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OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin [email protected]

OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin [email protected]

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Page 1: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

OPSM 301: Operations Management

Session 20:

Queue Management

Koç University

Zeynep [email protected]

Page 2: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

The Service Process

Customer Inflow (Arrival) Rate (Ri) ()– Inter-arrival Time = 1 / Ri

Processing Time Tp (unit load)– Processing Rate per Server = 1/ Tp (µ)

Number of Servers (c)– Number of customers that can be processed simultaneously

Total Processing Rate (Capacity) = Rp= c / Tp (cµ)

Page 3: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Operational Performance Measures

Flow time T= Tw + Tp (waiting+process)

Inventory I = Iw + Ip

Flow Rate R = Min (Ri, RpStable Process = Ri < Rp,, so that R = Ri

Little’s Law: I = R T, Iw = R Tw, Ip = R Tp

Capacity Utilization = Ri / Rp < 1

Safety Capacity = Rp – Ri

Number of Busy Servers = Ip= c = Ri Tp

waiting processing() Ri

e.g10 /hr

R ()

10 /hr

10 min, Rp=12/hrTw?

Page 4: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Summary: Causes of Delays and Queues

High Unsynchronized Variability in– Interarrival Times– Processing Times

High Capacity Utilization = Ri / Rp, or Low Safety Capacity Rs = Rp – Ri, due to

– High Inflow Rate Ri

– Low Processing Rate Rp = c/ Tp (i.e. long service time, or few servers)

Page 5: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

The Queue Length Formula

2ρ1

221)2(c

ρ piw

CVCVI

Utilization effect

Variability effectx

where Ri / Rp, where Rp = c / Tp, and

CVi and CVp are the Coefficients of Variation

(Standard Deviation/Mean) of the inter-arrival and processing times (assumed independent)

Page 6: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

VariabilityIncreases

AverageTime inSystem T

Utilization (ρ) 100%

Tp

Throughput- Delay Curve

Page 7: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

In words:

in high utilization case: small decrease in utilization yields large improvement in response time

this marginal improvement decreases as the slack in the system increases

Page 8: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Deriving Performance Measures from Queue Length Formula

Use the formula to find Iw

Tw = Iw /R

T = Tw + Tp

Ip = Tp R

I =Iw + Ip

Page 9: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

How can we reduce waiting?

Reduce utilization:– Increase capacity: faster servers, better process design, more

servers Reduce variability

– Arrival: Appointment system– Service:Standardization of processes, automation

We can control arrivals– Short lines (express cashiers)– Specific hours for specific customers– Specials (happy hour)

2ρ1

221)2(c

ρ piw

CVCVI

Page 10: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Example :Effect of pooling

4 Departments and 4 Departmental secretaries Request rate for Operations, Accounting, and Finance is 2

requests/hour Request rate for Marketing is 3 requests/hour Secretaries can handle 4 requests per hour Marketing department is complaining about the response time

of the secretaries. They demand 30 min. response time College is considering two options:

– Hire a new secretary– Reorganize the secretarial support

Assume inter-arrival time for requests and service times have exponential distribution (i.e. CV=1)

Page 11: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

11

Current SituationCurrent Situation

Accounting

Finance

Marketing

Operations

2 requests/hour

2 requests/hour

3 requests/hour

2 requests/hour

4 requests/hour

4 requests/hour

4 requests/hour

4 requests/hour

Page 12: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

12

Current Situation: waiting times

T =processing time+waiting time =0.25 hrs. + 0.25 hrs =0.5 hrs=30 min

Accounting, Operations, Finance:

Marketing:

75.03

25.2

25.275.01

75.0

2

11

0.75-1

75.0

1c ,75.04

3

22x2

w

w

T

I

25.02

5.0

5.05.01

5.0

2

11

0.5-1

5.0

1c ,5.04

2

22x2

R

IT

I

ww

w

T =processing time+waiting time =0.25 hrs. + 0.75 hrs =1 hr=60 min

Page 13: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

13

Proposal: Secretarial PoolProposal: Secretarial Pool

Accounting

Finance

Marketing

Operations

9 requests/hour

2

2

3

2

Arrival rate=R=9/hr Tp=1/4 hr, Rp=c/Tp=16/hr

Utilization=Ri/Rp=9/16

Page 14: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Proposed System: Secreterial pool

04.09

37.0

37.02

11

5625.01

5625.0

9R 4 5625.016

9

)14(2

R

IT

I

c

ww

w

T =processing time+waiting time =0.25 hrs. + 0.04 hrs =0.29 hr=17.4 min

In the proposed system, faculty members in all departments get their requests back in 17 minutes on the average. (Around 50% improvement for Acc, Fin, and Ops and 75% improvement for Marketing). Pooling improves waiting times by ensuring effective use of capacity

Page 15: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Server 1Queue 1

Server 2Queue 2

Server 1

Queue

Server 2

Effect of Pooling

Ri

Ri

Ri/2

Ri/2

Pooled service capacityreduces waiting

Page 16: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Examples of pooling in business

Consolidating back office work Call centers Single line versus separate queues

Page 17: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

The impact of task integration (pooling)

balances utilization... reduces resource interference... ...therefore reduces the impact of temporary bottlenecks there is more benefit from pooling in a high utilization and

high variability process pooling is beneficial as long as

• it does not introduce excessive variability in a low variability system

• the benefits exceed the task time reductions due to specialization

Page 18: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Intuition building exercise

Check the following website: Waiting Line Simulation (use internet explorer)

http://archive.ite.journal.informs.org/Vol7No1/DobsonShumsky/security_simulation.php

Run six different examples. Suggestion (you can use different numbers):– Arrival rate=9, service rate=10 , CV=0, CV=1, CV=2 CV=0.5– Arrival rate =9, service rate=12 CV=1 CV=0.5

write down the parameters and the average performance measures to observe the effect of utilization and variability on waiting times. Compare the simulation output with the results you find using formulas. Note the effect of variability and utilization.

18

Page 19: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Exercise: Example 1

19

An automated pizza vending machine heats and dispenses a slice of pizza in exactly 4 minutes. Customers arrive at a rate of one every 6 minutes with the arrival rate exhibiting a Poisson distribution.Determine:A) The average number of customers in line.B) The average total waiting time in the system.

Ri=1/6 per min=10/hr Tp=4 min, c=1 Rp =15/hr =10/15=0.66CVi=1, CVp=0

min84.7

064.010

66.0

customers 64.02

1

66.01

66.0 2

pw

ww

w

TTT

hrR

IT

I

Exercise: 1. What if we have a human server, with CV=1?2.What is the effect of buying a second machine?

Page 20: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Exercise Example 2: Computing Performance Measures

Given– Interarrival times: 10, 10, 2, 10, 1, 3, 7, 9, and 2 seconds

• Avg=6, stdev=3.937, Ri =1/6– Processing times: 7, 1, 7, 2, 8, 7, 4, 8, 5, 1 seconds

• Avg=5, stdev=2.8284– c = 1, Rp =1/5

Compute– Capacity Utilization r = Ri / Rp = 5/6=0.833– CVi = 3.937/6 = 0.6562– CVp = 2.8284/5 = 0.5657

Queue Length Formula– Iw = 1.5633

Hence– Tw = Iw / R = 9.38 seconds, and Tp = 5 seconds, so – T = 14.38 seconds, so– I = RT = 14.38/6 = 2.3966 customers in the system

Page 21: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Example 2:Effect of Increasing Capacity Assume an indentical server is added (c=2). Given

– Interarrival times: 10, 10, 2, 10, 1, 3, 7, 9, and 2• Avg=6, stdev=3.937, Ri =1/6

– Processing times: 7, 1, 7, 2, 8, 7, 4, 8, 5, 1• Avg=5, stdev=2.8284

– c = 2, Rp =2/5 Compute

– Capacity Utilization r = Ri / Rp = 0.4167– CVi = 3.937/6 = 0.6562– CVp = 2.8284/5 = 0.5657

Queue Length Formula– Iw = 0.07536

Hence– Tw = Iw / R = 0.45216 seconds, and Tp = 5 seconds, so – T = 5.45216 seconds, so– I = RT = 5.45216/6 = 0.9087 customers in the system

Page 22: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Capacity planning

A bank would like to improve its drive-in service by reducing waiting and transaction times. Average rate of customer arrivals is 30/hour. Customers form a single queue and are served by 4 windows in a FCFS manner. Each transaction is completed in 6 minutes on average. The bank is considering to lease a high speed information retrieval and communication equipment that would cost 30 TL per hour. The facility would reduce each teller’s transaction time to 4 minutes per customer.a. If our manager estimates customer cost of waiting in queue to be 20 TL per customer per hour, can she justify leasing this equipment?b. The competitor provides service in 8 minutes on average. If the bank wants to meet this standard, should it lease the new equipment?

Page 23: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Want to eliminate as much variability as possible from your processes: how?

specialization in tasks can reduce task time variability standardization of offer can reduce job type variability automation of certain tasks IT support: templates, prompts, etc. Incentives Scheduled arrivals to reduce demand variability Initiatives to smoothen arrivals

Page 24: OPSM 301: Operations Management Session 20: Queue Management Koç University Zeynep Aksin zaksin@ku.edu.tr

Want to reduce resource interference in your processes: how?

smaller lotsizes (smaller batches) better balanced line

by speeding-up bottleneck (adding staff, changing procedure, different incentives, change technology)

through cross-training eliminate steps buffers integrate work (pooling)