Simulation and Modelling MIS 7102

1. Simulation and Modelling MIS 7102 Introduction to Simulation & Modelling Week 1 By Agnes Rwashana Semwanga asemwanga@cit.mak.ac.ug Friday 6.00p.m.- 9.00p.m. Faculty of Technology Room 143 Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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. Introduction : Simulation and Modeling Wk1

6. Illustration(Descriptive – Performance Analysis) Simulation vs. Real World Introduction : Simulation and Modeling Wk1

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. Introduction : Simulation and Modeling Wk1

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. 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. Introduction : Simulation and Modeling Wk1

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. Introduction : Simulation and Modeling Wk1

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) Introduction : Simulation and Modeling Wk1

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. Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

17. Model Classification • Physical (prototypes) • Analytical (mathematical) • Computer (Monte Carlo Simulation) • Descriptive (performance analysis) • Prescriptive (optimization) Introduction : Simulation and Modeling Wk1

18. Physical (Prototypes) Introduction : Simulation and Modeling Wk1

19. Analytical (Mathematical) Single Stage Queuing Model Introduction : Simulation and Modeling Wk1

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. Introduction : Simulation and Modeling Wk1

21. Prescriptive (Optimization) Introduction : Simulation and Modeling Wk1

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? Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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? Introduction : Simulation and Modeling Wk1

27. The System:A Simple Processing System Machine (Server) Arriving Blank Parts Departing Finished Parts 7 6 5 4 Queue (FIFO) Part in Service • 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) Introduction : Simulation and Modeling Wk1

28. 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 Introduction : Simulation and Modeling Wk1

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 WQi = waiting time in queue of ith part Know: WQ1 = 0 (why?) N> 1 (why?) Introduction : Simulation and Modeling Wk1

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 TSi = time in system of part i Introduction : Simulation and Modeling Wk1

31. Goals of the Study:Output Performance Measures (cont’d.) • Utilization of the machine (proportion of time busy) • Many others possible (information overload?) Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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 Introduction : Simulation and Modeling Wk1

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) Introduction : Simulation and Modeling Wk1

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.) Introduction : Simulation and Modeling Wk1

40. Events for theSimple 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 Introduction : Simulation and Modeling Wk1

41. Events for theSimple 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) Introduction : Simulation and Modeling Wk1

42. Events for theSimple 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 Introduction : Simulation and Modeling Wk1

43. Simulation by Hand:Setup Introduction : Simulation and Modeling Wk1

44. Simulation by Hand:t = 0.00, Initialize Introduction : Simulation and Modeling Wk1

45. Simulation by Hand:t = 0.00, Arrival of Part 1 1 Introduction : Simulation and Modeling Wk1

46. Simulation by Hand:t = 1.73, Arrival of Part 2 2 1 Introduction : Simulation and Modeling Wk1

47. Simulation by Hand:t = 2.90, Departure of Part 1 2 Introduction : Simulation and Modeling Wk1

48. Simulation by Hand:t = 3.08, Arrival of Part 3 3 2 Introduction : Simulation and Modeling Wk1

49. Simulation by Hand:t = 3.79, Arrival of Part 4 4 3 2 Introduction : Simulation and Modeling Wk1

50. Simulation by Hand:t = 4.41, Arrival of Part 5 5 4 3 2 Introduction : Simulation and Modeling Wk1