1. Introduction : Simulation and Modeling Wk1Slide 1 Simulation and Modelling MIS 7102 Week 1 By Agnes Rwashana Semwanga asemwanga@cit.mak.ac.ug Introduction to Simulation & Modelling Friday 6.00p.m.- 9.00p.m. Faculty of Technology Room 143

2. Introduction : Simulation and Modeling Wk1Slide 2 Core for  MIS - Computer Information Systems (CIS) option Elective for  MIS – Management Information System (MIS) option  MIS – Information System management (ISM)  MIS – Internet and Database System (IDS)  MSC Computer Science

3. Introduction : Simulation and Modeling Wk1Slide 3 Course schedule Week 1 : Introduction to Simulation & Modelling Week 2 : Introduction to System Dynamics Week 3 : Systems thinking and Causal Loop Diagramming Week 4 : System Archetypes + Assignment for Class Presentation Week 5 : Assignment / Test 1 Week 6 : Causal Loop Diagrams with Vensim  Discrete Event Simulation by Guest Lecturer : Mr. John Ngubiri (next 3 weeks 1 hr [8-9]) Week 7 : Graphical Integration Week 8 : Feedback Structures Week 9-11 : Class Presentations - Assignment 3 Week 12 : Assignment / Test 2 Week 13 : Model Testing & Validation Week 14 : Stock and Flow with STELLA software - Practice Week 15 : Preparation for Exams

4. Introduction : Simulation and Modeling Wk1Slide 4 References Senge Peter (2003). The Fifth Discipline. Steward Robinson (2004). Simulation. The Practice of Model Development and Use. John Wiley and Sons Ltd. Goodman Michael R. (1989) Study Notes in System Dynamics Pid, M., (1992) Computer Simulation in Management Science, 3Ed John Wiley, Chichester

5. Introduction : Simulation and Modeling Wk1Slide 5 Simulation Is … Simulation – Definition : An imitation of a system. Imitation implies mimicking or copying something else. 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 Static simulation – imitates a system at a point in time Dynamic simulation – imitates a system as it progresses through time e.g movement of trains Computer based dynamic simulation – an imitation (on a computer) of a system as it progresses through time.

6. Introduction : Simulation and Modeling Wk1Slide 6 Illustration(Descriptive – Performance Analysis) Simulation vs. Real World

7. Introduction : Simulation and Modeling Wk1Slide 7 Typical Uses of Simulation Estimating a set of productivity measures in production systems, inventory systems, manufacturing processes, materials handling and logistics operations. Designing and planning the capacity of computer systems and communication networks so as to minimize response times. Conducting war games to train military personnel or to evaluate the efficacy of proposed military operations Evaluating and improving maritime port operations, such as container ports or bulk-material marine terminals (coal, oil or minerals), so as to find ways of reducing vessel port times. Improving health care operations, financial and banking operations, transportation systems and airports, among many others.

8. Use of Simulation Modelling Simulation is one of the most powerful management science/operation research tools applied to solve real problems. The modelling itself leads to greater insight into the Corporate investments appraisal. Involves the construction of a model of the problem domain which involves learning through experiments and then permits testing alternative scenarios using “what if..?” analysis.

9. Simulation in Business Modelling The essence of Modelling is that a manager makes decisions in advance of the real product, services or process being created. Simulation is one of the most fundamental approach to decision making. A tool to gain insights and explore possibilities through the formalised (situation) involved in using simulation models. Simulation explores the consequences of decision making rather than directly advising on the decision itself - it is a predictive rather than an optimising technique. Currently there are many tools both simulation language and packages that could be used for exploring the impacts of different decisions.

10. Introduction : Simulation and Modeling Wk1Slide 10 Systems System – a collection of parts organized for some purpose (Coyle, 1996). Checkland (1981) identifies 4 main classes of systems :  Natural system – origins lie in the origins of the universe eg. Atom  Designed physical systems – physical systems that are a result of human design e.g house, car  Designed abstract systems – abstract systems that are a result of human design e.g. mathematics and literature  Human activity systems – systems of human activity that are consciously or unconsciously ordered. e.g. Family, city, political system.

11. Introduction : Simulation and Modeling Wk1Slide 11 Why simulate ? – The Nature of operations Systems Variability - many operation systems are subject to variability. Some are predictable e.g. changing number of operators in a call centre during the day to meet the changing call volumes and others are unpredictable e.g arrival rate of patients at hospital emergency. Interconnectedness – components of the system do not work in isolation but affect one another. It is difficult to predict the effect of the interconnections. e.g customers who have to pass through various interconnected stages Complexity – many operations systems are complex, interconnections and combinations between the components of the system.

12. Introduction : Simulation and Modeling Wk1Slide 12 The Advantages of Simulation Some of the reasons why simulation is preferable to direct experimentation : Cost – Experimentation with real system is likely to be costly.  It is expensive to interrupt day to day operations to try out new ideas  If alterations cause operation’s performance to worsen- loss of customer satisfaction Time – Time consuming to experiment with the real system and this may take many weeks and months before a true reflection is obtained. Control of experimental conditions – It is useful to control the conditions under which the experiment is being done. e.g. it is not easy to control the arrival of patients at a hospital The real system does not exist. The real system may not exist. Flexibility to model things as they are (even if messy and complicated)

13. Introduction : Simulation and Modeling Wk1Slide 13 Disadvantages of Simulation Some of the problems associated with using simulation Expensive – simulation software is not necessarily cheap and cost of model development and use may be considerable. Time consuming – simulation is a time consuming approach Data hungry – most simulations require significant amount of data Requires expertise – requires skills in conceptual modelling, validation and statistics.

14. Introduction : Simulation and Modeling Wk1Slide 14 Applications for which Simulation might be used Manufacturing systems Public systems, health care, military, natural resources Transportation systems Construction systems Restaurant and entertainment systems Business process reengineering / management Food processing Computer system performance

15. Introduction : Simulation and Modeling Wk1Slide 15 Models Model – set of assumptions/approximations about how the system works  Study the model instead of the real system … usually much easier, faster, cheaper, safer  Can try wide-ranging ideas with the model – Make your mistakes on the computer where they don’t count, rather than for real where they do count  Often, just building the model is instructive – regardless of results  Model validity (any kind of model … not just simulation) – Care in building to mimic reality faithfully – Level of detail – Get same conclusions from the model as you would from system – More in Chapter 12

16. Introduction : Simulation and Modeling Wk1Slide 16 Types of Models Physical ( iconic ) models  Mock-ups of fast-food restaurants  Hospital scheduling  Operating room scheduling  Flight simulators Logical ( mathematical ) models  Approximations and assumptions about a system’s operation  Often represented via computer program in appropriate software  Exercise the program to try things, get results, learn about model behavior

17. Introduction : Simulation and Modeling Wk1Slide 17 Model Classification Physical (prototypes) Analytical (mathematical) Computer (Monte Carlo Simulation) Descriptive (performance analysis) Prescriptive (optimization)

18. Introduction : Simulation and Modeling Wk1Slide 18 Physical (Prototypes)

19. Introduction : Simulation and Modeling Wk1Slide 19 Analytical (Mathematical) Single Stage Queuing Model

20. Introduction : Simulation and Modeling Wk1Slide 20 Computer (Monte Carlo Simulation) Monte Carlo simulation is a spreadsheet simulation, which randomly generates values for uncertain variables over and over to simulate a model.

21. Introduction : Simulation and Modeling Wk1Slide 21 Prescriptive (Optimization)

22. Introduction : Simulation and Modeling Wk1Slide 22 Studying Logical Models If model is simple enough, use traditional mathematical analysis … get exact results, lots of insight into model  Queueing theory  Differential equations  Linear programming But complex systems can seldom be validly represented by a simple analytic model  Danger of over-simplifying assumptions … model validity? Often, a complex system requires a complex model, and analytical methods don’t apply … what to do?

23. Introduction : Simulation and Modeling Wk1Slide 23 Computer Simulation Broadly interpreted, computer simulation refers to methods for studying a wide variety of models of systems  Numerically evaluate on a computer  Use software to imitate the system’s operations and characteristics, often over time Can be used to study simple models but should not use it if an analytical solution is available Real power of simulation is in studying complex models Simulation can tolerate complex models since we don’t even aspire to an analytical solution

24. Introduction : Simulation and Modeling Wk1Slide 24 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? Most operational models:  Dynamic, Discrete-change, Stochastic

25. Introduction : Simulation and Modeling Wk1Slide 25 Using Computers to Simulate General-purpose languages (FORTRAN)  Tedious, low-level, error-prone  But, almost complete flexibility Support packages  Subroutines for list processing, bookkeeping, time advance  Widely distributed, widely modified Spreadsheets  Usually static models  Financial scenarios, distribution sampling, SQC

26. Introduction : Simulation and Modeling Wk1Slide 26 Using Computers to Simulate (cont’d.) Simulation languages  GPSS, SIMSCRIPT, SLAM, SIMAN  Popular, still in use  Learning curve for features, effective use, syntax High-level simulators  Very easy, graphical interface  Domain-restricted (manufacturing, communications)  Limited flexibility — model validity?

27. Introduction : Simulation and Modeling Wk1Slide 27 The System: A Simple Processing System Arriving Blank Parts Departing Finished Parts Machine (Server) Queue (FIFO) Part in Service 4567 General intent:  Estimate expected production  Waiting time in queue, queue length, proportion of time machine is busy Time units  Can use different units in different places … must declare  Be careful to check the units when specifying inputs  Declare base time units for internal calculations, outputs  Be reasonable (interpretation, roundoff error)

28. Introduction : Simulation and Modeling Wk1Slide 28 Model Specifics Initially (time 0) empty and idle Base time units: minutes Input data (assume given for now …), in minutes: Part NumberArrival TimeInterarrival TimeService Time 10.001.732.90 21.731.351.76 33.080.713.39 43.790.624.52 54.4114.284.46 618.690.704.36 719.3915.522.07 834.913.153.36 938.061.762.37 1039.821.005.38 11 40.82...... Stop when 20 minutes of (simulated) time have passed

29. Introduction : Simulation and Modeling Wk1Slide 29 Goals of the Study: 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 WQ i = waiting time in queue of ith part Know: WQ 1 = 0 (why?) N > 1 (why?)

30. Introduction : Simulation and Modeling Wk1Slide 30 Goals of the Study: 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 (a.k.a. cycle time ): Q(t) = number of parts in queue at time t TS i = time in system of part i

31. Introduction : Simulation and Modeling Wk1Slide 31 Goals of the Study: Output Performance Measures (cont’d.) Utilization of the machine (proportion of time busy) Many others possible (information overload?)

32. Introduction : Simulation and Modeling Wk1Slide 32 Pieces of a Simulation Model Entities  “Players” that move around, change status, affect and are affected by other entities  Dynamic objects — get created, move around, leave (maybe)  Usually represent “real” things – Our model: entities are the parts  Can have “fake” entities for modeling “tricks” – Breakdown demon, break angel  Usually have multiple realizations floating around  Can have different types of entities concurrently  Usually, identifying the types of entities is the first thing to do in building a model

33. Introduction : Simulation and Modeling Wk1Slide 33 Pieces of a Simulation Model (cont’d.) Attributes  Characteristic of all entities: describe, differentiate  All entities have same attribute “slots” but different values for different entities, for example: – Time of arrival – Due date – Priority – Color  Attribute value tied to a specific entity  Like “local” (to entities) variables  Some automatic in Arena, some you define

34. Introduction : Simulation and Modeling Wk1Slide 34 Pieces of a Simulation Model (cont’d.) (Global) Variables  Reflects a characteristic of the whole model, not of specific entities  Used for many different kinds of things – Travel time between all station pairs – Number of parts in system – Simulation clock (built-in Arena variable)  Name, value of which there’s only one copy for the whole model  Not tied to entities  Entities can access, change variables  Writing on the wall  Some built-in by Arena, you can define others

35. Introduction : Simulation and Modeling Wk1Slide 35 Pieces of a Simulation Model (cont’d.) Resources  What entities compete for – People – Equipment – Space  Entity seizes a resource, uses it, releases it  Think of a resource being assigned to an entity, rather than an entity “belonging to” a resource  “A” resource can have several units of capacity – Seats at a table in a restaurant – Identical ticketing agents at an airline counter  Number of units of resource can be changed during the simulation

36. Introduction : Simulation and Modeling Wk1Slide 36 Pieces of a Simulation Model (cont’d.) Queues  Place for entities to wait when they can’t move on (maybe since the resource they want to seize is not available)  Have names, often tied to a corresponding resource  Can have a finite capacity to model limited space — have to model what to do if an entity shows up to a queue that’s already full  Usually watch the length of a queue, waiting time in it

37. Introduction : Simulation and Modeling Wk1Slide 37 Pieces of a Simulation Model (cont’d.) Statistical accumulators  Variables that “watch” what’s happening  Depend on output performance measures desired  “Passive” in model — don’t participate, just watch  Many are automatic in Arena, but some you may have to set up and maintain during the simulation  At end of simulation, used to compute final output performance measures

38. Introduction : Simulation and Modeling Wk1Slide 38 Pieces of a Simulation Model (cont’d.) Statistical accumulators for the simple processing system  Number of parts produced so far  Total of the waiting times spent in queue so far  No. of parts that have gone through the queue  Max time in queue we’ve seen so far  Total of times spent in system  Max time in system we’ve seen so far  Area so far under queue-length curve Q ( t )  Max of Q ( t ) so far  Area so far under server-busy curve B ( t )

39. Introduction : Simulation and Modeling Wk1Slide 39 Simulation Dynamics: The Event-Scheduling “World View” Identify characteristic events Decide on logic for each type of event to  Effect state changes for each event type  Observe statistics  Update times of future events (maybe of this type, other types) Keep a simulation clock, future event calendar Jump from one event to the next, process, observe statistics, update event calendar Must specify an appropriate stopping rule Usually done with general-purpose programming language (C, FORTRAN, etc.)

40. Introduction : Simulation and Modeling Wk1Slide 40 Events for the Simple Processing System Arrival of a new part to the system  Update time-persistent statistical accumulators (from last event to now) – Area under Q ( t ) – Max of Q ( t ) – Area under B ( t )  “Mark” arriving part with current time (use later)  If machine is idle: – Start processing (schedule departure), Make machine busy, Tally waiting time in queue (0)  Else (machine is busy): – Put part at end of queue, increase queue-length variable  Schedule the next arrival event

41. Introduction : Simulation and Modeling Wk1Slide 41 Events for the Simple Processing System (cont’d.) Departure (when a service is completed)  Increment number-produced stat accumulator  Compute & tally time in system (now - time of arrival)  Update time-persistent statistics (as in arrival event)  If queue is non-empty: – Take first part out of queue, compute & tally its waiting time in queue, begin service (schedule departure event)  Else (queue is empty): – Make the machine idle (Note: there will be no departure event scheduled on the future events calendar, which is as desired)

42. Introduction : Simulation and Modeling Wk1Slide 42 Events for the Simple Processing System (cont’d.) The End  Update time-persistent statistics (to end of the simulation)  Compute final output performance measures using current (= final) values of statistical accumulators After each event, the event calendar’s top record is removed to see what time it is, what to do Also must initialize everything

43. Introduction : Simulation and Modeling Wk1Slide 43 Simulation by Hand: Setup

44. Introduction : Simulation and Modeling Wk1Slide 44 Simulation by Hand: t = 0.00, Initialize

45. Introduction : Simulation and Modeling Wk1Slide 45 Simulation by Hand: t = 0.00, Arrival of Part 1 1

46. Introduction : Simulation and Modeling Wk1Slide 46 Simulation by Hand: t = 1.73, Arrival of Part 2 12

47. Introduction : Simulation and Modeling Wk1Slide 47 Simulation by Hand: t = 2.90, Departure of Part 1 2

48. Introduction : Simulation and Modeling Wk1Slide 48 Simulation by Hand: t = 3.08, Arrival of Part 3 23

49. Introduction : Simulation and Modeling Wk1Slide 49 Simulation by Hand: t = 3.79, Arrival of Part 4 234

50. Introduction : Simulation and Modeling Wk1Slide 50 Simulation by Hand: t = 4.41, Arrival of Part 5 2345

51. Introduction : Simulation and Modeling Wk1Slide 51 Simulation by Hand: t = 4.66, Departure of Part 2 345

52. Introduction : Simulation and Modeling Wk1Slide 52 Simulation by Hand: t = 8.05, Departure of Part 3 45

53. Introduction : Simulation and Modeling Wk1Slide 53 Simulation by Hand: t = 12.57, Departure of Part 4 5

54. Introduction : Simulation and Modeling Wk1Slide 54 Simulation by Hand: t = 17.03, Departure of Part 5

55. Introduction : Simulation and Modeling Wk1Slide 55 Simulation by Hand: t = 18.69, Arrival of Part 6 6

56. Introduction : Simulation and Modeling Wk1Slide 56 Simulation by Hand: t = 19.39, Arrival of Part 7 67

57. Introduction : Simulation and Modeling Wk1Slide 57 Simulation by Hand: t = 20.00, The End 67

58. Introduction : Simulation and Modeling Wk1Slide 58 Simulation by Hand: Finishing Up Average waiting time in queue: Time-average number in queue: Utilization of drill press:

59. Introduction : Simulation and Modeling Wk1Slide 59 Comparing Alternatives Usually, simulation is used for more than just a single model “configuration” Often want to compare alternatives, select or search for the best (via some criterion) Simple processing system: What would happen if the arrival rate were to double?  Cut interarrival times in half  Rerun the model for double-time arrivals

60. Introduction : Simulation and Modeling Wk1Slide 60 Overview of a Simulation Study Understand the system Be clear about the goals Formulate the model representation Translate into modeling software Verify “program” Validate model Design experiments Make runs Analyze, get insight, document results

61. Introduction : Simulation and Modeling Wk1Slide 61 Review Questions Explain the following concepts, giving examples of the different types of each :  Simulation  Models  Systems Explain the advantages and disadvantages using simulation Using a bank system or supermarket, identify the elements of variability, interconnectedness and complexity Using examples of systems on Slide 10, clearly outline what aspects make simulation appropriate for at least 3 situations