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Staffing and Routing in Large-Scale Service Systems with Heterogeneous-Servers. Mor Armony. Based on joint papers with Avi Mandelbaum and Amy Ward. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A A A A. Motivation: Call Centers. - PowerPoint PPT Presentation
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Staffing and Routing in Large-Scale Service Systems with Heterogeneous-
Servers
Mor Armony
Based on joint papers with Avi Mandelbaum and
Amy Ward
¸ < ¹
Motivation: Call Centers
The Inverted-V Model
NK
K
K 21
• Calls arrive at rate (Poisson process).
• K server pools.
• Service times in pool k are exponential with rate k
N1
1
¹ 2 > ¹ 1
¹ > ¹
Experienced employees on averageprocess requests faster than new hires.Gans, Mandelbaum and Shen (2007)
…
The Problem
Routing: When an incoming call arrives to an empty queue, which agent pool should take the call?
Staffing: How many servers should be working in each pool?
¹ 2 > ¹ 1
¹ > ¹
x = y
NK
K
K 21
N1
1
…
Background: Human Effects in Large-Scale Service
Systems
M/M/N
M/M/N+M+M/M/N+
M/M/N+M
M/M/N++
Halfin & Whitt ’81
Borst et al ’04
Garnett et al ’02
Mandelbaum & Zeltyn ’08
Talk Outline
• M/M/N+ (Armony ‘05)• M/M/N++M (Armony & Mandelbaum
’08)• M/M/N++☺ (Armony & Ward ’08)
The Problem: M/M/N+
¹ 2 > ¹ 1
¹ > ¹
x = y
NK
K
K 21
N1
1
…
Minimize C1(N1) + ::: + CK (NK )Subject to P (wait > 0) · ®;
Assumption: FCFS
For some routing policy
The Routing Problem
¹ 2 > ¹ 1
¹ > ¹
x = y
Minimize C1(N1) + ::: + CK (NK )Subject to P (wait > 0) · ®;
For some routing policy
• For N1=N2=1 optimal routing is of a threshold form (the slow server problem)
• For general N, structure of optimal routing is an open problem (de Vericourt & Zhou)
• The optimal preemptive policy is FSFP (Proof: Sample-path argument)
The Asymptotic RegimeHalfin-Whitt (QED)
¹ 2 > ¹ 1
¹ > ¹
x = y
oN
K
k kk
1
, As
X̂ ¸ = X ¸ ¡ N ¸p
N ¸hX̂ ¸
i += scaled queue length
hX̂ ¸
i ¡= scaled # of idle servers
NK
K
K 21
N1
1
…
Asymptotically Optimal Routing
Proposition: The non-preemptive routing policy FSF is asymptotically optimal
Proof: State-space collapse: in the limit faster servers are always busy.
The preemptive and non-preemptive policies are asymptotically the same
Note: Thresholds are not-needed: The Halfin-Whitt regime is different from the conventional
heavy- traffic regime (Teh & Ward ’02).
Asymptotically Feasible Region
Asymptotic Feasibility
• Proposition: Under FSF
if and only if
where
provided that
lim¸ ! 1
P (wait > 0) = ® 0· ®· 1;
¹ 1N1 + ¹ 2N2 + ::: + ¹ K NK ¼¸ + ±p
¸; 0 · ±· 1
®=·1+
(±=p
¹ 1)©(±=p
¹ 1)Á(±=
p¹ 1)
¸¡ 1
;
liminf¸ ! 1 N1=N > 0.
Asymptotically Optimal Staffing
• All solutions of the form
have approximately the same cost
• Let C=inf {C(N) | ¹1N1+…+¹KNk=¸}
• Definition (Asymptotic Optimality)1. N* Asymptotically Feasible and2. (C(N*)-C)/(C(N)- C) · 1 (in the limit)
¹ 1N1 + ¹ 2N2 + ::: + ¹ K NK = ¸ + ±p
¸; 0< ±< 1
Asymptotically Optimal Staffing
Staffing Example:Homogeneous Cost Function
• Problem:
• Solve:
• To obtain:
• Note:
Minimize C1Np1 + C2N
p2 + ::: + CK N p
K ; p> 1Subject to P (wait > 0) · ®; for some routing policy
Minimize C1N p1 + C2N
p2 + ::: + CK N p
K ; p> 1Subject to ¹ 1N1 + ¹ 2N2 + ::: + ¹ K NK ¸ ¸ + ±
p¸
Nk
Nj=
µ¹ k=Ck
¹ j =Cj
¶1=(p¡ 1)
(1)
¹ 1N1 + ¹ 2N2 + ::: + ¹ K NK = ¸ + ±p
¸ (2)
N1=N > 0
Summary: M/M/N+• Routing: FSF• Staffing: Square-root safety capacity (QED
regime as an outcome)• Under FCFS non-idling is asymptotically
optimal• For non-idling policies: min P(W>0) min EW• Outperforming M/M/N• Faster servers are never idle• All idleness is experienced by the slowest
servers
Adding Fairness
Fairness in Call Center
¹ 2 > ¹ 1
¹ > ¹
x = y
Call centers care aboutEmployee burnout and turnover.
Some call centers address fairness byrouting to the server that has idled the longest (LISF).
How does LISF perform?
Do any other fair policies perform better?
NK
K
K 21
N1
1
…
The Fairness Problem
¹ 2 > ¹ 1
¹ > ¹
x = y
Minimize C1(N1)+…+CK(NK)
Subject to:
E(Waiting time)· W
E[# of idle servers of pool k]=
fk
E[Total # of idle servers]
* f1 + f2 + … + fK = 1
Assumption: Non-idling
NK
K
K 21
N1
1
…
The Fairness Problem: Routing
Minimize E[Waiting Time]Subject to:
E[# of idle servers of pool k]= fk
E[Total # of idle servers]
Analysis: Sample-path arguments are not straightforward even if preemption is allowed.
¹ 2 > ¹ 1
¹ > ¹
x = y
MDP Approach: Routing(Assumption: non-idling)
Q=1 Q=2 Q=31,1
1,00,0
0,1
= 1+ 2 N1 = N2 = 1
2
1
1
2Pslow
Pfast
Infinite state space
Numeric Example
MDP as an LP
• Complexity: Polynomial in N, Exponential in K• Solution: Switching curve (Difficult to
characterize explicitly).• How does solution perform vs. LISF?• Staffing search: Too long!!!• Instead, we propose an asymptotic approach.
Threshold Routing Control
)0 and(with
levelsat control dA threshol
0
11
K
K
LN L
N L...L
x = y2
NL1 L3 L2
FSF
FSFw/opool 3 FSF w/o pool 2
0
FSFw/opool 4
Outline of Asymptotic Analysis
• Formulation of a Diffusion Control Problem (DCP)
• Solution of DCP: Multi-Threshold Control
• Note: Resulting Diffusion has Discontinuous Drift
• Policy Translation: Multi-Threshold Policy
• Policy Adjustment: -Threshold Policy
• Establishing Asymptotic Optimality
²-Threshold Policy
X
Death rate
slope ¹2
slope ¹1
L N
Asymptotic Performance (Simulation)
1 = 1, 2 = 2, = 1, = 1.5, 2 = 2 = 3, N1=300, N2=200, ¸=674
A Simulation Comparison of the Threshold and LWISF Policy
0.0000002.000000
4.0000006.000000
8.00000010.000000
12.000000
0 0.5 1
Slow Server Idleness Proportion
E[N
um
be
r o
f W
ait
ing
C
us
tom
ers
]
Threshold
LWISF
Literature Review
• MDP approach to constrained optimization– Gans and Zhou (2003), Bhulai and Koole (2003)
• The Limit Regime– Halfin and Whitt (1981)
• The Inverted V (and more general) Models– Tezcan (2006), Atar (2007), Atar & Shwartz
(2008), Atar, Shaki & Shwartz (2009), Tseytlin (2008)
- Gurvich and Whitt (2007)
• Customer / Flow Fairness literature– Harchol-Balter and Wierman (2003, 2007)– Jahn et al (2005) & Schulz and Stier-Moses (2006)
• Fairness literature in HRM
Summary
• Server Heterogeneity: Effect on Staffing and Routing
• Incorporation of customer abandonment
• Incorporation of server fairness
• Simple routing schemes (priorities and threshold) • Simple staffing schemes (square-root safety
staffing)
Further Research
• Multi-skill environment (ongoing with Kocaga)
• LWISF policy (ongoing with Gurvich)• Non-idling assumption• Incorporate abandonment
(M/M/N++M+☺)• Other fairness criteria• Server compensation schemes
Acknowledgement: Rami Atar, Ashish Goel, Itay Gurvich, Tolga Tezcan & Assaf Zeevi