SIMULATION
Ders Bilgileri Dersin Kodu ve Adı Bölüm/Program Kullanılan Dil Dersi Veren Genel Amaç : Simulation (Benzetim) : MMF-Endüstri Mühendisliği Bölümü : İngilizce : Yard. Doç. Dr. GÜRKAN ÖZTÜRK Bu ders, kesikli olay benzetim düşüncesinin kullanılarak sistemlerin modellenmesi ve benzetiminin yapılmasını irdeler. Öğrenme Çıktıları ve Alt Beceriler Bu dersin sonunda öğrenci; benzetimin tanımını yapabilecek, benzetimin kullanım amaçlarını, avantaj ve dezavantajlarını sayabilecek, bir benzetim çalışmasının adımlarını açıklayabilecek, üretim ve servis sistemleri için hesap tablosu ve/veya Arena programı ile benzetim modeli kurabilecek, benzetim modelinin doğru çalışmasını sağlayabilecek ve geçerliliğini sınayabilecek, benzetim modelinin çıktılarını analiz ederek yorumlayabilecek, alternatif sistemler için model geliştirerek kıyaslamalar yapabilecek.
Ders Bilgileri Dersle İlgili Görüşme Saatleri Salı günler, Saat 14:00-15:00 arası Öğrenci Yükümlülükleri Öğrenciler düzenli olarak derslere katılmak, dersin internet sayfalarını takip etmek, verilen ödev ve projeleri zamanında teslim etmek ile yükümlüdür. Ayrıca dönem sonunda öğrenciler hazırladıkları projeleri sunacaktır. Ders Kitabı BCNN, Banks, J., Carson, J., Nelson B., Nicol, D., (2005) Discrete-Event System Simulation, 4th edition. Prentice Hall. KSS, Kelton, W. D., Sadowski R.P., and Sturrock, D.T., (2003) Simulation with Arena. Boston:McGraw- Hill Higher Education. AM, Altiok, T., Melamed B., (2007) Simulation Modeling And Analysis With Arena [electronic resource], California:Elsevier Inc. Önerilen Kaynaklar Law, A.M., and Kelton, W. D., (2000) Simulation Modeling and Analysis. New York:McGraw-Hill. Ross, S.M., (2002) Simulation, 3rd edition. Amsterdam : Academic Press.
Hafta Ders Bilgileri Haftalara Göre İşlenecek Konular Konu Ana Ders Kaynakları 1 Şubat - Benzetime giriş, uygulama alanları, Sistemler ve bileşenleri - Kesikli olay benzetimi, bir benzetim çalışmasının adımları BCNN-1 2 - Kesikli olay benzetimindeki kavramlar, Benzetim ve Bilgisayar - Rassal Sayılar BCNN-3 BCNN-7 3 - Rassal Değişkenler - Benzetim örnekleri ve hesaptablosu uygulamaları BCNN-8 BCNN-2 4 Mart - Girdi verilerinin analizi ve modellemesi - Arena, Temel işlemler ve girdilerin modellenmesi BCNN-9 KSS-4 5 - Model Geçerliliğinin doğrulanması - Arena, Detaylı işlemlerin modellenmesi BCNN-10 KSS-5 6 I. Ara sınavı 7 - Bir model için çıktı analizi BCNN-11 8 Nisan - Arena, Bitimli benzetim modelleri için çıktı analizi KSS-6 9 - Arena, Durağan durumlu modeller için çıktı analizi KSS-7 10 - Arena, Varlık Transferi KSS-8 11 - Alternatif sistemlerin kıyaslanması ve değerlendirilmesi BCNN-12 12 Mayıs II Ara sınavı 13 Proje Sunuşları 14 Proje Sunuşları 15 Telafi Haftası 16 Dönem Sonu Sınavları 17 Haziran Dönem Sonu Sınavları 18 19 Mazeret Sınavları
Ders Bilgileri Değerlendirme 1. Ara Sınav %20 40 20 2. Ara Sınav %20 20 Proje %20 20 Dönem Sonu Sınavı %40
Simulation A simulation is the imitation of the operation of a real-world process or system over time. Whether done by hand and or on a computer simulation involves the generation of an artificial history of a system and the observation of that artificial history to draw inferences concerning the operating characteristic of the real system.
Simulation The behavior of a system as it evolves over time is studied by developing a simulation model. Simulation modeling can be used both as an analysis tool for predicting the effect of changes to existing systems and as a design tool to predict the performance of new systems under varying sets of circumstances.
Simulation A model can be solved mathematically. However, Real world problems are very complex The simulation-generated data is used to estimate the measures of perfonmace of the system.
When simulation is the appropriate tool Simulation enables the study of, and experimentation with, the internal interactions of a complex system or of a subsystem within a complex system. Informational organizational, and environmental changes can be simulated, and the effect of these alterations on the model s behavior can be observed. The knowledge gained during the designing of a simulation model could be of great value toward suggesting improvement in the system under investigation. Changing simulation inputs and observing the resulting outputs can produce valuable insight into which variables are the most important and into how variables can interact.
When simulation is the appropriate tool (cont.) Simulation can be used to experiment with new designs or policies before implementation, so as to prepare for what might happen. Simulation can be used to verify analytic solution. Simulation models designed for training make learning possible without the cost and disruption of on-the-job instruction. Animation shows a system in simulated operation so that the plan can be visualized. The modern system (factory, wafer fabrication plant, service organization, etc.) is so complex that is internal interactions can be treated only through simulation.
When simulation is not the appropriate Simulation should not be used; when the problem can be solved by common sense. if the problem can be solved analytically. if it is easier to perform direct experiments. if the costs exceed the savings. if the resources and time are not available If no data available, not even estimates, simulation is not advised. If managers have unreasonable expectations, or if power of simulation is overestimated, simulation might not be appropriate. If system behavior is too complex or can t be defined, simulation is not appropriate.
Advantages of simulation New policies, operating procedures, decision rules, information flows, organizational procedures, and so on can be explored without disrupting ongoing operations of the real system. New hardware design, physical layouts, transportation systems, and so on can be tested without committing resources for their acquisition. Hypotheses about how and why certain phenomena occur can be tested for feasibility. Time can be compressed or expanded to allow for a speed-up or slowdown of the phenomena under investigation. Insights can be obtained about the interaction of variables. Insights can be obtained about the importance of variables to the performance of the system.
Advantages of simulation (cont.) Bottleneck analysis can be performed to discover where work in process, information, materials, and so on are being delayed excessively. A simulation study can help understanding how the system operates rather than how individuals think the system operates. what if questions can be answered. This particularly useful in the design of new systems.
Disadvantages of simulation Model building requires special training. Simulation results can be difficult to interpret. Simulation modeling and analysis can be time consuming and expensive. Simulation is used in some cases when analytical solution is possible.
Areas of applications Manufacturing applications Construction engineering and project management Military applications Logistics, supply chain, and distribution systems Transportation models and traffic Business process simulation Health care http://wintersim.org/
Trends in simulation applications Risk analysis, including in such areas Insurance Options pricing Portfolio analysis. Call-center analysis Simulation of large-scale systems such as Internet backbone Wireless networks
Systems and System Environment A system is defined as a group of objects that are joined together in some regular interaction or interdependence toward accomplishment of some purpose. A system is often affected by changes occurring outside the system.
Components of a system Entity is an object of interest in the system Attribute is a property of an entity Activity represents a time period of specified length State is collection of variables necessary to describe the system at any time Event is instantaneous occurrence that might change the state of the system.
Examples of Systems and Components System Entities Attributes Activities Events State variables Banking Customers Checking-account balance Production Machines Speed, capacity; breakdown rate Making deposits Welding; stamping Arrival; departure Breakdowns Number of busy tellers; number of customers waiting Status of machines (busy, idle or down) Inventory Warehouse Capacity Withdrawing Demand Levels of inventory, backlogged demands
Number of customers waiting in line or being served Discrete and continuous systems A discrete system is one in which state variable(s) change only at discrete set of points in time 3 2 1 t Time
Head of water behind dam Discrete and continuous systems A continuous system is one in which state variable(s) change continuously over time. 3 2 1 t Time
Model of a system A model is defined as a representation of a system for the purpose of the studying the system.
Types of Models Mathematical Symbolic notations Mathematical equations Physical Simulation models Static/dynamic Deterministic/stochastic Discrete/continuous
Discrete Event System Simulation Discrete-event system simulation is the modeling of systems in which the state variable changes only at a discrete set of points in time.
Steps in simulation 1. Problem Formulation 2. Setting of objectives and overall project plan 3. Model Conceptualization 4. Data Collection 5. Model translation No No 6.Verified? yes 7.Validated? yes 8. Experimental design yes 9. Production runs and analysis 10.More runs? No 11 Documentation and reporting yes 12 Implementation