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1.
Design, analyze, and manage a manufacturing or service system with uncertainty. Solve a single period decision problem containing uncertainty or randomness.
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2.
Design, analyze, and manage a manufacturing or service system with uncertainty. Solve a single period decision problem containing uncertainty or randomness.
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3.
Design, analyze, and manage a manufacturing or service system with uncertainty. Solve a single period decision problem containing uncertainty or randomness.
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4.
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?
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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?
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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?
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5.
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?
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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?
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6.
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?
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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?
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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?
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7.
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?
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8.
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.
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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.
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9.
a special case: the case where the inter-arrival times follow iid exponential distribution. We will learn how it is different from other distributions.
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a special case: the case where the inter-arrival times follow iid exponential distribution. We will learn how it is different from other distributions.
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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
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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
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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
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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
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11.
Continuous Time Markov Chain 2
A stochastic process is a continuous time Markov chain with state space
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Continuous Time Markov Chain 2
A stochastic process is a continuous time Markov chain with state space
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12.
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.
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13.
Design the system. How can we determine the number of server and customer, size of waiting capacity?
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Design the system. How can we determine the number of server and customer, size of waiting capacity?
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Design the system. How can we determine the number of server and customer, size of waiting capacity?
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14.
How does the system perform? For example that Utilization of servers, Average waiting time in queue, Average staying time in the system
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15.
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