Technical Supplement 2 Waiting Line Models McGraw-Hill/Irwin Copyright © 2011 by the McGraw-Hill Companies, Inc. All rights reserved.

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Technical Supplement 2 Waiting Line Models McGraw-Hill/Irwin Copyright © 2011 by the McGraw-Hill Companies, Inc. All rights reserved.

Learning Objectives 1. Explain the importance of waiting line models to service operations management. 2. Give a real example of a decision modeled with waiting line methods. 3. Demonstrate the measures important in waiting line situations. 4. Show how waiting line models can be solved. TS2-2

New York City Justice System Severe backlog Need to reduce average wait to 24 hours Analysis –Identification of system –Data gathering –Modeling – display estimated performance Used Monte Carlo simulation for waiting line problem due to complexity TS2-3

Example Waiting Line Systems ArrivalsQueueService Barber shopCustomersAssigned numbers Barbers Car washCarsLineWashers Computer center JobsStacked jobsCPU Shipping dockShipsEmpty or loadCranes Telephone system CallsLinesSwitchboard TS2-4

Waiting Line Systems COMPONENTS –Arrivals Calling population  limit K Distribution –Discipline FIFOLIFOpriority –Services Number of servers Distribution –Departures TS2-5

Waiting Line Decisions Steady State: –performance at equilibrium Transient State: –temporary characteristics on way to equilibrium Total Cost –balance Cost of providing service Cost of waiting TS2-6

Source of Waiting Line System Performance Differences Variance –In arrival pattern –In service times If constant system, calculations simple If variance, system will have idle periods balanced with periods where work is waiting Need to overbuild if variance exists TS2-7

Impact of Busy System If rate of arrival  rate of service capacity –System is degenerate (cannot get work done) If rate of arrival is close to service capacity –System will experience long waiting lines at some times should variance exist TS2-8

System Measures P n probability of n in the system W q average time waiting for service L q average number waiting for service Waverage time in system (including service) Laverage number in system (including being served)  system utilization rate TS2-9

Waiting Line Model Assumptions Arrival distribution Service distribution Number of servers Queue discipline Maximum number of customers in system Number of potential customers TS2-10

Arrival Distributions MExponential (or Poisson) DDeterministic (constant) E k Erlang (shape parameter k) GIGeneral independent Others = number of arrivals per unit time TS2-11

Service Distributions MExponential (or Poisson) DDeterministic (constant) E k Erlang (shape parameter k) GSGeneral service Others  = Mean service time TS2-12

Other Assumptions Snumber of servers (clones) Queue discipline –FIFO (FCFS)first in, first out –LIFOlast in, first out –SIROrandom –GDgeneral distribution (other rules) Max Line Length Calling Population TS2-13

Standard Models M/M/1:FCFS/  /  M/D/1:FCFS/  /  constant service M/GS/1:FCFS/  /  normal service M/M/s:FCFS/  /  multiple servers M/M/1:FCFS/k/  max k in system M/M/s:FCFS/k/  /multiple servers, max k others TS2-14

Waiting Line Models Summary 1.Many service operations involve waiting lines. 2. Waiting line models good to understand counterintuitive behavior of such systems. 3. For complex systems, use simulation. TS2-15