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HOMEWORK ASSIGNMENT 4
Chapter 15 – Problem 9
Sycamore Plastics (SP) is a manufacturer of polyethylene plastic pellets used as a raw material by manufacturers of plastic goods around the U.S. SP currently operate four manufacturing centers in Philadelphia, PA; Atlanta, GA; St. Louis, MO; and Salt Lake City, UT. The plants have different capacities and production costs as indicated in the table below:
Maximum Capacity Production Costs
PLANT (x 100,000 lbs.) (per 1,000 lbs)
Philadelphia 7.5 $325.00
Atlanta 9.0 $275.00
St. Louis 12.0 $305.00
Salt Lake City 10.3 $250.00
SP currently has six contract customers located in New York City, Birmingham, AL; Terre Haute, IN; Dallas, TX; Spokane, WA and San Diego, CA. Transportation costs between the plants and various customers, as well as contracted demand from each customer, are shown in the table below:
Transportation Costs per 1,000 lbs_______
From/To NYC Birmingham Terre Haute Dallas Spokane San Diego
Philadelphia $45 $52 $56 $62 $78 $85
Atlanta $55 $42 $58 $59 $80 $82
St. Louis $57 $60 $50 $54 $65 $70
Salt Lake City $72 $71 $67 $57 $52 $60
Total Demand 525 415 925 600 325 400
- Create a solver model and find the optimal solution to help SP develop a distribution plan that will minimize the costs to supply the customer’s demand
- Comment briefly on your solution. Beyond the obvious, does your proposed solution have any other implications for SP?
Chapter 18 – Problem 7
The following table contains the demand from the last 10 months:
|MONTH||ACTUAL DEMAND||MONTH||ACTUAL DEMAND|
- Calculate the single exponential smoothing forecast for these data using an α of .30 and an initial forecast (F1) of 31.
- Calculate the exponential smoothing with trend forecast for these data using an α of .30, a δ of .30, and an initial trend forecast of (T1) of 1, and an initial exponentially smoothed forecast of (F1) of 30.
- Calculate the mean absolute deviation (MAD) for each forecast. Which is best?
Chapter 18 – Problem 15
Historical demand for a product is:
- Using a weighted moving average with weights of 0.60, 0.30, and 0.10, find the July forecast. Remember to use the largest weight for the most recent month.
- Using the simple three-month moving average, find the July forecast.
- Using single exponential smoothing with an α of .20 and a June forecast of 13, find the July forecast. Make whatever assumptions you wish.
- Using simple linear regression analysis, calculate the regression equation for the preceding demand data.
- Using the regression equation in d, calculate the forecast for July.