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Homework 13-1 with Correct Answers Georgia Institute Of Technology ISYE 6501

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Question 18.1 Describe analytics models and data that could be used to make good recommendations to the power company. Here are some questions to consider: • The bottom-line question is which shutoffs should be done each month, given the capacity constraints. One consideration is that some o...

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  • April 11, 2023
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  • 2022/2023
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Question 18.1

Describe analytics models and data that could be used to make good recommendations to the power
company.

Here are some questions to consider:

• The bottom-line question is which shutoffs should be done each month, given the capacity
constraints. One consideration is that some of the capacity – the workers’ time – is taken
up by travel, so maybe the shutoffs can be scheduled in a way that increases the number of
them that can be done.
• Not every shutoff is equal. Some shutoffs shouldn’t be done at all, because if the power is
left on, those people are likely to pay the bill eventually. How can you identify which
shutoffs should or shouldn’t be done? And among the ones to shut off, how should they be
prioritized?

Think about the problem and your approach. Then talk about it with other learners, and share and
combine your ideas. And then, put your approaches up on the discussion forum, and give feedback
and suggestions to each other.

You can use the {given, use, to} format to guide the discussions: Given {data}, use {model} to
{result}.




Case Study
Here are my steps to solve the power company’s problem -

1. Identify customers will not pay the bills.
2. Estimate the amount of power a potential shutoff customer will use in the next 3 months.
3. Prioritize shutoffs given the capacity constraints.
4. Find out the quickest route to shut power off.

Step 1: Identify customers will not pay the bills.

given:

• number of months delinquent
• have there been past delinquencies (binary variable)
• outstanding balance on the account
• type of service address (e.g. residential, business)
• credit score
• payment history (lateness)


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