I am learning data science and have a question about our project.

A company has three assembly lines, which can be used to make products for customers based on their order. Depending upon the order size (count), one or more assembly lines are allocated, though this is based on best guess now. This makes that only one order (may have multiple products) can be manufactured at a time. Now, the company wants to use optimization to minimize the number of assembly lines to be allocated per order so multiple orders can be manufactured concurrently. Many of the orders can be recurring and may have ot be manufactured once a week or once a month or whatever.

I have order data by number of assembly lines used and the frequency of order. Obviously, an order has to be completed before a new one in the recurring cycle can start. I have 12 rows of data if frequency is monthly nad 52 rows for weekly.

How would I optimize the manufacturing cycle for the customer to balance the number of assembly lines used and change frequency if necessary without increasing my cost? I do not have cost data, but increasing frequency or increasing assembly lines would increase cost.


Tags: linear, optimization, regression

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