Task 1:

Formulate a linear programming (LP) model that may be solved to identify the optimal production and procurement plan for the company in each time period.

Task 2:

Use the data from the "production.csv" file to estimate the average machining time, assembly time, finishing time, and cost per unit for each product type as estimates of the parameters t_i^m, t_i^a, t_i^f, and C_i^P of the LP model.

Task 3:

Use the data from the "demand.csv" file to predict the demands D_i in time period 53 for each product. Discuss the prediction method that you chose and justify your choice.

Task 4:

Solve the LP formulated in Task 1 using the procurement cost specified above and parameters estimated in Tasks 2 and3to determine the optimal plan for period 53.

Task 5.

Perform sensitivity analysis by changing one parameter at a time (leaving all other parameters fixed at the values used in Task 4) and answer the following questions.

(a) By how much does the total production cost change as the demand for each product type changes by 1 unit?

(b) At most how much should the company be willing to pay to

(i) Increase the availability of machining time by one hour during regular run?

(ii) Increase the availability of finishing time by one hour during regular run?

(iii) Increase the availability of assembly time by one hour during regular run?

Task 6:

Use the data from "quality.csv" to train and test a Classification Tree that predicts theQualityof a batch based on values of the featuresTest1, Test2, Test3, and Test4.

Attachment:- Attachments.rar