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Graduate Program in Business Information Systems

Graduate Program in Business Information Systems. Simulation Aslı Sencer. Simulation. – V ery broad term – methods and applications to imitate or mimic real systems, usually via computer Applies in many fields and industries Very popular and powerful method. Advantages of Simulation.

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Graduate Program in Business Information Systems

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  1. Graduate Program in Business Information Systems Simulation Aslı Sencer

  2. Simulation • – Very broad term – methods and applications to imitate or mimic real systems, usually via computer • Applies in many fields and industries • Very popular and powerful method BIS 517-Aslı Sencer

  3. Advantages of Simulation • Simulation can tolerate complex systems where analytical solution is not available. • Allows uncertainty, nonstationarity in modeling unlike analytical models • Allows working with hazardous systems • Often cheaper to work with the simulated system • Can be quicker to get results when simulated system is experimented. BIS 517-Aslı Sencer

  4. The Bad News • Don’t get exact answers, only approximations, estimates • Requires statistical design and analysis of simulation experiments • Requires simulation expert and compatibility with a simulation software • Softwares and required hardware might be costly • Simulation modeling can sometimes be time consuming. BIS 517-Aslı Sencer

  5. Different Kinds of Simulation • Static vs. Dynamic • Does time have a role in the model? • Continuous-change vs. Discrete-change • Can the “state” change continuously or only at discrete points in time? • Deterministic vs. Stochastic • Is everything for sure or is there uncertainty? BIS 517-Aslı Sencer

  6. Using Computers to Simulate • General-purpose languages (C, C++, Visual Basic) • Simulation softwares, simulators • Subroutines for list processing, bookkeeping, time advance • Widely distributed, widely modified • Spreadsheets • Usually static models • Financial scenarios, distribution sampling, etc. BIS 517-Aslı Sencer

  7. Simulation Languages and Simulators • Simulation languages • GPSS, SIMSCRIPT, SLAM, SIMAN • Provides flexibility in programming • Syntax knowledge is required • High-level simulators • GPSS/H, Automod, Slamsystem, ARENA, Promodel • Limited flexibility — model validity? • Very easy, graphical interface, no syntax required • Domain-restricted (manufacturing, communications) BIS 517-Aslı Sencer

  8. When Simulations are Used • The early years (1950s-1960s) • Very expensive, specialized tool to use • Required big computers, special training • Mostly in FORTRAN (or even Assembler) • The formative years (1970s-early 1980s) • Computers got faster, cheaper • Value of simulation more widely recognized • Simulation software improved, but they were still languages to be learned, typed, batch processed BIS 517-Aslı Sencer

  9. When Simulations are Used (cont’d.) • The recent past (late 1980s-1990s) • Microcomputer power, developments in softwares • Wider acceptance across more areas • Traditional manufacturing applications • Services • Health care • “Business processes” • Still mostly in large firms • Often a simulation is part of the “specs” BIS 517-Aslı Sencer

  10. When Simulations are Used (cont’d.) • The present • Proliferating into smaller firms • Becoming a standard tool • Being used earlier in design phase • Real-time control • The future • Exploiting interoperability of operating systems • Specialized “templates” for industries, firms • Automated statistical design, analysis BIS 517-Aslı Sencer

  11. Popularity of Simulation • Consistently ranked as the most useful, popular tool in the broader area of operations research / management science • 1979: Survey 137 large firms, which methods used? 1. Statistical analysis (93% used it) 2. Simulation (84%) 3. Followed by LP, PERT/CPM, inventory theory, NLP, • 1980: (A)IIE O.R. division members • First in utility and interest — simulation • First in familiarity — LP (simulation was second) • 1983, 1989, 1993: Heavy use of simulation consistently reported 1. Statistical analysis2. Simulation BIS 517-Aslı Sencer

  12. Today: Popular Topics • Real time simulation • Web based simulation • Optimization using simulation BIS 517-Aslı Sencer

  13. Simulation Process • Develop a conceptual model of the system • Define the system, goals, objectives, decision variables, output measures, input variables and parameters. • Input data analysis: • Collect data from the real system, obtain probability distributions of the input parameters by statistical analysis • Build the simulation model: • Develop the model in the computer using a HLPL, a simulation language or a simulation software BIS 517-Aslı Sencer

  14. Simulation Process (cont’d.) • Output Data Analysis: • Run the simulation several times and apply statistical analysis of the ouput data to estimate the performance measures • Verification and Validation of the Model: • Verification: Ensuring that the model is free from logical errors. It does what it is intended to do. • Validation: Ensuring that the model is a valid representation of the whole system. Model outputs are compared with the real system outputs. BIS 517-Aslı Sencer

  15. Simulation Process (cont’d.) • Analyze alternative strategies on the validated simulation model. Use features like • Animation • Optimization • Experimental Design • Sensitivity analysis: • How sensitive is the performance measure to the changes in the input parameters? Is the model robust? BIS 517-Aslı Sencer

  16. Static Simulation:Monte-Carlo Simulation • Static Simulation with no time dimension. • Experiments are made by a simulation model to estimate the probability distribution of an outcome variable, that depends on several input variables. • Used the evaluate the expected impact of policy changes and risk involved in decision making. • Ex: What is the probability that 3-year profit will be less than a required amount? • Ex: If the daily order quantity is 100 in a newsboy problem, what is his expected daily cost? (actually we learned how to answer this question analytically) BIS 517-Aslı Sencer

  17. Ex1: Simulation for Dave’s Candies • Dave’s Candies is a small family owned business that offers gourmet chocolates and ice cream fountain service. For special occasions such as Valentine’s day, the store must place orders for special packaging several weeks in advance from their supplier. One product, Valentine’s day chocolate massacre, is bought for $7,50 a box and sells for $12.00. Any boxes that are not sold by February 14 are discounted by 50% and can always be sold easily. Historically Dave’s candies has sold between 40-90 boxes each year with no apparent trend. Dave’s dilemma is deciding how many boxes to order for the Valentine’s day customers. BIS 517-Aslı Sencer

  18. Ex1: Dave's Candies Simulation If the order quantity, Q is 70, what is the expected profit? Selling price=$12 Cost=$7.50 Discount price=$6 • If D<Q Profit=selling price*D - cost*Q + discount price*(Q-D) • D>Q Profit=selling price*Q-cost*Q BIS 517-Aslı Sencer

  19. Probability Distribution for Demand BIS 517-Aslı Sencer

  20. Generating Demands Using Random Numbers • During simulation we need to generate demands so that the long run frequencies are identical to the probability distribution found. • Random numbers are used for this purpose. Each random number is used to generate a demand. • Excel generates random numbers between 0-1. These numbers are uniformly distributed between 0-1. BIS 517-Aslı Sencer

  21. Generating random demands:Inverse transformation technique P(demand<=xi) P(demand=xi) 1 5/6 4/6 3/6 2/6 1/6 Generate U~UNIFORM(0,1) Let U=P(Demand<=D) then D=P-1(U) U1 U2 1/6 (xi) (xi) 40 50 60 70 80 90 40 50 60 70 80 90 D2=50 D1=80 BIS 517-Aslı Sencer

  22. Generating Demands BIS 517-Aslı Sencer

  23. Ex1: Simulation in Excel for Dave’s Candies Use the following excel functions to generate a random demand with a given distribution function. • RAND(): Generates a random number which is uniformly distributed between 0-1. • VLOOKUP(value, table range, column #): looks up a value in a table to detremine a random demand. • IF(condition, value if true, value if false): Used to calculate the total profit according to the random demand. BIS 517-Aslı Sencer

  24. BIS 517-Aslı Sencer

  25. Dynamic Simulation:Queueing System Arrivals Departures Service • is identified by: • Arrival rate, interarrival time distribution • Service rate, service time distribution • # servers • # queues • Queue capacities • Queue disciplines, FIFO, LIFO, etc. BIS 517-Aslı Sencer

  26. M/M/1 Queueing System Arrivals Departures Service M: interarrival time is exponentially distributed M: service time is exponentially distributed 1: There is a single server BIS 517-Aslı Sencer

  27. Ex3: Model Specifics • Initially (time 0) empty and idle • Base time units: minutes • Input data (assume given for now …), in minutes: Part Number Arrival Time Interarrival Time Service Time 1 0.00 1.73 2.90 2 1.73 1.35 1.76 3 3.08 0.71 3.39 4 3.79 0.62 4.52 5 4.41 14.28 4.46 6 18.69 0.70 4.36 7 19.39 15.52 2.07 8 34.91 3.15 3.36 9 38.06 1.76 2.37 10 39.82 1.00 5.38 11 40.82 . . . . . . . . . . • Stop when 20 minutes of (simulated) time have passed BIS 517-Aslı Sencer

  28. Queuing Simulation • Random variables: • Time between arrivals • Service time represented by probability distributions. • Events: • Arrival of a customer to the system • Departure from the system. • State variables: • # customers in the queue • Worker status {busy, idle} • Output measures: • Average waiting time in the queue • % utilization of the server • Average time spent in the system BIS 517-Aslı Sencer

  29. Output Performance Measures • Total production of parts over the run (P) • Average waiting time of parts in queue: • Maximum waiting time of parts in queue: N = no. of parts completing queue wait WQi = waiting time in queue of ith part Know: WQ1 = 0 (why?) N> 1 (why?) BIS 517-Aslı Sencer

  30. Output Performance Measures (cont’d.) • Time-average number of parts in queue: • Maximum number of parts in queue: • Average and maximum total time in system of parts: Q(t) = number of parts in queueat time t TSi = time in system of part i BIS 517-Aslı Sencer

  31. Output Performance Measures (cont’d.) • Utilization of the machine (proportion of time busy) • Many others possible (information overload?) BIS 517-Aslı Sencer

  32. Simulation by Hand • Manually track state variables, statistical accumulators • Use “given” interarrival, service times • Keep track of event calendar • “Lurch” clock from one event to the next • Will omit times in system, “max” computations here (see text for complete details) BIS 517-Aslı Sencer

  33. Simulation by Hand: Setup BIS 517-Aslı Sencer

  34. Simulation by Hand:t = 0.00, Initialize BIS 517-Aslı Sencer

  35. Simulation by Hand:t = 0.00, Arrival of Part 1 1 BIS 517-Aslı Sencer

  36. Simulation by Hand:t = 1.73, Arrival of Part 2 2 1 BIS 517-Aslı Sencer

  37. Simulation by Hand: t = 2.90, Departure of Part 1 2 BIS 517-Aslı Sencer

  38. Simulation by Hand:t = 3.08, Arrival of Part 3 3 2 BIS 517-Aslı Sencer

  39. Simulation by Hand:t = 3.79, Arrival of Part 4 4 3 2 BIS 517-Aslı Sencer

  40. Simulation by Hand:t = 4.41, Arrival of Part 5 5 4 3 2 BIS 517-Aslı Sencer

  41. Simulation by Hand:t = 4.66, Departure of Part 2 5 4 3 BIS 517-Aslı Sencer

  42. Simulation by Hand: t = 8.05, Departure of Part 3 5 4 BIS 517-Aslı Sencer

  43. Simulation by Hand:t = 12.57, Departure of Part 4 5 BIS 517-Aslı Sencer

  44. Simulation by Hand:t = 17.03, Departure of Part 5 BIS 517-Aslı Sencer

  45. Simulation by Hand:t = 18.69, Arrival of Part 6 6 BIS 517-Aslı Sencer

  46. Simulation by Hand: t = 19.39, Arrival of Part 7 7 6 BIS 517-Aslı Sencer

  47. Simulation by Hand:t = 20.00, The End 7 6 BIS 517-Aslı Sencer

  48. Ex3:Complete Record of the Hand Simulation BIS 517-Aslı Sencer

  49. Ex3: Simulation by Hand:Finishing Up • Average waiting time in queue: • Time-average number in queue: • Utilization of drill press: BIS 517-Aslı Sencer

  50. Randomness in Simulation • The above was just one “replication” — a sample of size one (not worth much) • Made a total of five replications: • Confidence intervals for expected values: • In general, • For expected total production, Note substantial variability across replications BIS 517-Aslı Sencer

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