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Linear programming

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An office manager needs to buy new filing cabinets. Cabinet A costs \$7, and takes up 6 square feet of floor space, and holds 9 cubic feet of files. Cabinet B costs \$8, takes up 8 square feet, and holds 13 cubic feet. He has only \$66 to spend and the office has room for no more than 60 square feet of cabinets. (Find the Objective Function and the Constraints; Let Cabinet A be "x", and Cabinet B be "y") How many of each can be bought to maximize storage capacity? What is the maximum storage capacity?

Solution Summary

This shows how to use linear programming to solve a maximization problem regarding file cabinets in an office.

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An office manager needs to buy new filing cabinets. Cabinet A costs \$7, and takes up 6 square feet of floor space, and holds 9 cubic feet of files. Cabinet B costs \$8, takes up 8 square feet, and holds 13 cubic feet. He has only \$66 to spend and ...

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• BSc , Wuhan Univ. China
• MA, Shandong Univ.
Recent Feedback
• "Your solution, looks excellent. I recognize things from previous chapters. I have seen the standard deviation formula you used to get 5.154. I do understand the Central Limit Theorem needs the sample size (n) to be greater than 30, we have 100. I do understand the sample mean(s) of the population will follow a normal distribution, and that CLT states the sample mean of population is the population (mean), we have 143.74. But when and WHY do we use the standard deviation formula where you got 5.154. WHEN & Why use standard deviation of the sample mean. I don't understand, why don't we simply use the "100" I understand that standard deviation is the square root of variance. I do understand that the variance is the square of the differences of each sample data value minus the mean. But somehow, why not use 100, why use standard deviation of sample mean? Please help explain."
• "excellent work"
• "Thank you so much for all of your help!!! I will be posting another assignment. Please let me know (once posted), if the credits I'm offering is enough or you ! Thanks again!"
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• "Thank you very much for your valuable time and assistance!"

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