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MGT 560 Queuing System Simulation

MGT 560 Queuing System Simulation. Stochastic Modeling. Steps in Simulation Process. 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)

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MGT 560 Queuing System Simulation

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

  2. Steps in Simulation Process • 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

  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 SystemsSingle 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

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

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

  8. Types of Queuing SystemsMultiple 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 CharacteristicsResults 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

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

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