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Simulation & Modeling - Smilulation Queuing System
This document discusses simulation examples of queueing systems. It describes three key steps in simulations: 1) determining input characteristics, 2) constructing a simulation table, and 3) generating input values and evaluating responses over repetitions. It then provides details on simulating a single-channel queue, including modeling arrivals and services as probability distributions and tracking events in a simulation table. The document concludes with an example simulation of customers arriving at and being served by a single checkout counter.
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Simulation & Modeling - Smilulation Queuing System
- 1. Simulation Examples Threesteps of the simulations Determine the characteristics of each of the inputs to the simulation. Quite often, these may be modeled as probability distributions, either continuous or discrete. Construct a simulation table. Each simulation table is different, for each is developed for the problem at hand. For each repetition i, generate a value for each of the p inputs, and evaluate the function, calculating a value of the response yi. The input values may be computed by sampling values from the distributions determined in step 1. A response typically depends on the inputs and one or more previous responses.
- 2. Inputs Response Xi1 Xi2Xip yiRepetition s Xij The simulation table provides a systematic method for tracking system state over time. … … 1 2 n · · ·
- 3. 2.1 Simulation ofQueueing Systems (1) A queuing system is described by its calling population, the nature of the arrivals, the service mechanism, the system capacity, and the queuing discipline. Calling population Waiting Line Server Fig. 2.1 Queueing System
- 4. 2.1 Simulation ofQueueing Systems (2) In the single-channel queue, the calling population is infinite. If a unit leaves the calling population and joins the waiting line or enters service, there is no change in the arrival rate of other units that may need service. Arrivals for service occur one at a time in a random fashion. Once they join the waiting line, they are eventually served. Service times are of some random length according to a probability distribution which does not change over time. The system capacity has no limit, meaning that any number of units can wait in line. Finally, units are served in the order of their arrival (often called FIFO: First In, First out) by a single server or channel.
- 5. 2.1 Simulation ofQueueing Systems (3) Arrivals and services are defined by the distribution of the time between arrivals and the distribution of service times, respectively. For any simple single- or multi-channel queue, the overall effective arrival rate must be less than the total service rate, or the waiting line will grow without bound. In some systems, the condition about arrival rate being less than service rate may not guarantee stability
- 6. 2.1 Simulation ofQueueing Systems (4) System state : the number of units in the system and the status of the server(busy or idle). Event : a set of circumstances that cause an instantaneous change in the state of the system. In a single-channel queueing system there are only two possible events that can affect the state of the system. the arrival event : the entry of a unit into the system the departure event : the completion of service on a unit. Simulation clock : used to track simulated time.
- 7. 2.1 Simulation ofQueueing Systems (5) If a unit has just completed service, the simulation proceeds in the manner shown in the flow diagram of Figure 2.2. Note that the server has only two possible states : it is either busy or idle. Departure Event Remove the waiting unit from the queue Begin servicing the unit Begin server idle time Another unit waiting? YesNo Fig. 2.2 Service-just-completed flow diagram
- 8. 2.1 Simulation ofQueueing Systems (6) The arrival event occurs when a unit enters the system. The unit may find the server either idle or busy. Idle : the unit begins service immediately Busy : the unit enters the queue for the server. Arrival Event Server busy? Unit enters queue for service Unit enters service YesNo Fig. 2.3 Unit-entering-system flow diagram
- 9. 2.1 Simulation ofQueueing Systems (7) Fig. 2.4 Potential unit actions upon arrival Fig. 2.5 Server outcomes after service completion
- 10. 2.1 Simulation ofQueueing Systems (8) Simulations of queueing systems generally require the maintenance of an event list for determining what happens next. Simulation clock times for arrivals and departures are computed in a simulation table customized for each problem. In simulation, events usually occur at random times, the randomness imitating uncertainty in real life. Random numbers are distributed uniformly and independently on the interval (0, 1). Random digits are uniformly distributed on the set {0, 1, 2, … , 9}. The proper number of digits is dictated by the accuracy of the data being used for input purposes.
- 11. 2.1 Simulation ofQueueing Systems (9) Pseudo-random numbers : the numbers are generated using a procedure. Table 2.2. Interarrival and Clock Times Assume that the times between arrivals were generated by rolling a die five times and recording the up face.
- 12. 2.1 Simulation ofQueueing Systems (10) Table 2.3. Service Times Assuming that all four values are equally likely to occur, these values could have been generated by placing the numbers one through four on chips and drawing the chips from a hat with replacement, being sure to record the numbers selected. The only possible service times are one, two, three, and four time units.
- 13. 2.1 Simulation ofQueueing Systems (11) The interarrival times and service times must be meshed to simulate the single-channel queueing system. Table 2.4 was designed specifically for a single-channel queue which serves customers on a first-in, first-out (FIFO) basis.
- 14. 2.1 Simulation ofQueueing Systems (12) Table 2.4 keeps track of the clock time at which each event occurs. The occurrence of the two types of events(arrival and departure event) in chronological order is shown in Table 2.5 and Figure 2.6. Figure 2.6 is a visual image of the event listing of Table 2.5. The chronological ordering of events is the basis of the approach to discrete-event simulation described in Chapter 3.
- 15. 2.1 Simulation ofQueueing Systems (13) Figure 2.6 depicts the number of customers in the system at the various clock times.
- 16. 2.1 Simulation ofQueueing Systems (14) Example 2.1 Single-Channel Queue Assumptions • Only one checkout counter. • Customers arrive at this checkout counter at random from 1 to 8 minutes apart. Each possible value of interarrival time has the same probability of occurrence, as shown in Table 2.6. • The service times vary from 1 to 6 minutes with the probabilities shown in Table 2.7. • The problem is to analyze the system by simulating the arrival and service of 20 customers. Checkout Counter Arrival Departure
- 17. 2.1 Simulation ofQueueing Systems (15)
- 18. 2.1 Simulation ofQueueing Systems (16) Example 2.1 (Cont.) A simulation of a grocery store that starts with an empty system is not realistic unless the intention is to model the system from startup or to model until steady-state operation is reached. A set of uniformly distributed random numbers is needed to generate the arrivals at the checkout counter. Random numbers have the following properties: The set of random numbers is uniformly distributed between 0 and 1. Successive random numbers are independent. Random digits are converted to random numbers by placing a decimal point appropriately. The rightmost two columns of Tables 2.6 and 2.7 are used to generate random arrivals and random service times.
- 19. 2.1 Simulation ofQueueing Systems (17) Example 2.1 (Cont.) Table 2.8 The first random digits are 913. To obtain the corresponding time between arrivals, enter the fourth column of Table 2.6 and read 8 minutes from the first column of the table.
- 20. 2.1 Simulation ofQueueing Systems (18) Example 2.1 (Cont.) Table 2.9 The first customer's service time is 4 minutes because the random digits 84 fall in the bracket 61-85
- 21. 2.1 Simulation ofQueueing Systems (19) Example 2.1 (Cont.) The essence of a manual simulation is the simulation table. The simulation table for the single-channel queue, shown in Table 2.10, is an extension of the type of table already seen in Table 2.4. Statistical measures of performance can be obtained form the simulation table such as Table 2.10. Statistical measures of performance in this example. Each customer's time in the system The server's idle time In order to compute summary statistics, totals are formed as shown for service times, time customers spend in the system, idle time of the server, and time the customers wait in the queue.
- 23. 2.1 Simulation ofQueueing Systems (20) The probability that a customer has to wait in the queue : 0.65 65.0 20 13 )( === customersofnumberstotal waitwhocustomersofnumber waityprobabilit The fraction of idle time of the server : 0.21 21.0 86 18 === simulationoftimeruntotal serveroftimeidletotal serveridleofyprobabilit The probability of the server being busy: 0.79 (=1-0.21) Example 2.1 (Cont.) The average waiting time for a customer : 2.8 minutes (min)8.2 20 56 === customersofnumberstotal queueinwaitcustomerstimetotal timewaitngaverage
- 24. 2.1 Simulation ofQueueing Systems (21) Example 2.1 (Cont.) The average service time : 3.4 minutes (min)4.3 20 68 === customersofnumberstotal timeservicetotal timeserviceaverage This result can be compared with the expected service time by finding the mean of the service-time distribution using the following equation. ∑ ∞ = = 0 )()( s sspSE (min)2.3)05.0(6)10.0(5)25.0(4)30.0(3)20.0(2)10.0(1)( =+++++=SE The expected service time is slightly lower than the average service time in the simulation. The longer the simulation, the closer the average will be to )(SE
- 25. 2.1 Simulation ofQueueing Systems (22) The average time between arrivals : 4.3 minutes The average waiting time of those who wait : 4.3 minutes (min)3.4 13 56 === wiatwhocustomersofnumberstotal queueinwaitcustomerstimetotal waitwhothoseoftimewaitingaverage This result can be compared to the expected time between arrivals by finding the mean of the discrete uniform distribution whose endpoints are a=1 and b=8. (min)3.4 19 82 1 == − = arrivalsofnumbers arrivalsbetweentimesallofsum arrivalsbetweentimeaverage (min)5.4 2 81 2 )( = + = + = ba AE The longer the simulation, the closer the average will be to )(AE Example 2.1 (Cont.)
- 26. 2.1 Simulation ofQueueing Systems (23) The average time a customer spends in the system : 6.2 minutes Example 2.1 (Cont.) (min)2.6 20 124 === customersofnumberstotal systeminspendcustomerstimetotal systemtheinspendscustomertimeaverage average time customer spends in the system average time customer spends waiting in the queue average time customer spends in service = + ∴ average time customer spends in the system = 2.8 + 3.4 = 6.2 (min)
- 27. 2.4 Summary Thischapter introduced simulation concepts via examples in order to illustrate general areas of application and to motivate the remaining chapters. Ad hoc simulation tables were used in completing the example. Events in the tables were generated using uniformly distributed random numbers and, in one case, random normal numbers. The example illustrate the need for determining the characteristics of the input data, generating random variables from the input models, and analyzing the resulting response.