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simulation with arena

Questions and Answers of Simulation With Arena

from Chapter 3, comparing specialized serial vs. generalized parallel operations in environments of both high-variance and low-variance (no-variance, actually) service times. It appeared in Table
that the generalized parallel arrangement provided better service, though the improvement was much stronger in the high-variance service-time environment. For output performance measures, use both
(from just a single run) statistically justif ed?
Recall Exercise 3-13, which modif ed Model
to add 18% onto the individual-task times in the generalized integrated-work parallel conf guration of Model 3-3. Make a valid statistical comparison to see if the model of Exercise
differs signif cantly from the original specialized serial conf guration of Model 3-2. In other words, if you had to endure this 18% increase in task-processing times, should you still move from the
For the enhanced machine-repair model of Exercise 5-22, do a valid statistical analysis to defend your choice of the number of repair technicians to hire to minimize total average cost per hour. Make
Use Arena to simulate the news vendor problem of Section 2.7.1. Consider just the case q5 160, and run for 30 days to get an average daily prof t and 95% conf dence interval, as well as the
Generalize your Arena model from Exercise
to consider all f ve values of q in Section 2.7.1, in a single Arena run, using the same daily demand realization for each value of q .
In the outpatient clinic of Exercise 4-30, suppose you could add one unit of resource to any one of the f ve stations (receptionist, nurse, exam room, checkout administrator, or lab technician).
The outpatient clinic of Exercise
has received an economic-stimulus capital grant of $400,000 to expand service and reduce patients’ average total time in system. They can allocate these funds in any way they like to add any number
Armed now with the statistical-analysis tools of this chapter, do a better job with Exercise 4-25. Stick with 100 replications for each of the two scenarios (with AJ as in Exercise 4-23, and without
that was included in Exercise 4-25), but expand the output performance measures to the mean total time in system of parts sorted according to each of: (a) Part As, regardless of how/where they exit
In Section 6.6, we chose both the starting point (“Suggested Value” in OptQuest) and the number of scenarios (“Simulations” in OptQuest) allowed pretty much arbitrarily. Experiment with
the Suggested starting values we used for the six Controls in the order listed there were (3, 3, 3, 3, 3, 29); consider this as well as (0, 0, 0, 0, 0, 26) and (5, 5, 5, 5, 5, 50). And for the number
Exercises 4-31, 4-32, and
considered three different versions of a grocery-store checkout area. Do a better of job of comparing them, on the basis of average total time in system of customers, using the Process Analyzer. Make
Make 25 replications of the race model from Exercise 4-35, and report 95% conf dence intervals on the expected f rst- and last-place f nish times. Put a text box in your model with these conf dence
In Exercise 4-7 (with a reprocess probability of 8%), is the run long enough to generate a batch-means-based conf dence interval for the steady-state expected average cycle time? Why or why not? If
Modify your solution for Exercise 5-2 to include transfer times between part arrival and the f rst machine, between machines, and between the last Machine 1 and the system exit. Assume all part
Using the model from Exercise 7-2, change the processing time for the second pass on Machine 1 to TRIA(6.7, 9.1, 13.6) using Sequences to control the f ow of parts through the system and the
A part arrives every 10 minutes to a system having three workstations (A, B, and C), where each workstation has a single machine; the f rst part arrives at time 0. There are four part types, each
Modify your solution for Exercise 7-4 to use the Expressions feature for determining the processing times (rather than assigning them in the Sequence data module). Run for a single replication of
Modify your solution for Exercise 7-5 so that all parts follow the same path through the system: Workstation A – Workstation B – Workstation C. If a part does not require processing at a
Three types of customers arrive at a small airport: check baggage (30%, that is, for each arriving customer there is a 0.30 probability that this is a “check-baggage” customer), purchase tickets
Parts arrive at a four-machine system according to an exponential interarrival distribution with mean 10 minutes. The four machines are all different, and there’s just one of each. There are f ve
Modify your solution for Exercise 7-8 to include the travel times that are move-specifc. The travel times are given here as the parameters for a triangular distribution (in minutes). Compare your
Modify your solution to Exercise 4-21 to use sequences to control the f ow of parts through the system. (HINT: Reset the value of Entity.Jobstep or IS.) Compare your results with those from Exercise
Modify Model 7-1 to account for acquiring a new customer, in addition to the one supplying the existing three part types. This new customer will supply two new types of parts—call them Type 4 and
Modify your solution to Exercise 5-2 to use Sequences to control the f ow of parts through the system. Also add a transfer time between arrival and the f rst machine, between both machines, and
Modify your solution to Exercise 7-12 to account for a 20% increase in processing time when the part returns to the f rst machine for its last operation. View this as a terminating simulation, and
In Exercise 7-7, suppose that you had the option of adding one agent to the system, and could add that agent to any one of the check-bag counter, the gate, the ticket-buying counter, or the X-ray
Change your model for Exercise
to include fork trucks to transport the parts between stations. Assume that there are two fork trucks that each travel at 85 feet per minute. Loading or unloading a part by the fork truck requires
Change your model for Exercise 7-4 to use nonaccumulating conveyors to transfer the parts between stations. Assume that there is a single conveyor that starts at the arrive area and continues to
Change your model for Exercise 5-2 to use a fork truck (45 feet/minute) for transportation of parts in the system. Assume that the parts arrive at an incoming dock and exit at a second dock.
Using the model from Exercise 8-3, set the number of transporters to four and make three runs using transporter selection rules of Smallest Distance, Largest Distance, and Cyclical. Run your
Modify Model 4-3 to include the use of a single truck to transfer parts from the two prep areas to the sealer. Assume that the distance between any pair of the three stations is 100 feet and that
Modify Model 4-3 to include the use of two conveyors to transfer parts from the two prep areas to the sealer. Both conveyors are 100 feet long and are made up of 20 cells of 5 feet each. The conveyor
A prototype of a new airport bag-screening system is currently being designed, as in the following f gure. Bags arrive to the system with interarrival times of EXPO(0.25) (all times are in minutes),
Develop a model of a cross-dock system that groups and transfers material for further shipment. This facility has f ve incoming docks and three outgoing docks. Trucks arrive at each of the incoming
A military ground force maintains a forward position and a rear depot 22 miles away to supply it. There is a single commodity that is consumed at the forward position and resupplied to it from the
Starting with Model 10-1, modify the logic to store the time between arrivals and the processing time (excluding any waiting time) in attributes. Then use the ReadWrite module from the Advanced
Start with Model 10-2 and use the data f le you generated in Exercise 10-1 to specify both call-arrival logic and call-handling time. Note that since the data are time between arrivals rather than
Build a simple, single-server queueing model with entity interarrival times of EXPO(0.25) minutes. Using the ReadWrite module, prompt and query at the beginning of the simulation run for the
Create the model described in Exercise 10-3, replacing the ReadWrite modules with a VBA form that’s displayed at the beginning of the simulation run.
Using the single-server model from Exercise 10-4, add logic to play a sound or display a message whenever the number of entities in the service queue exceeds some threshold value. Allow the modeler
Present a VBA form at the end of the simulation run, reporting the average and maximum queue lengths for the product queues in Model 10-1. If you have a charting kel01315_ch10_423-478.indd 477
Create a model that writes 1,000 records to a f le. Each record will include the record number and ten random samples, each between 0 and 100,000. In Run > Setup > Reports , disable the normal model
Build a discrete-event model that changes the value of the volume in a tank as described for Model 11-2b using a maximum step size of 0.01 minutes. Record time-persistent statistics on the volume in
The owner of a franchise of gas stations is interested in determining how large the storage tank should be at a new station. Four gas pumps, all dispensing the same grade of fuel, will be installed
to incorporate the availability of coal in the storage yard so that barges will wait at the dock until coal is available for loading. Compare the average and maximum number of barges waiting and
O’Hare Candy Company, maker of tasty sweets, is preparing to install a new licorice production facility and needs to determine the rates at which equipment should run. In particular, they are
Hope Bottling Company operates a bottling plant, handling many types of products. They are interested in analyzing the effective capacity of an orange juice bottling line as part of their plans
For the soaking-pit furnace problem (Model 11-5), use the model to evaluate the performance improvement resulting from preheating arriving ingots so that their temperature is uniformly distributed
Simulate the population dynamics involving the growth and decay of an infectious, but easily curable disease. The disease occurs within a single population, and recovery from the disease results
The Lanchester combat equations , developed by the British mathematician Frederick W. Lanchester during World War I to analyze the new phenomenon of aerial warfare, consider the levels x ( t ) and
The ancient Lanchester combat equations have the same def nition of x ( t ) and y ( t ) as in Exercise 11-8, but instead model the rate of depletion of each side to be proportional to the product of
In Exercise 11-8, suppose that both sides receive random-sized re-enforcements that increase their numbers, and that these re-enforcements arrive at random times. Re-enforcements to x ( t ) arrive
Modify your ancient Lanchester model in Exercise 11-9 so that both sides receive re-enforcements as described in Exercise 11-10. The parameters of the re-enforcements, however, are different here:
Modify Model
for a different way to allocate random numbers to support synchronization for CRN, as follows. When a new part arrives, (pre-)generate and store in attributes of this entity its processing-time
Modify Model 12-3 from Section 12.5.1 to demand, in addition to the 95% conf dence-interval half width on the expected average total WIP being no more than 0.4, that the half width of the 95% conf
Modify Model 12-3 from Section 12.5.1 to demand instead that the ratio of the 95% conf dence-interval half width to the point estimate on the expected average total WIP be less than 0.04, as
Combine Exercises 12-3 and 12-4, as follows. Set up a sequential-sampling run so that you get 4% relative-precision conf dence intervals on both the expected average total WIP as well as on the
Modify Model 12-4 from Section 12.5.2 to terminate the replication when the ratio of the half width to the midpoint (point estimate) of the automatic batch-means run-time conf dence interval on
As noted in Section 12.4.1, Chance-type Decide modules use random-number stream 10 for their hypercoin f ips, and there’s no way to change that in the module. Describe (in words) how you could work

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