1. MGT 560 Queuing System Simulation Stochastic Modeling © Victor E. Sower, Ph.D., C.Q.E. 2007

2. Steps in Simulation Process 1.Define problem 2.Define important variables in problem 3.Collect data 4.Construct mathematical model 5.Validate model 6.Define experiments to run 7.Run experiments 8.Consider results (possible model modification) 9.Decide on course of action Victor E. Sower, Ph.D., C.Q.E. 2007

3. Advantages of Simulation Straightforward and flexible Can analyze complex real-world situations Can use any distributions—not just standard ones Time compression Can address “what-if” questions Off-line Can study interactions of individual variables and components Victor E. Sower, Ph.D., C.Q.E. 2007

4. Limitations of Simulation Expensive and time consuming Does not generate optimal solutions The results from the model are limited by the quality of the design of the model Each simulation model is unique to a particular problem Victor E. Sower, Ph.D., C.Q.E. 2007

5. Types of Queuing Systems Single channel; Single phase Victor E. Sower, Ph.D., C.Q.E. 2007 Channel – the number of parallel servers Phase – the number of servers in sequence

6. Types of Queuing Systems Multiple channel; Single phase Victor E. Sower, Ph.D., C.Q.E. 2007

7. Types of Queuing Systems Single channel/Multiple phase Victor E. Sower, Ph.D., C.Q.E. 2007

8. Types of Queuing Systems Multiple channel/Multiple phase Victor E. Sower, Ph.D., C.Q.E. 2007

9. Data Collection Source of customers – Infinite – Finite Victor E. Sower, Ph.D., C.Q.E. 2007

10. Data Collection Arrival Rate/Interarrival Time – Arrival Rate (Poisson) – Interarrival Time (Exponential) Victor E. Sower, Ph.D., C.Q.E. 2007

11. Data Collection Service Rate/Service Time – Service Rate (Poisson) – Service Time (Exponential) Victor E. Sower, Ph.D., C.Q.E. 2007

12. Data Collection Queue Discipline – FCFS – LIFO – Random – Others Victor E. Sower, Ph.D., C.Q.E. 2007

13. Data Collection Queue Length – Infinite – Finite Balking Victor E. Sower, Ph.D., C.Q.E. 2007

14. System Operating Characteristics Results from Model LAvg. no. of customers in system L q Avg. no. of customers in the queue WAvg. time customer spends in system W q Avg. time customer spends in queue pUtilization rate Victor E. Sower, Ph.D., C.Q.E. 2007

15. System Considerations Waiting line costs Service quality Psychology of waiting Balking Victor E. Sower, Ph.D., C.Q.E. 2007