# Simulation



## eng. ahmed elkady (4 يناير 2010)

INTRODUCTION TO SIMULATION

1. Definition of Simulation Technique 

Simulation is one of the most powerful tools available to decision-makers responsible for the design and operation of complex processes and systems. It makes possible the study, analysis and evaluation of situations that would not be otherwise possible. In an increasingly competitive world, simulation became an indispensable problem solving methodology for engineers [1], designers and managers. 
Simulation is the imitation of the operation of a real-world process or system over time. Simulation involves the generation of an artificial history of a system, and the observation of that artificial history to draw inferences concerning the operating characteristics of the real system that is represented. Simulation is an indispensable problem-solving methodology for the solution of many real-world problems. Simulation is used to describe and analyze the behavior of a system, ask "what if" questions about the real system, and to aid in the design of real systems. Both existing and conceptual systems can be modeled with simulation.
2. Modeling Concepts 
There are several concepts underlying simulation. These include system and model, events, system state variables, entities and attributes, list processing, activities and delays, and finally the definition of discrete-event simulation. Additional information on these topics is available from Banks, Carson and Nelson [3] and Law and Kelton [2]. The discussion in this section follows that of Carson [4].
2.1 System, Model and Events 
A model is a representation of an actual system. Immediately, there is a Concern about the limits or boundaries of the model that supposedly represent the system. The model should be complex enough to answer the questions raised, but not too complex. Consider an event as an occurrence that changes the state of the system.
2.2 System State Variables
The system state variables are the collection of all information needed to define what is happening within the system to a sufficient level (i.e., to attain the desired output) at a given point in time. The determination of system state variables is a function of the purposes of the investigation, so what may be the system state variables in one case may not be the same in another case even though the physical system is the same. Determining the system state variables is as much an art as a science. However, during the modeling process, any omissions will readily come to light. (And, on the other hand, unnecessary state variables may be eliminated.) Having defined system state variables, a contrast can be made between discrete-event models and continuous models based on the variables needed to track the system state. The system state variables in a discrete-event model remain constant over intervals of time and change value only at certain well-defined points called event times. Continuous models have system state variables defined by Differential or difference equations giving rise to variables that may change continuously over time. Some models are mixed discrete-event and continuous. There are also continuous models that are treated as discrete-event models after some reinterpretation of system state Variables, and vice versa.
2.3 Entities and Attributes
an entity represents an object that requires explicit definition. An entity can be dynamic in that it "moves" through the system, or it can be static in that it serves other entities. An entity may have attributes that pertain to that entity alone. Thus, attributes should be considered as local values. 
2.4 Resources
a resource is an entity that provides service to dynamic entities. The resource can serve one or more than one dynamic entity at the same time, i.e., operates as a parallel server. A dynamic entity can request one or more units of a resource. If denied, the requesting entity joins a queue, or takes some other action (i.e., diverted to another resource, ejected from the system). (Other terms for queues include files, chains, buffers, and waiting lines.) If permitted to capture the resource, the entity remains for a time, and then releases the resource. There are many possible states of the resource. Minimally, these states are idle and busy. But other possibilities exist including failed, blocked, or starved.
2.5 List Processing 
Entities are managed by allocating them to resources that provide service, by attaching them to event notices thereby suspending their activity into the future, or by placing them into an ordered list. Lists are used to represent queues. Lists are often processed according to FIFO (first-in first-out), but there are many other possibilities. For example, the list could be processed by LIFO (last-in-first out), According to the value of an attribute, or randomly, to mention a few. 
2.6 Activities and Delays
an activity is duration of time whose duration is known prior to commencement of the activity. Thus, when the duration begins, its end can be scheduled. The duration can be a constant, a random value from a statistical distribution, the result of an equation, input from a file, or computed based on the event state. 
2.7 Discrete-Event Simulation Model
Sufficient modeling concepts have been defined so that a discrete event simulation model can be defined as one in which the state variables change only at those discrete points in time at which events occur. Events occur as a consequence of activity times and delays. Entities may compete for system resources, possibly joining queues while waiting for an available resource. Activity and delay times may "hold" entities for durations of time. A discrete-event simulation model is conducted over time ("run") by a mechanism that moves simulated time forward. The system state is updated at each event along with capturing and freeing of resources that may occur at that time.

3. Advantages and Disadvantages of Simulation
Simulation have a number of advantages over analytical or mathematical models for analyzing systems. First of all, the basic concept of simulation is easy to comprehend and hence often easier to justify to management or customers than some of the analytical models. In addition, a simulation model may be more credible, because its behavior has been compared to that of the real system or because it requires fewer simplifying assumptions and hence captures more of the true characteristics of the system under study. Additional advantages include [1], [3].
We can test new designs, layouts, etc.​​1. without committing resources to their implementation.
2. It can be used to explore new staffing policies, operating procedures, decision rules, organizational structures, information flows, etc. without disrupting the ongoing operations.
Simulation allows us to identify bottlenecks in​​3. information, material and product flows and test options for increasing the flow rates.
It allows us to test hypothesis about how or why certain phenomena​​4. Occur in the system.
Simulation allows us to control time. Thus we can​​5. Operate the system for several months or years of experience in a matter of seconds allowing us to quickly look at long time horizons or we can slow down phenomena for study.
It allows us to gain insights into how a modeled​​6. System actually works and understanding of which variables are most important to performance.
7. Simulation's great strength is its ability to let us experiment with new and unfamiliar situations and to answer "what if" questions.
Even though simulation has many strengths and advantages, it is not without drawbacks. Among these are:
1.Simulation modeling is an art that requires specialized training and therefore skill levels of practitioners vary widely. The utility of the study depends upon the quality of the model and the skill of the modeler.
Gathering highly reliable input data can be time​​2. Consuming and the resulting data is sometimes highly questionable. Simulation cannot compensate for inadequate data or poor management decisions.
Simulation models are input-output models, i.e. they yield the​​3. Probable output of a system for a given input. They are therefore "run" rather than solved. They do not yield an optimal solution; rather they serve as a tool for analysis of the behavior of a system under conditions specified by the experimenter.​​​​Engineer Ahmed elkady ​​Industrial engineer ​​4 / 1 / 2010​​​


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## eng. ahmed elkady (4 يناير 2010)

بسم الله وعلى الله نتوكل
هذا هو اول موضوع لى فى هذا المنتدى المفضل لى ارجوا من الله ان يكون هذا العمل خالصاً لوجه الكريم وارجوا من الاخوة الكرام اضافة التعليقات والتوجيهات لكى استفيد من ارائكم .
ولكم جزيل الشكر.


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## صناعي1 (5 يناير 2010)

مرحبا بك مهندس احمد و جزاك الله خيرا


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## eng. ahmed elkady (5 يناير 2010)

وجزاك الله خيراً ايضاً


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## سلطية (6 مارس 2010)

السلام عليكم 
انا بحاجة لمشروع عن المحاكاة باستخدام arena


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## Up 2 Date (6 مارس 2010)

*بداية أحب أشكر المهندس أحمد على موضوعة الجميل أنا درست المحاكاة ولكن للأسف لم نتطرق ابدا الى البرامج التي من خلالها يمكن تطبيق المحاكاة وهنا أتمنى لو نساعد بعض ونوجدمايسمى بcase study ندرسها سويا ونتشاور فيها كذلك نعرف ماهي البرامج المناسبة وافضلية اي برنامج بالنسبة للاخر اتمنى اكون عرفت اوصل فكرتي وياريت يكون فيه تفاعل اكتر من كده
مع تحــياتي*
​


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## safa aldin (29 أبريل 2010)

جزاك الله خيرا


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## safa aldin (29 أبريل 2010)

جزاك الله خيرا:85::75::16:


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## SALAHEDEEN (26 مايو 2010)

i need answers for chapters exercises of simulation with arena 5th edition by kelton


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## SALAHEDEEN (26 مايو 2010)

i need answer for simulation with arena 5th edition


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