1. | ![]() |
Newsvendor Problem 1 | Design, analyze, and manage a manufacturing or service system with uncertainty. Solve a single period decision problem containing uncertainty or randomness. | ![]() |
2. | ![]() |
Newsvendor Problem 2 | Design, analyze, and manage a manufacturing or service system with uncertainty. Solve a single period decision problem containing uncertainty or randomness. | ![]() |
3. | ![]() |
Newsvendor Problem 3 | Design, analyze, and manage a manufacturing or service system with uncertainty. Solve a single period decision problem containing uncertainty or randomness. | ![]() |
4. | ![]() |
Discrete Time Markov Chain 1 | How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods? | ![]() |
![]() |
Discrete Time Markov Chain 1 | How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods? | ![]() |
|
![]() |
Discrete Time Markov Chain 1 | How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods? | ![]() |
|
5. | ![]() |
Discrete Time Markov Chain 2 | How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods? | ![]() |
![]() |
Discrete Time Markov Chain 2 | How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods? | ![]() |
|
6. | ![]() |
Discrete Time Markov Chain 2 | How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods? | ![]() |
![]() |
Discrete Time Markov Chain 2 | How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods? | ![]() |
|
![]() |
Discrete Time Markov Chain 2 | How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods? | ![]() |
|
7. | ![]() |
Discrete Time Markov Chain 3 | How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods? | ![]() |
8. | ![]() |
Poisson Process 1 | The way we modeled was to denote the inter-arrival times between customers by a random variable and assumed that the random variable has a exponential distribution. | ![]() |
![]() |
Poisson Process 1 | The way we modeled was to denote the inter-arrival times between customers by a random variable and assumed that the random variable has a exponential distribution. | ![]() |
|
9. | ![]() |
Poisson Process 2 | a special case: the case where the inter-arrival times follow iid exponential distribution. We will learn how it is different from other distributions. | ![]() |
![]() |
Poisson Process 2 | a special case: the case where the inter-arrival times follow iid exponential distribution. We will learn how it is different from other distributions. | ![]() |
|
10. | ![]() |
Continuous Time Markov Chain 1 | The time period is discretized so that time is denoted by integers. Consider discrete‐time stochastic process having discrete state space | ![]() |
![]() |
Continuous Time Markov Chain 1 | The time period is discretized so that time is denoted by integers. Consider discrete‐time stochastic process having discrete state space | ![]() |
|
![]() |
Continuous Time Markov Chain 1 | The time period is discretized so that time is denoted by integers. Consider discrete‐time stochastic process having discrete state space | ![]() |
|
![]() |
Continuous Time Markov Chain 1 | The time period is discretized so that time is denoted by integers. Consider discrete‐time stochastic process having discrete state space | ![]() |
|
11. | ![]() |
Continuous Time Markov Chain 2 | A stochastic process is a continuous time Markov chain with state space | ![]() |
![]() |
Continuous Time Markov Chain 2 | A stochastic process is a continuous time Markov chain with state space | ![]() |
|
12. | ![]() |
Queueing basics 1 | Queueing theory deals with a set of systems having waiting space. Analyzing a simple queue, a set of queues connected with each other will be covered as well in the end. | ![]() |
13. | ![]() |
Queueing basics 2 | Design the system. How can we determine the number of server and customer, size of waiting capacity? | ![]() |
![]() |
Queueing basics 2 | Design the system. How can we determine the number of server and customer, size of waiting capacity? | ![]() |
|
![]() |
Queueing basics 2 | Design the system. How can we determine the number of server and customer, size of waiting capacity? | ![]() |
|
14. | ![]() |
Queueing basics 3 | How does the system perform? For example that Utilization of servers, Average waiting time in queue, Average staying time in the system | ![]() |
15. | ![]() |
Queueing basics 4 | Case study | ![]() |
![]() |
Queueing basics 4 | Case study | ![]() |