MGT 560 Queuing System Simulation Stochastic Modeling Victor

Steps in Simulation Process 1. 2. 3. 4. 5. 6. 7. 8. 9. Define

Advantages of Simulation • • Straightforward and flexible Can analyze complex real-world situations Can

Limitations of Simulation • Expensive and time consuming • Does not generate optimal solutions

Types of Queuing Systems Single channel; Single phase Channel – the number of parallel

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

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

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

Data Collection • Source of customers – Infinite – Finite Victor E. Sower, Ph.

Data Collection • Arrival Rate/Interarrival Time – Arrival Rate (Poisson) – Interarrival Time (Exponential)

Data Collection • Service Rate/Service Time – Service Rate (Poisson) – Service Time (Exponential)

Data Collection • Queue Discipline – FCFS – LIFO – Random – Others Victor

Data Collection • Queue Length – Infinite – Finite • Balking Victor E. Sower,

System Operating Characteristics Results from Model • • • L Avg. no. of customers

System Considerations • • Waiting line costs Service quality Psychology of waiting Balking Victor

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

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

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

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

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

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

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

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

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

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

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

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

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

System Operating Characteristics Results from Model • • • L Avg. no. of customers in system Lq Avg. no. of customers in the queue W Avg. time customer spends in system Wq Avg. time customer spends in queue p Utilization rate Victor E. Sower, Ph. D. , C. Q. E. 2007

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