IE 301 • Tüm Sınavlar • Operations Research II
Her ne kadar Operations Research I dersinin devamı gibi dursa da aslında hiç ortak yönü olmayan bu derste yeni kaslar geliştiriyor, olasılıkla modellemeyi öğreniyoruz.
Tabii ki bu derste de adres Unicourse!
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Eğitmen
Ömer Faruk Altun
Co-founder & Head of Education
2011 yılında Endüstri Mühendisliği okumak için başladığım Sabancı Üniversitesi'nden 2018 yılında Bilgisayar Mühendisi olarak mezun oldum. 13 yıldır Altun ismiyle başta Sabancı Üniversitesi olmak üzere çeşitli okullarda Endüstri ve Bilgisayar Mühendisliği alanlarında ders vermekteyim. Unicourse'ta sunduğum derslerin yanında eğitim departmanının da sorumluluğunu üstlenmekteyim.
Geçme Garantisi
Derslerimize güveniyoruz. Olur da sınavlarına bizimle hazırlandığın halde dersten kalırsan, iade alabilirsin. Koşullar
Konular
Probability Review
4 konu anlatımı
Random Variables and Probability Distributions
Conditional Probability
Total Probability Rule
Expected Value
Markov Chains
8 konu anlatımı
What is a Markov Chain?
One-Step Transition Probabilities
Example
n-Step Transition Probabilities
Chapman-Kolmogorov Equations
Example
Unconditional State Probabilities
Example
Steady State Probabilities and Applications
12 konu anlatımı
Steady State Distribution
Example
Classes and State Properties
Periodicity and Ergodic Markov Chains
Example
Example
Example
First Passage Times
Example
Expected First Passage Time
Expected Recurrence Time
Example
Absorbing Markov Chains
9 konu anlatımı
Absorbing Markov Chains
Matrix Structure
Inverse of a Matrix
Expected Number of State Visits
Example
Expected Time Until Absoption
Example
Absorption Probabilities
Example
Exponential Distribution and Poisson Processes
13 konu anlatımı
Counting Processes
Assumptions of Poisson Processes
Example 1
Memoryless Property
Example 2
Minimum of Exponential Random Variables
Example 3
Thinning
Example 4
Superposition
Example 5
Non-homogenous Poisson Processes
Example 6
Sample Midterm Problems
23 soru
Markov Chains 1
Markov Chains 2
Markov Chains 3
Markov Chains 4
Markov Chains 5
Markov Chains 6
Steady State Probabilities and Applications 1
Steady State Probabilities and Applications 2
Steady State Probabilities and Applications 3
Steady State Probabilities and Applications 4
Steady State Probabilities and Applications 5
Steady State Probabilities and Applications 6
Steady State Probabilities and Applications 7
Steady State Probabilities and Applications 8
Steady State Probabilities and Applications 9
Steady State Probabilities and Applications 10
Steady State Probabilities and Applications 11
Absorbing Markov Chains 1
Absorbing Markov Chains 2
Exponential Distribution and Poisson Process 1
Exponential Distribution and Poisson Process 2
Exponential Distribution and Poisson Process 3
Exponential Distribution and Poisson Process 5
Continuous Time Markov Chains
9 konu anlatımı
From Discrete to Continuous
Example 1
Example 2
Example 3
Steady State Distribution
Q Matrix
Example 4
Example 5
Example 6
Birth-and-Death Models
11 konu anlatımı
A subproblem of CTMC
Random Rates, Finite Chain
Example 1
Random Rates, Infinite Chain
Example 2
Equal Rates, Infinite Chain, Single Server
Example 3
Equal Rates, Finite Chain, Single Server
Example 4
Equal Rates, Finite Chain, Multiple Servers
Example 5
Queueing Theory
10 konu anlatımı
Kendall's Notation
Terminology
M/M/1 Queue
Example 1
M/M/s Queue
Example 2
M/M/1/K Queue
Example 3
M/M/s/K Queue
Example 4
Markov Decision Processes
6 konu anlatımı
Introduction
Policy Improvement Algorithm: Step 1
Policy Improvement Algorithm: Step 2
Example 1
LP Solution
LP Solution with Discount
Sample Final Problems
18 soru
Continuous Time Markov Chains 1
Continuous Time Markov Chains 2
Continuous Time Markov Chains 3
Continuous Time Markov Chains 4
Continuous Time Markov Chains 5
Birth and Death 1
Birth and Death 2
Birth and Death 3
Birth and Death 4
Queueing Theory 1
Queueing Theory 2
Queueing Theory 3
Queueing Theory 4
Queueing Theory 5
Queueing Theory 6
Queueing Theory 7
Queueing Theory 8
Markov Decision Process 1