent agents

• Customers represent e-mailsCustomers represent e-mails

• Model parameters Model parameters

– Number of nodes and the number of servers at each nodeNumber of nodes and the number of servers at each node

– Markovian routing probability matrixMarkovian routing probability matrix

– Mean and SCV (=Variance/MeanMean and SCV (=Variance/Mean22) service time at each ) service time at each

node node

– Arrival rate and SCV for new e-mailsArrival rate and SCV for new e-mails


Numerical ExperimentsNumerical Experiments

• Queueing model was solved using the Queueing model was solved using the Rapid Rapid

Analysis of Queueing Systems (RAQS) packageAnalysis of Queueing Systems (RAQS) package

– RAQS is a software package for analyzing general RAQS is a software package for analyzing general

queueing network models based on a two-moment queueing network models based on a two-moment

framework [http://www.okstate.edu/cocim/raqs/]framework [http://www.okstate.edu/cocim/raqs/]

• Simulation results obtained using a model in Arena Simulation results obtained using a model in Arena

7.07.0– Replications – 10Replications – 10

– Run length – 9,240 hoursRun length – 9,240 hours

– Warm up – 840 hours Warm up – 840 hours


EXPERIMENTAL EXPERIMENTAL DESIGNDESIGN

* Numbers refer to priorities assigned to new email, previously processed (by the same agent) email, and previously processed (by the a different agent) email, respectively. First Come First Served (FCFS) gives priority only based on arrival time.

FACTORFACTOR LEVELSLEVELS

RoutingRouting Directly routed to the same Directly routed to the same agentagent

Randomly routedRandomly routed

Routed to agent with the Routed to agent with the lightest current loadlightest current load

Priority Scheme*Priority Scheme* 1, 2, 31, 2, 3

1, 3, 11, 3, 1

2, 1, 32, 1, 3

2, 3, 12, 3, 1

3, 1, 23, 1, 2

3, 2, 13, 2, 1

FCFSFCFS


EXPERIMENTAL DESIGNEXPERIMENTAL DESIGN

• PERFORMANCE MEASURESPERFORMANCE MEASURES

– Purchase Inquiry Response TimePurchase Inquiry Response Time– Purchase Inquiry Resolution TimePurchase Inquiry Resolution Time– Problem Resolution Request Response Problem Resolution Request Response

TimeTime– Problem Resolution Request Resolution Problem Resolution Request Resolution

TimeTime


RESULTS (high RESULTS (high utilization)utilization)Purchase Inquiry Resolution Time

PRIORITY

fcfs321312231213132123

24

22

20

18

16

14

12

10

ROUTING

low est load

none

same agent

Purchase Inquiry Response Time

PRIORITY

fcfs321312231213132123

20

18

16

14

12

10

8

ROUTING

low est load

none

same agent

•Prioritization schemes that gave last priority to new email messages result in longer response and resolution times.

•By routing incoming messages to the agent with the fewest messages waiting for processing, the load is balanced across the agents.

•Routing messages to the agent who previously processed the message may result in disparity in individual agent utilizations, causing a gap between the best and worst performance.


CONCLUSIONSCONCLUSIONS

• Simulation, which has been used for Simulation, which has been used for modeling customer call centers, can also modeling customer call centers, can also be used to model the unique be used to model the unique characteristics of characteristics of customer contact centerscustomer contact centers

• Management decisions regarding routing Management decisions regarding routing and priority schemes can impact and priority schemes can impact performanceperformance

• The queuing model consistently The queuing model consistently underestimates the system performance underestimates the system performance measures.measures.


Scheduling Email Scheduling Email Processing to Reduce Processing to Reduce Information Overload Information Overload

and Interruptionsand Interruptions


Prior relevant research on Prior relevant research on Email Overload & Email Overload &

interruptions researchinterruptions research• First reportedFirst reported by Peter Denningby Peter Denning (1982),Later by (1982),Later by Hiltz, et al. (1985), Whittaker, et al.(1996) and Hiltz, et al. (1985), Whittaker, et al.(1996) and many…many…

• According to According to distraction theorydistraction theory, interruption is “an , interruption is “an externally generated, randomly occurring, discrete externally generated, randomly occurring, discrete eventevent that breaks continuity of cognitive focus on a that breaks continuity of cognitive focus on a primary task“ (Corragio 1990; Tétard F. 2000). primary task“ (Corragio 1990; Tétard F. 2000). – Research done in HCI is rich but in MS/OR??? Research done in HCI is rich but in MS/OR???

• Research that looks at the problem of information Research that looks at the problem of information overload and interruptions simultaneously is scarce. overload and interruptions simultaneously is scarce. (Speier et al.1999, Jackson, et al., 2003, 2002, 2001), (Speier et al.1999, Jackson, et al., 2003, 2002, 2001), Venolia et al. (2003) Venolia et al. (2003)


Research ModelResearch Model

*Utilization: Probability of a knowledge worker being busy (λ/µ)

Resource utilization change

Task Complexity mix

Task Completion time

Resource utilization

Number of Interruptions per task

Interrupt arrival pattern

Email Policy


Our approach- Our approach- SIMULATIONSIMULATION

Interrupt arrives

IL + Interrupt processing

Interrupt departs

Recall time- RLPre-processing Post-processing

Policies that we are comparing :-Triage: (C1-morning, C1-Afternoon)Scheduled: (C2, C4, C8(Jackson et al. 2003))Flow (continuous): C

Phases of task processing(Miyata & Norman, 1986):-

•Planning •Execution •Evaluation


Notations usedNotations used

ii Task types- simple (S), complex (C) , email(E)Task types- simple (S), complex (C) , email(E)thus, i = {S, C, E}thus, i = {S, C, E}

PrimPrim Primary task, which is either a simple or a complex task.Primary task, which is either a simple or a complex task.Prim ={S, C}Prim ={S, C}

ρρ Minimum utilization of knowledge worker Minimum utilization of knowledge worker ii arrival rate for task of type i arrival rate for task of type i μμii Service rates for task of type iService rates for task of type iPP Planning phase of a taskPlanning phase of a taskExeExe Execution phase of taskExecution phase of taskEvalEval Evaluation phase of taskEvaluation phase of taskStageStage Current stage of task processing. Thus Stage= {P, Exe, Eval}Current stage of task processing. Thus Stage= {P, Exe, Eval}IIPrim-StagePrim-Stage Interruption lag for a primary task at a particular processing Interruption lag for a primary task at a particular processing

stage.stage.

RRPrim-StagePrim-Stage Resumption lag for a primary task at a particular processing Resumption lag for a primary task at a particular processing stage.stage.

RRPrimPrim, Ĭ, ĬPrimPrim Mean Resumption lag & Mean Interruption lag for a primary Mean Resumption lag & Mean Interruption lag for a primary task task


Mathematical conditions and Mathematical conditions and equationsequations

Following conditions were Following conditions were implemented in the simulation implemented in the simulation model:model:


Mathematical conditions & Mathematical conditions & equations equations

0 ≤ ≤ 1


Mathematical conditions & Mathematical conditions & equationsequations

Parameters chosen for Beta distribution are k=2 & l =1For positive linear relationship between ε and .


Model ImplementationModel ImplementationSn, Cn- new simple & complex task Si, Ci – interrupted simple & complex task E – Email (Interrupt)

9

Profile Profile plotsplots

Marginal Means of % change in Utilization

POLICY

ContinuCJacksonC4C2C1_mornC1_after

Est

ima

ted

Ma

rgin

al M

ea

ns

14

12

10

8

6

4

2

0

Work Loadlevel

high

low

medium


POLICY


Est

ima

ted

Ma

rgin

al M

ea

ns

14

12

10

8

6

4

2

0

Email Arrival patter

100-0

80-20


POLICY


Est

ima

ted

Ma

rgin

al M

ea

ns

14

12

10

8

6

4

2

0

task complexity

L-simple, M-complex

M-simple, L-complex

RU

Number of interruptions per simple task

POLICY


Est

ima

ted

Ma

rgin

al M

ea

ns

70

60

50

40

30

20

10

0

workload level

high

low

medium

RU


POLICY


Est

ima

ted

Ma

rgin

al M

ea

ns

60

50

40

30

20

10

0

task complexity

L-simple, M-complex

M-simple, L-complex


POLICY


Est

ima

ted

Ma

rgin

al M

ea

ns

60

50

40

30

20

10

0

email arrival patter

100-0

80-20

% increase in utilization

# of interruptions per simple or complex task

Results

Policy C4 resulted in (1) minimum percentage increase in utilization (2) minimum # of interruptions per simple task(3) minimum # of interruptions per complex

tasks Result holds under:• The work environment requires high, medium

or low utilization of knowledge worker, or• The work environment requires processing of

either more simple or more complex tasks, or• For both arrival patterns (Pattern I: when all

email arrived during office hrs, Pattern II: when 80% emails arrived during office hrs).


Practical implicationsPractical implications

• If other tasks are more important If other tasks are more important and email communication is and email communication is secondary !secondary !• Process emails 4 times a day with each Process emails 4 times a day with each

processing block not exceeding 45 min.processing block not exceeding 45 min.

• Is timely email processing a survival Is timely email processing a survival issue for your kind of organization?issue for your kind of organization?– Use flow (continuous) policyUse flow (continuous) policy


A Stochastic Programming A Stochastic Programming Approach to Managing Approach to Managing

Email OverloadEmail Overload


Email OverloadEmail Overload

• Inability to respond to all email Inability to respond to all email in a timely in a timely mannermanner

• The knowledge worker must not only take The knowledge worker must not only take into consideration the current email that is in into consideration the current email that is in need of processing and the timeliness of this need of processing and the timeliness of this email, but he or she must also consider what email, but he or she must also consider what futurefuture email demands may be on the horizon. email demands may be on the horizon.

• Stochastic Programming takes possible Stochastic Programming takes possible FUTURE scenarios into considerationFUTURE scenarios into consideration

• All other efforts consider only the present All other efforts consider only the present state state


An Illustrative Example: An Illustrative Example:

Optimizing Email ProcessingOptimizing Email Processing

• With respect to email processing, the optimization With respect to email processing, the optimization involves maximizing the utility or value of the emails involves maximizing the utility or value of the emails that are processed. that are processed.

• The optimal solution must take into consideration The optimal solution must take into consideration that the utility of a processed email may decrease that the utility of a processed email may decrease with time. with time.

• The optimal solution must also consider the potential The optimal solution must also consider the potential arrival of different types of email in the future. arrival of different types of email in the future.

• The decision variables correspond to whether or not The decision variables correspond to whether or not to process an email in a given stage (time frame). to process an email in a given stage (time frame).

• The stochastic parameters include the potential The stochastic parameters include the potential arrival of various types of emails.arrival of various types of emails.


An Illustrative Example: An Illustrative Example: Optimizing Email ProcessingOptimizing Email Processing

• Beginning Inbox (i = type, j = age)Beginning Inbox (i = type, j = age)

jj

ii44 33 22 22 00

55 88 44 00 11



• Utility of email processed (i = type, j Utility of email processed (i = type, j = age)= age)

jj

ii1010 1010 66 33 00

88 77 00 -4-4 -4-4



• Arrival scenarios (number of type i Arrival scenarios (number of type i email arriving)email arriving)

Arrival ScenariosArrival Scenarios

ProbabilitieProbabilitiess .2.2 .2.2 .1.1 .5.5

ii33 22 55 88

22 33 55 44



• Time needed to process email (days)Time needed to process email (days)

ii.1.1

.1.1


FormulationsFormulations

• LP – Single-periodLP – Single-period

• LP – Multi-periodLP – Multi-period

• SP – Perfect InformationSP – Perfect Information

• SP – Here and NowSP – Here and Now


FormulationFormulation

Sets and IndicesSets and Indices• TT is the set of the different days under is the set of the different days under

considerationconsideration• II is the set of possible types of email messagesis the set of possible types of email messages• JJ is the set of possible ages of an email message in is the set of possible ages of an email message in

daysdays• QQ is the set of possible arrival scenariosis the set of possible arrival scenarios

• t = 1..4t = 1..4 denotes the day under considerationdenotes the day under consideration• i = 1..2i = 1..2denotes the type of email messagedenotes the type of email message• j = 1..5j = 1..5denotes the age of an email messagedenotes the age of an email message• q = 1..64q = 1..64 denotes the arrival scenariodenotes the arrival scenario


SP FormulationSP Formulation(Here and Now)(Here and Now)

ParametersParameters• NNtt = 1,i,j,q= 1,i,j,q This represents the number of email of type i This represents the number of email of type i

that are j that are j days old on day one. This represents the days old on day one. This represents the

beginning beginning inbox.inbox.• AAt,i,qt,i,q This represents the number of arriving email of This represents the number of arriving email of

type i, given scenario q.type i, given scenario q.• UUi,ji,j This represents the utility or value of an This represents the utility or value of an

email of type i, email of type i, having an age of j. having an age of j.

• PPqq This represents the probability of scenario q.This represents the probability of scenario q.• DDii This represents the time needed, in days, to This represents the time needed, in days, to

process process an email of type i.an email of type i.


SP Formulation (cont.)SP Formulation (cont.)(Here and Now)(Here and Now)

VariablesVariables• XXt,i,j,qt,i,j,q This represents the number of email that are This represents the number of email that are

processed on processed on day t, that are of type i and have an age of day t, that are of type i and have an age of j, given scenario j, given scenario q.q.

• NNt,i,j,qt,i,j,q This represents the number of email of type i that This represents the number of email of type i that are j days are j days

old on day t, given scenario q.old on day t, given scenario q.

Objective FunctionObjective Function• Max ΣMax ΣqqΣΣiiΣΣjj P Pqq X Xt,i,j,q t,i,j,q UUi,ji,j


SP Formulation (cont.)SP Formulation (cont.)(Here and Now)(Here and Now)

ConstraintsConstraints• NNt,i,j,qt,i,j,q = N = Nt-1,i,j-1,qt-1,i,j-1,q – X – Xt-1,i,j-1,qt-1,i,j-1,q t > 1, i = 1..2, j > 1, q = 1..64t > 1, i = 1..2, j > 1, q = 1..64• NNt,i,j,qt,i,j,q = A = At,i,qt,i,q t > 1, I – 1..2, j = 1, q = 1..64t > 1, I – 1..2, j = 1, q = 1..64• XXt,i,j,qt,i,j,q <= N <= Nt,i,j,qt,i,j,q t = 1..4, i = 1..2, j < 5, q = 1..64t = 1..4, i = 1..2, j < 5, q = 1..64• XXt,i,j,qt,i,j,q = N = Nt,i,j,qt,i,j,q t = 1..4, i = 1..2, j = 5, q = 1..64t = 1..4, i = 1..2, j = 5, q = 1..64• ΣΣii Σ Σjj X Xt,i,j,qt,i,j,q D Dii <= 1 <= 1 t = 1..4, i = 1..2, j = 1..5, q = 1..64 t = 1..4, i = 1..2, j = 1..5, q = 1..64

• XXt,i,j,qt,i,j,q = X = Xt,i,j,q+1t,i,j,q+1 t = 1, i = 1..2, j = 1..5, q < 63t = 1, i = 1..2, j = 1..5, q < 63• XXt,i,j,qt,i,j,q = X = Xt,i,j,q+1t,i,j,q+1 t = 2, i = 1..2, j = 1..5, q < 63t = 2, i = 1..2, j = 1..5, q < 63• XXt,i,j,qt,i,j,q = X = Xt,i,j,q+1t,i,j,q+1 t = 3, i = 1..2, j = 1..5, q < 63t = 3, i = 1..2, j = 1..5, q < 63• XXt,i,j,qt,i,j,q = X = Xt,i,j,q+1t,i,j,q+1 t = 4, i = 1..2, j = 1..5, q < 63t = 4, i = 1..2, j = 1..5, q < 63


Sample ResultsSample Results

FCFSFCFS

LP – LP – SingleSingle--periodperiod

LP – LP – Multi-Multi-periodperiod

SP – SP – PerfecPerfect t Infor-Infor-matiomationn

SP – SP – Here & Here & NowNow

Total Total

4-day 4-day UtilityUtility

120.68120.68 166.60166.60 264.00264.00 258.78258.78 218.00218.00


ExtensionsExtensions

• More realistic modeling of the problem More realistic modeling of the problem needed:needed:– Differences in service times for different email Differences in service times for different email

classesclasses• Identification of utilitiesIdentification of utilities• Automatic identification of email Automatic identification of email

categoriescategories• Real time solution of the SPR problemReal time solution of the SPR problem

before the Inbox is shown to the userbefore the Inbox is shown to the user– Another optimization challengeAnother optimization challenge


Future researchFuture research

• Perform these studies in experimental or field Perform these studies in experimental or field settings.settings.– Use measures of Perceived Information Use measures of Perceived Information

Overload (NASA-TLX, SWAT)Overload (NASA-TLX, SWAT)• More realistic modeling by incorporating email More realistic modeling by incorporating email

characteristics as well as knowledge worker characteristics as well as knowledge worker differencesdifferences

• Single vs. multi-user settings/Network modelingSingle vs. multi-user settings/Network modeling• Nonlinear formulationsNonlinear formulations• Stochastic knapsackStochastic knapsack

A really rich domain for OR/MS modeling!!!A really rich domain for OR/MS modeling!!!

Beyond Spam: OR/MS Modeling Opportunities for Email Response Management Ramesh Sharda Robert A....

Documents

Transcript of Beyond Spam: OR/MS Modeling Opportunities for Email Response Management Ramesh Sharda Robert A....