model Building_updated_by_me_in_the_year.pptx

mcqs = {
"questions": [
"An efficient supervisor should possess:\nA. Technical skills\nB. Conceptual skills\nC. Human relation skills\nD. All the above",
"Which of the following is NOT an interpersonal role of a manager?\nA. figurehead\nB. leader\nC. disseminator\nD. liaison",
"Performing virtually all activities that involve subordinates is an example of:\nA. figurehead\nB. leader\nC. Liaison\nD. None of the given",
"The process of monitoring, comparing and correcting is called:\nA. Controlling\nB. Coordinating\nC. Leading\nD. Directing",
"Who described the Classical Approach as the Engineering Approach?\nA. Gullick\nB. Fayol\nC. Urwick\nD. Mooney",
"________ is a French Industrialist who identified the basic Management functions.\nA. Weber\nB. Taylor\nC. Herzberg\nD. Fayol",
"Management exists at which levels of the organization?\nA. Top\nB. Bottom\nC. Middle\nD. All",
"Under mechanism of scientific management, scientific task setting includes:\nA. Time study\nB. Motion study\nC. Method study\nD. All of the above",
"The philosophy of management known as total quality management developed primarily out of the work of:\nA. Henri Fayol\nB. Frederick Taylor\nC. Robert McNamara\nD. W. Edwards Deming",
"Writing an Organizational Strategic plan is an example of:\nA. Staffing\nB. Leading\nC. Organizing\nD. Planning",
"Which of the following skills is most essential to get the most out of the people?\nA. Technical skill\nB. Human Skill\nC. Conceptual skill\nD. Communication Skill",
"Monitoring organizational progress towards goal attainment is called:\nA. Controlling\nB. Organizing\nC. Leading\nD. Planning",
"An efficient supervisor should possess:\nA. Technical Skills\nB. Conceptual Skills\nC. Human Relations skills\nD. All the Above",
"The process of monitoring, comparing and correcting is called:\nA. Controlling\nB. Coordinating\nC. Leading\nD. Directing",
"Which one of the following individuals is most closely associated with scientific management?\nA. Fredrick Taylor\nB. Mary Parker Follett\nC. Harold Koontz\nD. Max Weber",
"A plan developed to carry out a course of action that is not likely to be repeated in the future is called:\nA. Specific Plan\nB. Single Use plan\nC. Reaction plan\nD. Directional plan",
"The degree to which jobs are standardized and guided by rules and procedures is called:\nA. Work specialization\nB. Centralization\nC. Decentralization\nD. Formalization",
"Which of the following is not included in Decisional role of a manager?\nA. Entrepreneur\nB. Disturbance handler\nC. Collecting\nD. Negotiator",
"If a manager gives information regarding organization policies and actions, then he/she is performing an ______ role.\nA. Interpersonal\nB. Informational\nC. Decisional\nD. None of the given",
"------------- is the use of scientific method to define the “one best way” f

1. SSG517 Systems Simulation and Animation Dr K. O. Orolu Department of Systems Engineering, University of Lagos

2. SIMULATION MODEL BUILDING Problem analysis and Data collection

3. Problem analysis and information collection The information is then represented as logic flow diagrams, hierarchy trees, narrative, or any other convenient means of representation

4. AN EXAMPLE: A PRODUCTION CONTROL PROBLEM Consider a packaging/ warehousing process with the following steps: 1. The product is filled and sealed. 2. Sealed units are placed into boxes and stickers are placed on the boxes. 3. Boxes are transported to the warehouse to fulfill customer demand.

5. System Properties/Assumptions Availability of Raw Materials : There is always sufficient raw material for the process never to starve. Processing Time : Processing is carried out in batches, five units to a batch. Finished units are placed in the warehouse. Data collected indicate that unit-processing times are uniformly distributed between 10 and 20 minutes.

6. System Properties/Assumptions (cont’d) Processing Experience : The process experiences random failures, which may occur at any point in time. Times between failures are exponentially distributed with a mean of 200 minutes. Data collection also showed that repair times are normally distributed, with a mean of 90 minutes and a standard deviation of 45 minutes. Warehouse Capacity : The warehouse has a capacity (target level) of R = 500 units. Processing stops when the inventory in the warehouse reaches the target level. From this point on, the production process becomes blocked and remains inactive until the inventory level drops to the reorder point, which is assumed to be r=150 units. The process restarts with a new batch as soon as the reorder level is down-crossed. This is a convenient policy when a resource needs to be distributed among various types of products. For instance, when our process becomes blocked, it may actually be assigned to another task or product that is not part of our model.

7. System Properties/Assumptions (Cont’d)

8. System Properties/Assumptions (Cont’d)

9. Data collection and Input Analysis Data collection is needed for estimating model input parameters. The analyst can formulate assumptions on the distributions of random variables in the mode

10. Int r oduction to Input Ana l ysis Y ou ’ v e made your fl o wcharts, and you h a v e a pretty good idea of all of the processes that customers h a v e to unde r go as th e y m o v e through the system. Y ou ’ v e e v en programmed your model in your f a v orite simulation language. But there ’ s one little hurdle left — proper simulation input analysi s . What distri b utions do you use to model interarr i v al times, service times, breakd o wn times, etc.?

11. What shall we do?

12. Easy Data Ana l ysis Ideas If you ta k e enough obser v ations, the histogram will e v entually co n v e r ge to the true distri b ution. You should a l w ays plot out your data before doing a n ything else. Example: Hist o g r ams.

13. Stem-and-leaf di a g r ams 10 000 9 998764422110 8 9764544321 7 965432100 6 7532 5 433 4 8 A  Stem and Leaf Plot  is a special table where each data value is split into a "stem" (the first digit or digits) and a "leaf" (usually the last digit). These are sort of sid e w ays histograms with numbers

14. Intro to Input Analysis Whi c h Distri b ution?

15. Whi c h Distri b ution, II?

16. Whi c h Distri b ution III?

17. Game Plan

18. Challenges No / Little Data This issue turns up more often than you w ould e xpect. There could literally be no data a vailable, or the data that you h a v e is a wful (goofy v alues, not cleaned properl y , etc.). What to do? No great options — b ut here are some suggestions. Intervi e w so-called “ e xperts” T ry to at least get minimum, maximum, and “most li k ely” distri b ution v alues out of them — then you can guess uniform or triangular distri b utions. Getting quantiles from the e xpert is e v en bette r . At least discuss the nature of the obser v ations.

19. If you h a v e some idea about the nature of the R Vs, maybe you can ma k e a good guess as to the distri b ution. Discrete or continuous? Are obser v ations successes / f ailures? Then think Bernoulli, binomial, geometric, n e g at i v e binomial. Do obser v ations adhere to Poisson assumptions? Then Poisson (if you ’ re counting arr i v als) or e xponential (interarr i v al times). Are obser v ations a v erages or sums? Then maybe normal. Are obser v ations bounded? Then think beta. Reliability or job times? Maybe g amma, W ei b ull, lognormal, etc. Can you think of a n ything else from the p h ysical characteristics underlying the R V?

20. 2. Nonstanda r d / Goofy / Mixture Distri b utions Can attempt to model as a mixtu r e of reasonable distri b utions. Easier: Can sample from the empiri c al distri b ution or a smoothed v ersion of the empirical. This is a form of bootst r apping . Here ’ s a forced marriage of t w o normals — most packages can ’ t pick this up or fit it properl y .

21. 3. Nonstationa r y Data

22. 4. Multi v ariate / Correlated Data

23. Multi v ariate / Correlated Data: W hat do you need to do? Identify mult i v ariate / serial correlation situations. Propose appropriate models. Examples: Mult i v ariate normal for heights and weights. Time series models for serially correlated obser v ations, e.g., autor e gress i v e-m o ving a v erage ARMA( p, q ), EAR(1), A R T O P , . Estimate rel e v ant parameters . Examples: Mult i v ariate normal: Ma r ginal means and v ariances plus c o v ariances Time series: V alidate to see if your estimated model is actually a n y good. Alternat i v e: Can bootstrap samples from an empirical distri b ution (if you h a v e enough data).

24. Using Software

25. Assignment Select the company where you work as a case study. Identify a unit/system of interest in the company Analyze the System with respect to the following: identify input parameters, Determine the performance measures of interest, Identify the relationships among parameters and variables, Identify the rules governing the operation of system components Represent t he information gathered as logic flow diagrams, hierarchy trees, narrative, or any other convenient means of representation. Hence, generate six relevant questions that will provide insight for the simulation of the system.   The Six possible relevant questions could start with: What…? How: …? Why…? Can: …? Does: …? Should: …?

26. Assignment Model and simulate a simple job shop system that produces two types of products, A and B, using three machines, M1, M2, and M3. Each product has a different routing and processing time through the system . Identify a unit/system of interest in the company Analyze the System with respect to the following: identify input parameters, Determine the performance measures of interest, Identify the relationships among parameters and variables, Identify the rules governing the operation of system components Represent t he information gathered as logic flow diagrams, hierarchy trees, narrative, or any other convenient means of representation. Hence, generate six relevant questions that will provide insight for the simulation of the system.   The Six possible relevant questions could start with: What…? How: …? Why…? Can: …? Does: …? Should: …?