# Monthly Archives: May 2013

## Quiz Simkom 5 Simulation Optimization

Simulation Optimization is providing solutions to important practical problems previously beyond reach.  This paper explores how new approaches are significantly expanding the power of Simulation Optimization for managing risk.  Recent advances in Simulation Optimization technology are leading to new opportunities to solve problems more effectively. Specifically, in applications involving risk and uncertainty, Simulation Optimization surpasses the capabilities of other optimization methods, not only in the quality of solutions, but also in their interpretability and practicality.  In this paper, we demonstrate the advantages of using a Simulation Optimization approach to tackle risky decisions, by showcasing the methodology on two popular applications from the areas of finance and business process design.

Whenever uncertainty exists, there is risk.  Uncertainty is present when there is a possibility that the outcome of a particular event will deviate from what is expected.  In some cases, we can use past experience and other information to try to estimate the probability of occurrence of different events.  This allows us to estimate a probability distribution for all possible events.  Risk can be defined as the probability of occurrence of an event that would have a negative effect on a goal.  On the other hand, the probability of occurrence of an event that would have a positive impact is considered an opportunity (see Ref. 1 for a detailed discussion of risks and opportunities).  Therefore, the portion of the probability distribution that represents potentially harmful, or unwanted, outcomes is the focus of risk management.

Risk management is the process that involves identifying, selecting and implementing measures that can be applied to mitigate risk in a particular situation.1  The objective of risk management, in this context, is to find the set of actions (i.e., investments, policies, resource configurations, etc.) to reduce the level of risk to acceptable levels.  What constitutes an acceptable level will depend on the situation, the decision makers’ attitude towards risk, and the marginal rewards expected from taking on additional risk.  In order to help risk managers achieve this objective, many techniques have been developed, both qualitative and quantitative.  Among quantitative techniques, optimization has a natural appeal because it is based on objective mathematical formulations that usually output an optimal solution (i.e. set of decisions) for mitigating risk.  However, traditional optimization approaches are prone to serious limitations.

In Section 2 of this paper, we briefly describe two prominent optimization techniques that are frequently used in risk management applications for their ability to handle uncertainty in the data; we then discuss the advantages and disadvantages of these methods.  In Section 3, we discuss how Simulation Optimization can overcome the limitations of traditional optimization techniques, and we detail some innovative methods that make this a very useful, practical and intuitive approach for risk management.  Section 4 illustrates the advantages of Simulation Optimization on two practical examples.  Finally, in Section 5 we summarize our results and conclusions.

Very few situations in the real world are completely devoid of risk.  In fact, a person would be hard-pressed to recall a single decision in their life that was completely risk-free.  In the world of deterministic optimization, we often choose to “ignore” uncertainty in order to come up with a unique and objective solution to a problem.  But in situations where uncertainty is at the core of the problem – as it is in risk management – a different strategy is required.

In the field of optimization, there are various approaches designed to cope with uncertainty.2,3  In this context, the exact values of the parameters (e.g. the data) of the optimization problem are not known with absolute certainty, but may vary to a larger or lesser extent depending on the nature of the factors they represent.  In other words, there may be many possible “realizations” of the parameters, each of which is a possible scenario.

Traditional scenario-based approaches to optimization, such as scenario optimization and robust optimization, are effective in finding a solution that is feasible for all the scenarios considered, and minimizing the deviation of the overall solution from the optimal solution for each scenario.  These approaches, however, only consider a very small subset of possible scenarios, and the size and complexity of models they can handle are very limited.

Robust Optimization

Robust optimization may be used when the parameters of the optimization problem are known only within a finite set of values.  The robust optimization framework gets its name because it seeks to identify a robust decision – i.e. a solution that performs well across many possible scenarios.

In order to measure the robustness of a given solution, different criteria may be used.  Kouvelis and Yu identify three criteria:  (1) Absolute robustness; (2) Robust deviation; and (3) Relative robustness.  We illustrate the meaning and relevance of these criteria, by describing their robust optimization approach.

Posted by on 28/05/2013 in Uncategorized

## Quiz Simkom 4 Simulation Output Analysis

Input processes driving a simulation are random variables (e.g.,interarrival times, service times, and breakdown times). Must regard the output from the simulation as random. Runs of the simulation only yield estimates of measures of system performance (e.g., the mean customer waiting time). These estimators are themselves random variables, and are therefore subject to sampling error. Must take sampling error must be taken into account to make valid inferences concerning system performance.

Problem: simulations almost never produce raw output that is independent and identically distributed (i.i.d.) normal data. Example: Customer waiting times from a queueing system. . .

(1) Are not independent — typically, they are serially correlated. If one customer at the post office waits in line a long time, then the next customer is also likely to wait a long time.

(2) Are not identically distributed. Customers showing up early in the morning might have a much shorter wait than those who show up just before closing time.

(3) Are not normally distributed — they are usually skewed to the right (and are certainly never less than zero).

Thus, it’s difficult to apply “classical” statistical techniques to the analysis of simulation output.Our purpose: Give methods to perform statistical analysis ofoutput from discrete-event computer simulations.

Types of Simulations

To facilitate the presentation, we identify two types of simulations with respect to output analysis: Finite-Horizon (Terminating) and Steady-State simulations.

Finite-Horizon Simulations: The termination of a finite-horizon simulation takes place at a specific time or is caused by the occurrence of a specific event. Examples are:

• Mass transit system between during rush hour.
• Distribution system over one month.
• Production system until a set of machines breaks down.
• Start-up phase of any system — stationary or nonstationary

Steady-state simulations: The purpose of a steady-state simulation is the study of the long-run behavior of a system. A performance measure is called a steady-state parameter if it is a characteristic of the equilibrium distribution of an output stochastic process. Examples are: Continuously operating communication system where the objective is the computation of the mean delay of a packet in the long run. Distribution system over a long period of time.

here I attach the course video of Simulation Output Analysis to provide more information

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Posted by on 28/05/2013 in Uncategorized

## Quiz Simkom 3 Model Building part 2

“Simulator” redirects here. For other uses, see Simulation (disambiguation) and Simulator (disambiguation). Not to be confused with Stimulation.

Simulation is the imitation of the operation of a real-world process or system over time. The act of simulating something first requires that a model be developed; this model represents the key characteristics or behaviors/functions of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system over time.

Simulation is used in many contexts, such as simulation of technology for performance optimization, safety engineering, testing, training, education, and video games. Simulation is also used with scientific modelling of natural systems or human systems to gain insight into their functioning. Simulation can be used to show the eventual real effects of alternative conditions and courses of action. Simulation is also used when the real system cannot be engaged, because it may not be accessible, or it may be dangerous or unacceptable to engage, or it is being designed but not yet built, or it may simply not exist

Key issues in simulation include acquisition of valid source information about the relevant selection of key characteristics and behaviours, the use of simplifying approximations and assumptions within the simulation, and fidelity and validity of the simulation outcomes.

so from information above, I’ll show you the example of simulation of air plane

Posted by on 28/05/2013 in Uncategorized

## Quiz Simkom 2 Model Verification and Validation

Verification and Validation of Simulation Models

• Verification: concerned with building the model right. It is utilized in the comparison of the conceptual model to the computer representation that implements that conception.
• It asks the questions: Is the model implemented correctly in the computer? Are the input parameters and logical structure of the model correctly represented?
• Validation: concerned with building the right model. It is utilized to determine that a model is an accurate representation of the real system. Validation is usually achieved through the calibration of the model, an iterative process of comparing the model to actual system behavior and using the discrepancies between the two, and the insights gained, to improve the model. This process is repeated until model accuracy is judged to be acceptable.

Verification of Simulation Models

Many common sense suggestions can be given for use in the verification process.

1. Have the code checked by someone other than the programmer.
2. Make a flow diagram which includes each logically possible action a system can take when an event occurs, and follow the model logic for each action for each event type.
3. Closely examine the model output for reasonableness under a variety of settings of the input parameters. Have the code print out a wide variety of output statistics.
4. Have the computerized model print the input parameters at the end of the simulation, to be sure that these parameter values have not been changed inadvertently.
5. Make the computer code as self-documenting as possible. Give a precise definition of every variable used, and a general description of the purpose of each major section of code.

These suggestions are basically the same ones any programmer would follow when debugging a computer program.

Calibration and Validationof Models

Optimization

Optimization is the appropiate technology to combine of values for the variable that can be controlled to seek the combination of value that provide the most desirable output from the simulation model.

The way to find the optimal solution is follow some steps:

1. Identify all possible decision variables that affect the output of the system
2. Based on the possible values of each decision variable, identify all possible solutions
3. Evaluate each of these solutions accurately
4. Compare each solution fairly

Here I provide you the video that represent how to verify and validate the model simulation

Posted by on 20/05/2013 in Uncategorized

## Quiz Simkom 1 Model Building part 1

Model Building

Simulation is often used:

• no suitable theoretical model exists
• the problem is so complex that a theoretical model cannot represent the interrelationships properly.
Simulation is ’the imitative representation of the functioning of one system or process by means of the functioning of another’
• Simulation is “the modeling of a process or system in such a way that the model mimics the response of the actual system to events that take place over time.”
• By studying the behavior of the model, insight about the behavior of the actual system can be gained.
• In practice,
1. Simulation is performed using commercial simulation software.
2. Performance statistics are gathered during the simulation
3. Modern simulation software provides a realistic, graphical animation of the system being modeled.
4. During the simulation, the user can interactively adjust the animation speed and change model parameter values to do “what-if” analysis on the fly.
5. State-of-the art simulation technology provides optimization capability

Why we choose to simulate?

• Simulation provides a way to validate whether or not the best decisions are being made.
• Simulation avoid the expensive, time-consuming, and disrupted nature of traditional trial-and-error techniques.
• The power of simulation lies in the fact that it provides a method of analysis that is not only formal and predictive, but is capable of accurately predicting the performance of a system.
• By using a computer to model a system before it is built or to test operating policies before they are actually implemented, many of the pitfalls can be avoided

When Simulation is Appropriate

• Not all system problemsthat could be solved with the aid of simulation should be solved using simulation,
• It is important to select the right toolfor the task.
• Simulation has certain limitationsof which one should be aware before making a decision to apply it to a given situation.
• As a general guideline, simulation is appropriate if:
1.  An operational (logical or quantitative) decision is being made.
2. The process being analyzed is well defined and repetitive.
3. Activities and events are interdependent and variable.
4. The cost impact of the decision is greater than the cost of doing the simulation.
5. The cost of experiment on the actual system is greater than the cost of simulation.

The process of  simulation experimentation