Business Analytics & Modeling
Q1 LP
| MGMT 5312 Fall 2023- Final Exam |
| This is an open book exam but you are NOT allowed to consult with anyone. Violation of code of Academic Integrity will lead to severe penalty. |
| You have 2 hrs. and 30 minutes to complete and upload the exam |
| Q1. (33 points) |
| Southern Oil company produces two grades of gasolene: regular and premium. |
| The profit contributions are $ 0.3 per gallon for regular gasolene & $ 0.50 per gallon for premium gasolene. |
| Each gallon of regular gasoline contains 0.3 gallons of grade A crude oil and each gallon of premium gasolene contans 0.6 gallons of grade A crude oil. |
| For the next production period, Southern has 18,000 gallons of grade A crude oil available. |
| The refiner used to produce the gasolenes has a production capacity of 50,000 gallons for the next production period. |
| Southern Oil's distributors have indicated that the demand for premium gasoline for the next production period will be at most 20,000 gallons. |
| Set up the problem as an LP problem and determine the optimal solution using solver. |
Q2 Forecasting
| Year | Quarter | Revenue | Q2. (33 points) | |
| 1 | 1 | 20 | The data shows the Sales Revenue for an organization for 5 years for each quarter. | |
| 2 | 100 | a. Use mutiple regression model with dummy varaibles as below to develop an equation to account for seasonal effects in the data. | ||
| 3 | 175 | Qtr1 = 1 if Qtr1, 0 otherwise; Qtr2 = 1 if Qtr2, 0 otherwise; Qtr3 = 1 if Qtr3, 0 otherwise; | ||
| 4 | 13 | b. Based on (a), compute estimates for quarterly sales for year 6. | ||
| 2 | 1 | 37 | c. Let Period= 1 refer to observation in quarter 1 of year 1, Period= 2 refer to observation in quarter 2 of year 1, ...Period= 20 refer to observation in quarter 4 of year 5. | |
| 2 | 136 | Based on the dummy variables defined in (a) and the variable Period, develop an equation to account for any seaasonal effects and linear trend in the time series. | ||
| 3 | 245 | d. Based on the seasonal effects and the linear trend, compute estimates for quarterly sales for year 6. | ||
| 4 | 26 | e. Is the model in (a) or that in (c), more effective? Give reason. | ||
| 3 | 1 | 75 | ||
| 2 | 155 | |||
| 3 | 326 | |||
| 4 | 48 | |||
| 4 | 1 | 92 | ||
| 2 | 202 | |||
| 3 | 384 | |||
| 4 | 82 | |||
| 5 | 1 | 176 | ||
| 2 | 282 | |||
| 3 | 445 | |||
| 4 | 181 |
Q3 Simulation
| Q3. (34 points) | ||||||||||||
| Comet Dry Cleaners specializes in same-day dry cleaning. Presently, an average of 20 garments are held over for the next day because of capacity constraints. | ||||||||||||
| The outlet manager is contemplating expanding to elimate the backlog. | ||||||||||||
| A probability distribution of garments received and the maximum number of garments that can be cleaned after expansion is given below. | ||||||||||||
| Run a simulation of 50 days to determine the daily backlog (garments received less garments cleaned) | ||||||||||||
| Determine the average daily backlog, if any, after expansion. | ||||||||||||
| If the cost associated with garments being held over is $ 25 per garment, and the added cost of expansion is $200 per day, is the saving enough to justify expansion? | ||||||||||||
| Note: A suggested template is provided for use. | ||||||||||||
| Probability | Beginning Range of Prob. | Garments Received per day | Trial | Random Number | Garments received per day | Trial | Random Number | Garments cleaned per day | Backlog | |||
| 0.1 | 50 | 1 | 1 | |||||||||
| 0.25 | 60 | 2 | 2 | |||||||||
| 0.3 | 70 | 3 | 3 | |||||||||
| 0.25 | 80 | 4 | 4 | |||||||||
| 0.1 | 90 | 5 | 5 | |||||||||
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| Probability | Beginning Range of Prob. | Garments Cleaned per day | 10 | 10 | ||||||||
| 0.3 | 50 | 11 | 11 | |||||||||
| 0.4 | 60 | 12 | 12 | |||||||||
| 0.3 | 70 | 13 | 13 | |||||||||
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