Simulation 2nd Edition Stewart Robinson - Solutions

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E12.6 Carry out some verification and validation tests with the Panorama Televisions model (Appendix 2: Section A2.2.1).Table 12.1 Simulation Results and Real World Data for Mean Customer Waiting Time (minutes) at a Bank.Replication/Day Simulation result(mean customer waiting time)Bank data(mean
E12.5 Carry out some verification and validation tests with the Wardeon Cinema model(Appendix 1: Section A1.2.1).
E12.4 Carry out some verification and validation tests with the bank model developed from the case described in Exercise E6.1 (Chapter 6).
E12.3 Obtain and read some simulation case studies (see Exercise E1.3 in Chapter 1 for potential sources). Identify the methods used to verify and validate the models.Can you identify any ways in which the verification and validation could be improved upon?
E12.2 Table 12.1 shows the results (mean customer waiting time) obtained from 10 replications with a simulation model of a bank. Each replication was run for a period of one day. It also shows the data collected by observing 10 days of operation of the bank. Does this data give confidence in the
E12.1 Compare and contrast the difficulties that might be encountered in validating a simulation model of:a) An existing manufacturing plantb) A unique construction project
E10.10 Section 10.5 describes various formal approaches to search experimentation (experimental design, metamodelling and optimization). Carry out some further research into one or more of these areas, identifying the key methods for aiding simulation experimentation. (Hint: use the references in
E10.9 Carry out a sensitivity analysis on a model (e.g. the bank model, the Wardeon Cinema model or the Panorama Televisions model). Select two or three key data and vary them to determine the sensitivity of the results. Vary the data individually and in combination in order to understand their
E10.7 Perform a series of experiments with the Wardeon Cinema model (Appendix 1:Section A1.2.1). Aim to identify the quantity of resources required.E10.8 Perform a series of experiments with the Panorama Televisions model (Appendix 2:Section A2.2.1). Aim to achieve the target throughput required.
E10.6 Perform a series of experiments with the bank model developed from the case described in Exercise E6.1 (Chapter 6). Aim to identify the best combination of ATMs.
E10.5 A second set of replications have been run with the manufacturing model in Exercise E10.1, this time with an improvement to the cycle time of a bottleneck process. The results are shown in Table 10.13. Common random numbers have been used for both sets of experiments. Is the new scenario
E10.4 Apply the batch means method to the output data from the Panorama Televisions model (Appendix 2). Use the BatchMeans.xls spreadsheet provided on the web site (www.wileyeurope.com/go/robinson).Table 10.11 Results from 10 Replications with a Manufacturing Model (Warm-up Period Data have been
E10.3 Apply the batch means method to the artificial time-series data created for Exercise 9.6 (Chapter 9). Use the BatchMeans.xls spreadsheet provided on the web site (www.wileyeurope.com/go/robinson).
E10.2 Table 10.12 shows the daily throughput results from a single run with a manufacturing model (the warm-up period data have been deleted). Using a batch size of four, calculate a confidence interval for these data using the batch means method.What does the confidence interval suggest about the
E10.1 Ten replications have been performed with a manufacturing model. The mean daily throughput results are shown in Table 10.11 (the warm-up period data have been deleted). Calculate a confidence interval for these results. What does this tell us about the likely value of the mean daily
E9.12 Section 9.6.2 discusses the concept of variance reduction. Two methods of variance reduction are introduced in the book, common random numbers (Section 8.3.2)and antithetic variates (Section 9.6.2). A number of other methods of variance reduction have been proposed. Investigate the theory and
E9.11 Test the effect of using antithetic variates with a simulation model. For instance, use the Wardeon Cinema or Panorama Televisions models. What is the effect on the standard deviation of the results obtained from multiple replications? Are fewer replications required to obtain a good estimate
E9.10 What are the warm-up, replications and run-length requirements for the Panorama Televisions model (Appendix 2: Section A2.2.1)?
E9.9 What are the warm-up, replications and run-length requirements for the Wardeon Cinema model (Appendix 1: Section A1.2.1)?
E9.8 Determine the warm-up period and number of replications required with the bank model developed from the case described in Exercise E6.1 (Chapter 6).
E9.7 Try alternative values for the constants in the formula in Exercise E9.6 and see what effect it has on the warm-up period, number of replications and run-length required.
E9.6 Artificial time-series data can be created using formula such as:Xt = aXt–1 + Normal(mean =b, SD = b/5)These series include an initial transient and variability. Using the formula above generate 10 series of data, each with 500 observations, in a spreadsheet. Set a = 0.9, b = 1 and the
E9.5 Table 9.10 shows the results (mean daily throughput) obtained from 30 replications with the manufacturing model in Exercise E9.4. Each replication has been run for a period of 100 days after the warm-up period, which is deleted from the data.Determine the number of replications that should be
E9.4 Table 9.9 shows the results obtained for daily throughput from a non-terminating simulation of a manufacturing facility. Five replications have been performed with the model. The results shown are the mean average of the five replications. Determine the warm-up period using:a) The time-series
E9.3 Investigate the effect of changing the pseudo random number streams on the results obtained from a simulation model. For instance, experiment with the bank model(described in Chapter 6, Exercise E6.1), or the Wardeon Cinema (Appendix A1.2.1)and Panorama Televisions (Appendix A2.2.1) models.
E9.2 The time-series graphs below show typical simulation output. For each graph identify the type of model (terminating or non-terminating) and the nature of the simulation output (transient, steady-state, steady-state cycle). Resul Period (c) Period (a) Resut Period (d) Period (b)
E9.1 For the following simulation models identify the expected type of model (terminating or non-terminating) and the nature of the simulation output (transient, steady-state, steady-state cycle).a) A model of a refrigerator manufacturing plant that aims to determine plant throughput.b) A model of
E8.3 Develop a computer model for the Panorama Televisions case described in Appendix 2(Section A2.2.1). First devise the model structure and then develop the model code in the software of your choice.
E8.2 Develop a computer model for the Wardeon Cinema case described in Appendix 1(Section A1.2.1). First devise the model structure and then develop the model code in the software of your choice.
E8.1 Develop a computer model for the bank case described in Exercise E6.1 (Chapter 6).First devise the model structure and then develop the model code in the software of your choice. Set up the data so that the mean arrival rate is about 80% of the mean service rate (for the purposes of
E7.7 Identify the data requirements for the Panorama Televisions case described in Appendix 2 (Section A2.2.1).
E7.6 Identify the data requirements for the Wardeon Cinema case described in Appendix 1 (Section A1.2.1).
E7.5 Identify the data requirements for the bank model described in Exercise E6.1(Chapter 6).
E7.4 Use the Distributions.xls spreadsheet (see the web site www.wileyeurope.com/go/robinson) to investigate the effect of changing distribution parameters on the shape, mean and standard deviation of the distributions.
E7.3 Table 7.9 provides 100 values collected from the four processes listed in exercise E7.2. The mean and standard deviation of the data are also calculated. Fit statistical distributions to the data for each of the processes. Note: these data are available in a spreadsheet (Exercise7.xls) on the
E7.2 Which statistical distributions would be most appropriate for modelling the following processes?ž Process 1: the weight of a bottle leaving a filling processž Process 2: the time between failure of a machinež Process 3: the check-in time at an airportž Process 4: the number of orders for a
E7.1 A simulation model is being developed of the check-in area at an airport. Different airlines follow slightly different procedures and within airlines the staff have quite different levels of experience. Discuss the problems that might be encountered in collecting data on check-in times.
E6.5 Design a conceptual model for the Wardeon Cinema case described in Appendix 1(Section A1.2.1). Identify the modelling objectives, model inputs and outputs, model content and any assumptions and simplifications.E6.6 Design a conceptual model for the Panorama Televisions case described in
E6.4 For each of the following situations identify a method of simplification that could be employed in a simulation model of the system:a) A factory that produces a range of products: 80% of orders are for two product types, 18% for five product types and the remaining 2% for 17 product types.b) A
E6.3 Obtain and read some simulation case studies (see Exercise E1.3 in Chapter 1 for potential sources). Identify the objectives of the simulation study, the experimental factors and responses, and any assumptions and simplifications that were made. As far as possible try to identify the scope and
E6.2 Take a typical operations system, preferably one that can be observed (e.g. a supermarket or airport), and identify in broad terms at least three conceptual models that could be developed of the system. For each model identify the objectives and model content.
E6.1 A bank is planning its requirements for ATMs (automated teller machines) in a new branch. There are spaces for up to six ATMs, not all of which have to be used.Three types of ATM can be purchased: general ATMs (giving cash, balances, mini statements and PIN change facilities), ATMs for paying
E5.1 Section 5.5.2 introduces various methods for representing a conceptual model. Investigate these methods in more depth, identifying the strengths and weaknesses of the various approaches. Use the references provided in Section 5.5.2 as a starting point.
E2.5 Section 2.3.5 describes how samples can be taken from a normal distribution. Various algorithms are available for sampling from a range of statistical distributions. For each of the distributions described in Appendix 4 identify a sampling algorithm. (Hint: use the references in Section 2.3.5
E2.4 Section 2.3.6 describes an algorithm for generating random numbers. Research and identify alternative mechanisms for generating random numbers. What are the benefits and problems with alternative generators? How can the efficacy of a random number generator be tested? (Hint: use the references
E2.3 Following a discussion with the airport’s operations manager, more accurate data on the take-off and landing of aeroplanes have come to light. Aeroplanes are classified into two sizes: small and large. Small aeroplanes only require a 1.5-minute slot on the runway, while large aeroplanes
E2.2 For the airport problem described in Exercise E2.1 develop a three-phase discrete-event simulation of the problem.a) Define the B-events and C-events for the problem.b) Simulate 20 minutes of operation at the airport.
E2.1 An airport is planning its requirements for runway facilities and wishes to know whether the current plan to have a single full-length runway is sufficient. It is expected that during peak periods aeroplanes will land every 4 minutes (exactly)and that aeroplanes will take-off with the same
E1.3 There are many case studies describing the application of simulation to real problems.Obtain and read some simulation case studies. Why was simulation used? What benefits were obtained? Some journals that often publish simulation case studies are: IIE Solutions, Interfaces, OR Insight and the
E1.2 Take a typical operations system, preferably one that can be observed (e.g. a bank or supermarket), and identify the elements of variability, interconnectedness and complexity.
E1.1 Think of situations where simulation could be used, for instance, from day-to-day life, a place of study or work. What aspects of each situation make simulation appropriate?
ž When is simulation appropriate?
ž Why would an organization choose to develop and use a simulation model?
What exactly is a simulation?
Carry out some verification and validation tests with the Panorama Televisions model (Appendix 3).
Carry out some verification and validation tests with the Wardeon Cinema model (Appendix 2).
Carry out some verification and validation tests with the simple queue model (Appendix 1).
Carry out some verification and validation tests with the bank model developed from the case described in Exercise E6.1(Chapter 6).
Obtain and read some simulation case studies (see Exercise E1.3 in Chapter 1 for potential sources). Identify the methods used to verify and validate the models. Can you identify any ways in which the verification and validation could be improved upon?
Table 12.1 shows the results (mean customer waiting time) obtained from ten replications with a simulation model of a bank. Each replication was run for a period of one day. It also shows the data collected by observing ten days of operation of the bank. Does this data give confidence in the
Compare and contrast the difficulties that might be encountered in validating a simulation model of:a) An existing manufacturing plantb) A unique construction project
Section 10.5 describes various formal approaches to search experimentation(experimental design, metamodelling and optimisation).Carry out some further research into one or more of these areas, identifying the key methods for aiding simulation experimentation.(Hint: use the references in Section
Carry out a sensitivity analysis on a model (e.g. the bank model, the Wardeon Cinema model or the Panorama Televisions model). Select two or three key data and vary them to determine the sensitivity ofthe results. Vary the data individually and in combination in order to understand their effect on
Perform a series of experiments with the Panorama Televisions model(Appendix 3). Aim to achieve the target throughput required.
Perform a series of experiments with the Wardeon Cinema model(Appendix 2). Aim to identify the quantity of resources required.
Perform a series of experiments with the bank model developed from the case described in Exercise E6.1(Chapter 6). Aim to identify the best combination of ATMs.
A third and fourth set of replications for two new scenarios have been run with the manufacturing model in Exercises E10.1 and E10.5.The results are shown in Table 10.17. Common random numbers have been used for all sets of experiments.i. Compare each scenario to the base scenario (scenario 1).
A second set of replications have been run with the manufacturing model in Exercise E10.1, this time with an improvement to the cycle time of a bottleneck process. The results are shown in Table 10.16.Common random numbers have been used for both sets of experiments.Assuming the aim is to increase
Apply the batch means method to the output data from the Panorama Televisions model (Appendix 3). Use the BatchMeans.xls spreadsheet provided on the companion web site.
Apply the batch means method to the artificial time-series data created for Exercise 9.6 (Chapter 9). Use the BatchMeans.xls spreadsheet provided on the companion web site. Table 10.14 Results from ten replications with a manufacturing model (warm-up period data have been deleted) Replication Mean
Table 10.15 shows the daily throughput results from a single run with a manufacturing model (the warm-up period data have been deleted).Using a batch size of four, calculate a confidence interval for these data using the batch means method. What does the confidence interval suggest about the
Ten replications have been performed with a manufacturing model.The mean daily throughput results are shown in Table 10.14 (the warm-up period data have been deleted).i. Calculate a confidence interval for these results. What does this tell us about the likely value of the mean daily throughput?ii.
Section 9.7.2 discusses the concept of variance reduction. Two methods of variance reduction are introduced in the book, common random numbers (Section 8.3.2) and antithetic variates (Section 9.7.2).A number of other methods of variance reduction have been proposed.Investigate the theory and
Test the effect of using antithetic variates with a simulation model.For instance, use the simple queue model (Appendix 1), or the Wardeon Cinema (Appendix 2) and Panorama Televisions (Appendix 3) models. What is the effect on the standard deviation of the results obtained from multiple
What are the warm-up, replications and run-length requirements for the Panorama Televisions model (Appendix 3)?
What are the warm-up, replications and run-length requirements for the Wardeon Cinema model (Appendix 2)?
What are the warm-up, replications and run-length requirements for the simple queue model (Appendix 1)?
Determine the warm-up period and number of replications required with the bank model developed from the case described in Exercise E6.1(Chapter 6).
Try alternative values for the constants in the formula in Exercise E9.6 and see what effect it has on the warm-up period, number of replications and run-length required.
Artificial time-series data can be created using formula such as:Xt=aXt–1 + Normal (mean=b, SD=b/5)These series include an initial transient and variability. Using the formula above generate ten series of data, each with 500 observations, in a spreadsheet. Set a = 0.9, b = 1 and the initial value
Table 9.8 shows the results (mean daily throughput) obtained from 30 replications with the manufacturing model in Exercise E9.4. Each replication has been run for a period of 100 days after the warm-up period, which is deleted from the data. Determine the number of replications that should be
The Exercise9.xls spreadsheet on the companion web site contains six different datasets, each run for a number of replications. For each dataset use time-series inspection and the MSER heuristic to determine the warm-up period. When do you think MSER worked well, and with which datasets did it
Table 9.7 shows the results obtained for daily throughput from a nonterminating simulation of a manufacturing facility. Five replications have been performed with the model. The results shown are the mean average of the five replications. Determine the warm-up period using:a) The time-series
Investigate the effect of changing the pseudo random number streams on the results obtained from a simulation model. For instance, experiment with the bank model (described in Chapter 6, Exercise E6.1), the simple queue model (Appendix 1) or the Wardeon Cinema (Appendix 2)and the Panorama
The time-series graphs below show typical simulation output. For each graph identify the type of model (terminating or non-terminating) and the nature of the simulation output (transient, steady-state, steady-state cycle). Result Period Period (c) () Result Result Period Period (d) (b)
For the following simulation models identify the expected type of model(terminating or non-terminating) and the nature of the simulation output(transient, steady-state, steady-state cycle).a) A model of a refrigerator manufacturing plant that aims to determine plant throughput.b) A model of a
Develop a computer model for the Panorama Televisions case described in Appendix 3. First devise the model design and then develop the model code in the software of your choice.
Develop a computer model for the Wardeon Cinema case described in Appendix 2. First devise the model design and then develop the model code in the software of your choice.
Develop a computer model for the simple queue model case described in Appendix 1. First devise the model design and then develop the model code in the software of your choice.
Develop a computer model for the bank case described in Exercise E6.1(Chapter 6). First devise the model design and then develop the model code in the software of your choice. Set-up the data so that the mean arrival rate is about 80 per cent of the mean service rate (for the purposes of
Identify the data requirements for the Panorama Televisions case described in Appendix 3 (Section A3.2.1).Table 7.10 Data collected from four processes Observation Process 1 Process 2 Process 3 Process 4 1 9.91 9.21755 2.65 5 2 9.53 142.887 3.40 8 3 9.99 32.0088 2.64 6 4 10.04 75.3285 3.38 8 5
Identify the data requirements for the Wardeon Cinema case described in Appendix 2 (Section A2.2.1).
Identify the data requirements for the simple queue model case described in Appendix 1 (Section A1.2.1).
Identify the data requirements for the bank model described in exercise E6.1(chapter 6).
Use the Distributions.xls spreadsheet (see the companion web site) to investigate the effect of changing distribution parameters on the shape, mean and standard deviation of the distributions.
Table 7.10 provides 100 values collected from the four processes listed in exercise E7.2. The mean and standard deviation of the data are also calculated. Fit statistical distributions to the data for each of the processes.Note: these data are available in a spreadsheet (Exercise7.xls) on the
Which statistical distributions would be most appropriate for modelling the following processes?• Process 1: the weight of a bottle leaving a filling process• Process 2: the time between failure of a machine• Process 3: the check-in time at an airport• Process 4: the number of orders for a
A simulation model is being developed of the check-in area at an airport.Different airlines follow slightly different procedures and within airlines the staff have quite different levels of experience. Discuss the problems that might be encountered in collecting data on check-in times.
Design a conceptual model for the Panorama Televisions case described in Appendix 3 (Section A3.2.1). Identify the modelling objectives, model inputs and outputs, model content and any assumptions and simplifications.
Design a conceptual model for the Wardeon Cinema case described in Appendix 2 (Section A2.2.1). Identify the modelling objectives, model inputs and outputs, model content and any assumptions and simplifications.

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Simulation 2nd Edition Stewart Robinson - Solutions

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