Simulation Sesi 12 Dosen Pembina: Danang Junaedi IF-UTAMA1

Simulation A technique for conducting experiments with a computer on a model of a management system Frequently used DSS tool Major Characteristics of Simulation ◦ Simulation imitates reality and capture its richness ◦ Simulation is a technique for conducting experiments ◦ Simulation is a descriptive not normative tool ◦ Simulation is often used to solve very complex, risky problems IF-UTAMA2

What is Simulation IF-UTAMA3

Simulation for Decision Makers  In DSS, simulation refers to a technique for conducting experiments with a computer on a model of a management system.  Characteristics of Simulation;  While models in general represent reality, simulation usually imitates it closely.  It is a technique for conducting experiments. It can describe and/or predict the characteristics of a given system under different circumstances.  It can be used for complex decision making IF-UTAMA4

Case : Simulation Saves Siemens Millions Problem:  Siemens Solar Industries (SSI), the world’s largest maker of solar electric products, suffered continuous problems in poor material flow, unbalanced resource use, bottlenecks in throughput & schedule delays. Solution:  SSI built a cleanroom contamination-control technology.  The simulation provided a virtual laboratory for engineers to experiment with various configurations before the physical systems were constructed. Results:  SSI improved their manufacturing process significantly.  The cleanroom facility saved SSI over $75 million/ year. IF-UTAMA5

Advantages and Disadvantages of Simulation IF-UTAMA6

Limitations of Simulation Cannot guarantee an optimal solution Slow and costly construction process Cannot transfer solutions and inferences to solve other problems So easy to sell to managers, may miss analytical solutions Software is not so user friendly IF-UTAMA7

Simulation Methodology Set up a model of a real system and conduct repetitive experiments 1. Problem Definition 2. Construction of the Simulation Model 3. Testing and Validating the Model 4. Design of the Experiments 5. Conducting the Experiments 6. Evaluating the Results 7. Implementation IF-UTAMA8

Simulation Types Probabilistic Simulation ◦ Distribution:  Discrete distributions : systems monitor the systems each time a change in its state takes place  Continuous distributions : system monitor changes in a state of system at descret points in time ◦ Probabilistic simulation via Monte Carlo technique ◦ Time Dependent versus Time Independent Simulation ◦ Simulation Software ◦ Visual Simulation ◦ Object-oriented Simulation IF-UTAMA9

Simulation Development IF-UTAMA10

Some Applications of Simulation IF-UTAMA11

Visual Spreadsheets User can visualize models and formulas with influence diagrams Not cells--symbolic elements IF-UTAMA12

Visual Interactive Modeling (VIM) Visual interactive modeling (VIM), also called ◦ Visual interactive problem solving ◦ Visual interactive modeling ◦ Visual interactive simulation Use computer graphics to present the impact of different management decisions. Can integrate with GIS Users perform sensitivity analysis Static or a dynamic (animation) systems IF-UTAMA13

Generated Image of Traffic at an Intersection from the Orca Visual Simulation Environment (Courtesy Orca Computer, Inc.) IF-UTAMA14

Visual Interactive Simulation (VIS) Decision makers interact with the simulated model and watch the results over time Visual interactive models and DSS ◦ Queueing IF-UTAMA15

Monte Carlo Simulation IF-UTAMA16

Monte Carlo Technique IF-UTAMA17

Step 1 Probability Distribution IF-UTAMA18

Step 2 Building a Cumulative Probability Distribution IF-UTAMA19

Step 3 Setting Random Number Interval IF-UTAMA20

Step 4 Generating Random Numbers IF-UTAMA21

Step 5 Simulating the Experience IF-UTAMA22

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Simulation of Queuing Problem IF-UTAMA24

Queuing Problem IF-UTAMA25

Dist 1 Inter-Arrival Times IF-UTAMA26

Dist 2 Unloading Times IF-UTAMA27

Example IF-UTAMA28

Example-contd : Some Simple Statistic IF-UTAMA29

Simulation and Inventory Analysis The Basic Model IF-UTAMA30

Referensi 1. Dr. Mourad YKHLEF,2009,Decision Support System-Simulation, King Saud University 2. Richard K. Min.2002.Information Systems for Management. OUR LADY OF THE LAKE UNIVERSITY SCHOOL OF BUSINESS 3. Insoo Hwang.-. Modeling and Analysis. Department of MIS, Jeonju university 4. Efraim Turban and Jay E. Aronson Decision Support Systems and Intelligent Systems 6th edition. Prentice Hall IF-UTAMA31