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Pratima RaySuivre

15 Aug 2014•0 j'aime•29,492 vues

15 Aug 2014•0 j'aime•29,492 vues

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Formation

Subject : Operations Research

Pratima RaySuivre

Simulation and its applicationAlesh Dulal

Operations Research - ModelsSundar B N

Introduction to Simulationchimco.net

Facility Location/Prduction Design & Development/Process Selectionviveksangwan007

Manufacturing systems Sumit Bhattacharya

Operations Research: Significance and limitations Sanjeet Yadav

- 1. Applications of simulation in business Pratima Ray MMS Sem III Presented by :
- 2. What is simulation ? Real System. MODEL of Real System. (Complex real-world system ) A process of designing (Conducting experiments with THIS model ) “SIMULATION is the process of designing a MODEL of a real system and conducting a series of repeated trial-and-error (for which there are no optimal solutions as such, unlike mathematical models) experiments with this MODEL for the purpose of either understanding the behavior of the system and/or evaluating various strategies for the operation of the system.”
- 3. Wooden, mechanical, horse simulator during World War I Human-in- the-loop simulation of outer space Visualization of a direct numerical simulation model. Military Simulation Life simulation games
- 4. When to use Simulation ? Real System. MODEL of Real System. • To stimulate is to duplicate (imitate) the features, appearance and characteristics of a real system. • The idea behind this is :- to initiate a real world situation mathematically, then to study its properties and finally to draw conclusions. • Real-life system is not touched UNTIL the advantages and disadvantages are first measured on the SYSTEM’S MODEL. • For making decisions on very complex problems for which there are no optimal solutions. • Problems which need analytical(logical reasoning) approaches/which cannot be definitively quantified. • Risky to attempt straight and optimal solutions/decisions. • When it is not advisable to experiment with reality itself. •To study almost any problem that involves uncertainty. • Where mathematical simplification is not feasible. • Lack of time to gather operating data from a real system. • When real system can get very costly.
- 5. Simulation’s greatest strength is its ability to answer “what if” questions... Advantages • Straight forward and flexible • suitable to analyze large and complex real-life problems • Sometimes simulation is the only method available • Does not interfere with real world system. • It may be used over and over to analyze different situations. • Breaking down of complicated systems into sub systems • Data for further analysis can easily be generated • Avoids cost of real world experimentation. • serves as a ‘pre-service test’
- 6. Disadvantages • Sometimes simulation models are expensive and take a long time to develop. • It is a trial and error approach that may produce different solutions in repeated runs • It is often too long and a complicated process to develop a model. Simulation results are sometimes hard to interpret. • Difficult for people to understand that they are not looking at reality but an abstraction of the real world • Each application of simulation is ad-hoc to ……a great extent. • The simulation model does not produce any `````answers by itself the user has to provide all ````` the constraints for the solutions that he ````` ````` wants to examine.
- 8. A Simulation Model • SIMULATION is a numerical technique for conducting experiments on a digital computer (It is a technique which uses computers). • SIMULATION MODEL represents a system using number and symbols that ca be readily manipulated.
- 9. Simulation Modelling Classifications Static vs. Dynamic: • Static: No attempts to model a time sequence of changes. • Dynamic: Updating each entity at each occurring event. Deterministic vs. Stochastic: • Deterministic: Rule based. • Stochastic: Based on conditional probabilities. Discrete vs. Continuous: • Discrete: Changes in the state of the system occur instantaneously at random points in time as a result of the occurrence of discrete events. • Continuous: Changes of the state of the system occur continuously over time.
- 10. Stochastic Simulation • (Definition)When a system contains certain factors that can be represented by a probability distribution • Probability distribution is used to quantify the outcomes. Eg. Flipping of a coin, outcome{H,T} • A random variable assigns number to the possible occurrence to each outcome. • There are 2 techniques of simulation: – Monte-Carlo: used for decision making under uncertainty – System simulation technique: reproduction of operating system • Monte-Carlo technique is generally used.
- 11. Monte-Carlo: • This technique uses random number and is generally used to solve problems requiring decision making under uncertainty and where mathematical formulation is impossible. • It is a recent O.R innovation(treated as a synonym for simulation) • Novelty lies in making use of pure chances to construct a simulated version of process exactly as pure chances operates the original system under working conditions Table of random numbers • Monte-Carlo sim. requires generation of random numbers(generated using digital computers) that is an integral part of the observations (samples) from the probability distribution.
- 12. Application of simulation in business with example • Simulation is used in almost all fields, restricted by our imagination and the ability to translate such imagination into computer directives. • BUSINESS Applications: stock and commodity analysis, pricing policies, marketing strategies, cash flow analysis, forecasting, etc. • Considering an example of how sim. can be used in queuing system. Sometimes careful analysis reveals a great output rate than thought possible. Considering an EXAMPLE of university Health-service Outpatient Clinic where this analysis was preferred Strategy was to build a Mount-Carlo Simulation to use the model experimentally and improve the clinic operations.
- 15. Expected processing time = 866/10 = 86.6
- 16. Thank You

- One of the most widely used O.R techniques AS it is a versatile tool which provides solution to a variety of O.R problems which are otherwise difficult to solve.
- It is a technique (quantitative or otherwise) for carrying out experiments for analysing the behaviour and evaluating the performance of a proposed system under assumed condition of reality.
- A procedure for testing and experimenting on models to answer to answer what if…, then so and so.. types of questions Relatively straight forward and flexible and can be modified to accommodate changing environments of real situation. This approach is suitable to analyze large and complex real-life problems that cannot be solved by usual quantitative methods. Sometimes simulation is the only method available (when all other techniques fail) Does not interfere with real world system. It may be used over and over to analyze all kinds of different situations. Breaking down of complicated systems into sub systems then study each of them individually or jointly Data for further analysis can easily be generated from stimulation model Avoids cost of real world experimentation. It serves as a ‘pre-service test’ to trace out new policies and decision rules before running the risk of experimenting on the real system.
- Gathering highly reliable input data can be time consuming and therefore expensive. Sometimes simulation models are expensive and take a long time to develop. For eg a corporate planning model may take a long time to develop and may alsoprove to be expensive. It is a trial and error approach that may produce different solutions in repeated runs It is often too long and a complicated process to develop a model. Difficult for people (who built it)to understand that they are not looking at reality but an abstraction of the real world Each application of simulation is ad-hoc to a great extent. The simulation model does not produce any answers by itself the user has to provide all the constraints for the solutions that he wants to examine
- SIMULATION is a numerical technique for conducting experiments on a digital computer , which involves certain types of mathematical and logical relationships necessary to describe the behaviour and structure of complex real world system over extended periods of time.
- (definition)When a system contains certain factors that can be represented by a probability distribution Eg. Flipping of a coin, outcome{H,T} A random variable assigns number to the possible occurrence to each outcome. In simulation random variables are numerically controlled and are used to stimulate elements of uncertainty that are defined in a model.
- Give example
- Waiting lines are an important consideration in capacity planning. Waiting lines tie up additional resources (waiting space, time, etc.); they decrease the level of customer service: and they require additional capacity to reduce them. 8. Most of the models described in the chapter assume arrivals are processed on a first-come, first-served basis (FCFS). Many examples of FCFS exist. Sometimes, however, customers are processed on a priority basis rather than FCFS. That is, late arriving customers may be processed ahead of those already waiting. A hospital emergency room is an example; seriously ill or injured persons are attended to while less seriously ill persons wait. A key difference in the multiple priority model compared to other models is computation of average waiting times, and average number waiting, for each of the classes or categories of waiting customers. 2. Waiting lines(can occur in any business) occur whenever demand for service exceeds capacity (supply). Even in systems that are underloaded, waiting lines tend to form if arrival and service patterns are highly variable because the variability creates temporary imbalances of supply and demand.