Discrete event simulation - Revision history

Ghent9000: /* Computational model */

2013-01-26T18:56:55Z

‎Computational model

Ghent9000: /* Computational model */

2013-01-26T18:56:04Z

‎Computational model

Ghent9000: /* Building conceptual model */

2013-01-26T18:54:55Z

‎Building conceptual model

Ghent9000: /* Solved example */

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‎Solved example

Ghent9000: /* Real applications */

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‎Real applications

Ghent9000: /* Execution */

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‎Execution

Ghent9000: /* Definition */

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‎Definition

Ghent9000: /* Execution */

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‎Execution

Ghent9000: /* Execution */

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‎Execution

Ghent9000: /* Execution */

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‎Execution

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 With the information you gained in this exercise you can decide for yourself which kind of program you prefer. You can now download or buy this program and practice translating the conceptual model from exercise 1 in a computational model. Use several values and for each variable and see what happens. Learn by trial and error to understand how the system works. Try to analyze the reports and make a decision based on that report.
-If have followed all of the above steps you just generated your first reports and made a decision that was enforced by a discrete-event simulation. You are ready to solve more difficult problems and to become a real simulation engineer!
+If you have followed all of the above steps, you just generated your first reports and made a decision that was enforced by a discrete-event simulation. You are ready to solve more difficult problems and to become a real simulation engineer!
 = References =

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 ==Computational model==
-When you have your conceptual model ready, the next step is to execute the model in a computer program. There are two different types that are mainly used to do this. Can you name these two explain them briefly and give advantage and disadvantage?
+When you have your conceptual model ready, the next step is to execute the model in a computer program. There are two different types that are mainly used to do this. Can you name these two and explain them briefly and give advantages and disadvantages?
 With the information you gained in this exercise you can decide for yourself which kind of program you prefer. You can now download or buy this program and practice translating the conceptual model from exercise 1 in a computational model. Use several values and for each variable and see what happens. Learn by trial and error to understand how the system works. Try to analyze the reports and make a decision based on that report.

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 ==Building conceptual model==
 In order to fully understand how to build a conceptual model of a discrete-event simulation you should try to build a simple one yourself. Solve step 1 and 2 of “How to solve a discrete event simulation” of the following problem.
-At the post office there is one employee serving customers. Customers arrive according to an exponential distribution and occur at a rate of λ. The service time follows a log-normal distribution with mean μ and standard deviation σ. If there the post employee is not working the customers start queuing.  The customers will wait an infinite amount of time if necessary. What would happen if there would be a second employee serving customers?
+At the post office there is one employee serving customers. Customers arrive according to an exponential distribution and occur at a rate of λ. The service time follows a log-normal distribution with mean μ and standard deviation σ. If the post employee is not working the customers start queuing.  The customers will wait an infinite amount of time if necessary. What would happen if there would be a second employee serving customers?
 If you have solved this correctly you should find something like this:
 # Our goal is to simulate system with extra employee. What is going to happen to our queue?

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 Airline companies often have a telephone reservation system. In order to optimize this system we need to do a discrete-event simulation. The goal of this model is to simulate the effectiveness of the system with a different number of reservationists. The model can be described as follows:
-The customers can only make a reservation by telephone, so all customers have to call on the phone. Customers will not hang up so a customer can wait an indefinite amount of time. There is an unlimited amount of costumers that can be waiting on the phone at the same time. A new customer will get a 9-seconds recorded message in case all the reservation employees are busy. There are M reservationists working to answer the customer’s reservation needs. The incoming calls follow an exponential distribution and occur at a rate of λ. Customers can have two types of requests. First of all a flight reservation that requires multiple-destinations with probability p and secondly a single-destination flight reservation with probability 1-p. The service time of multiple-destination request follows a log-normal distribution with mean μ and standard deviation σ. The service time of a single-destination request follows a Weibull distribution with shape k and scale λ. The overall probability distribution of service time is a combined density function based on the probability and the distribution of the two options.
+The customers can only make a reservation by telephone, so all customers have to call on the phone. Customers will not hang up so a customer can wait an indefinite amount of time. There is an unlimited amount of costumers that can be waiting on the phone at the same time. A new customer will get a 9-seconds recorded message in case all the reservation employees are busy. There are M reservationists working to answer the customer’s reservation needs. The incoming calls follow an exponential distribution and occur at a rate of λ. Customers can have two types of requests. First of all a flight reservation that requires multiple-destinations with probability p and secondly a single-destination flight reservation with probability 1-p. The service time of a multiple-destination request follows a log-normal distribution with mean μ and standard deviation σ. The service time of a single-destination request follows a Weibull distribution with shape k and scale λ. The overall probability distribution of service time is a combined density function based on the probability and the distribution of the two options.
 We can solve this problem in the following steps:

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 * In hospitals and healthcare in general, simulations are used for modeling waiting list of patients, modeling facility changes and bed planning.
-* Banks and financial service firms use discrete-event-simulations for the modeling and simulation of their call centers and for the cash management of ATM’s.
+* Banks and financial service firms use discrete-event simulations for the modeling and simulation of their call centers and for the cash management of ATM’s.
 * The applications in logistics and transportation corporations are obvious: optimizing the material handling system, manpower planning and shipping strategies.
 * The public sector including the police and the army uses discrete-event simulations for the modeling of emergency cases and the engineering of criminal investigation process.
 =Solved example=

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 “For any important large-scale real application we would write the programs in a standard general-purpose language, and avoid all the simulation languages we know."
-<ref name="general-purpose">[ Bratley P, Fox B. L., Schrage L. E. , (1987). "A Guide to Simulation, " Springler-Verlag, New York </ref>
+<ref name="general-purpose"> Bratley P, Fox B. L., Schrage L. E. , (1987). "A Guide to Simulation, " Springler-Verlag, New York </ref>
-On the other hand: “we believe, in general, that a modeler would be prudent to give serious consideration to the use of a simulation package." <ref name="simulation">[ Law M. A., Kelton K. W., (2000). "Simulation Modeling and Analysis", McGraw-Hill Education </ref>
+On the other hand: “we believe, in general, that a modeler would be prudent to give serious consideration to the use of a simulation package." <ref name="simulation">Law M. A., Kelton K. W., (2000). "Simulation Modeling and Analysis", McGraw-Hill Education </ref>

← Older revision		Revision as of 18:49, 26 January 2013
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−	“Discrete event simulation utilizes a mathematical/logical model of a physical system that portrays state changes at precise points in simulated time. Both the nature of the state change and the time at which the change occurs mandate precise description. Customers waiting for service, the management of parts inventory or military combat are typical domains of discrete event simulation." <ref name="DES">[Nance, Richard E.(1993). "A History of Discrete Event Simulation," Virginia Poltechnic Institute and State University </ref>	+	“Discrete event simulation utilizes a mathematical/logical model of a physical system that portrays state changes at precise points in simulated time. Both the nature of the state change and the time at which the change occurs mandate precise description. Customers waiting for service, the management of parts inventory or military combat are typical domains of discrete event simulation." <ref name="DES">Nance, Richard E.(1993). "A History of Discrete Event Simulation," Virginia Poltechnic Institute and State University </ref>

	A more theoretical top-down description of discrete-event simulations is the following: The start of a discrete- event simulation is a model. A model is the construction of a conceptual framework, used to represent a system. The specific characteristics of discrete-event simulation model are the following:		A more theoretical top-down description of discrete-event simulations is the following: The start of a discrete- event simulation is a model. A model is the construction of a conceptual framework, used to represent a system. The specific characteristics of discrete-event simulation model are the following:

← Older revision		Revision as of 18:48, 26 January 2013
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	<ref name="general-purpose">[ Bratley P, Fox B. L., Schrage L. E. , (1987). "A Guide to Simulation, " Springler-Verlag, New York </ref>		<ref name="general-purpose">[ Bratley P, Fox B. L., Schrage L. E. , (1987). "A Guide to Simulation, " Springler-Verlag, New York </ref>

−	On the other hand ~~Law and Kelton (2000, page 204) state~~: “we believe, in general, that a modeler would be prudent to give serious consideration to the use of a simulation package." <ref name="simulation">[ Law M. A., Kelton K. W., (2000). "Simulation Modeling and Analysis", McGraw-Hill Education </ref>	+	On the other hand: “we believe, in general, that a modeler would be prudent to give serious consideration to the use of a simulation package." <ref name="simulation">[ Law M. A., Kelton K. W., (2000). "Simulation Modeling and Analysis", McGraw-Hill Education </ref>

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 The execution of the computational model is one of the most important issues when dealing with simulations. Generally there are two beliefs about how to solve this issue.
-“for any important large-scale real application we would write the programs in a standard general-purpose language, and avoid all the simulation languages we know."
+“For any important large-scale real application we would write the programs in a standard general-purpose language, and avoid all the simulation languages we know."
 <ref name="general-purpose">[ Bratley P, Fox B. L., Schrage L. E. , (1987). "A Guide to Simulation, " Springler-Verlag, New York </ref>
-On the other hand Law and Kelton (2000, page 204) state: “we believe, in general, that a modeler would be prudent to give serious consideration to the use of a simulation package."
+On the other hand Law and Kelton (2000, page 204) state: “we believe, in general, that a modeler would be prudent to give serious consideration to the use of a simulation package." <ref name="simulation">[ Law M. A., Kelton K. W., (2000). "Simulation Modeling and Analysis", McGraw-Hill Education </ref>
 ==General-purpose programming languages==
 Discrete-event simulation is a particular application of scientific computing. Therefore most general-purpose programming languages are suitable for solving discrete-event simulations. Nowadays C, C++ and Java are the most commonly used languages because of their popularity amongst all programmers. The main advantage of general-purpose programming languages is the flexibility and freedom they offer to programmers.