4. A consultant has been working on measuring performances of the private hospitals locating in Sinop. Because of the privacy concerns, the names of the hospitals are not shared. The hospitals (H1, H2, and H3) will be evaluated systematically by means of DEA based on AHP method. In the proposed analysis, initially traditional AHP analysis is decided to be applied in order to calculate the weights of the hospitals. Then, the weights derived from AHP method will be directly used in the DEA analysis. Below, pairwise comparison matrices collected from decision maker, are demonstrated. Based on these, calculate the global weights of the hospitals and provide the ranking of the alternatives. (Note: The consistencies of the pairwise comparison matrices have already controlled.) Pairwise Comparison Matrix of Criteria Pairwise Comparison Matrix of hospitals with regard to (wrt) Criterion 2 Criteria Criterion 1 (C1) Criterion 2 (C2) Criterion 3 (C3) CI C2 C3 Alternatives HI Н2 H3 3 3 H1 7 1 Н2 5 1 H3 1 Pairwise Comparison Matrix of hospitals wrt Criterion 1 Pairwise Comparison Matrix of hospitals wrt Criterion 3 Alternatives HI Н2 H3 Alternatives H1 Н2 H3 HI 1 2. H1 1 1/5 Н2 1/3 1 Н2 H3 3. 1 H3 7

4. A consultant has been working on measuring performances of the private hospitals locating in Sinop. Because of the privacy concerns, the names of the hospitals are not shared. The hospitals (H1, H2, and H3) will be evaluated systematically by means of DEA based on AHP method. In the proposed analysis, initially traditional AHP analysis is decided to be applied in order to calculate the weights of the hospitals. Then, the weights derived from AHP method will be directly used in the DEA analysis. Below, pairwise comparison matrices collected from decision maker, are demonstrated. Based on these, calculate the global weights of the hospitals and provide the ranking of the alternatives. (Note: The consistencies of the pairwise comparison matrices have already controlled.) Pairwise Comparison Matrix of Criteria Pairwise Comparison Matrix of hospitals with regard to (wrt) Criterion 2 Criteria Criterion 1 (C1) Criterion 2 (C2) Criterion 3 (C3) CI C2 C3 Alternatives HI Н2 H3 3 3 H1 7 1 Н2 5 1 H3 1 Pairwise Comparison Matrix of hospitals wrt Criterion 1 Pairwise Comparison Matrix of hospitals wrt Criterion 3 Alternatives HI Н2 H3 Alternatives H1 Н2 H3 HI 1 2. H1 1 1/5 Н2 1/3 1 Н2 H3 3. 1 H3 7

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

See similar textbooks

Similar questions

The classified department of a monthly magazine has used a combination of quantitative and qualitative methods to forecast sales of advertising space. Results over a 20-month period are as follows: Month Error Month Error 1 —8 11 1 2 —2 12 6 3 4 13 4 7 14 4 5 9 15 1 6 5 16 —2 7 0 17 —4 8 —3 18 —8 9 -9 19 —5 10 —4 20 —1 a. Compute a tracking signal for months 11 through 20. Compute
Chart and Regression analysis : What does the intercept predict? X: C16 (number of cars on the sales lot) versus Y: C17 (cars sold per day) Equation: y=2.9x + 14.5 Slope:2.9 Intercept:14.5 Does the intercept mean the intercept is 14.5 means that the cars sold per day( Y) predicted number of cars on sale lot(X) to be 14.5, but this intercept has no meaning. So, I will not use to predict cars sold per day?
Café Michigan's manager, Gary Stark, suspects that demand for mocha latte coffees depends on the price being charged. Based on historical observations, Gary has gathered the following data, which show the numbers of these coffees sold over six different price values: Price Number Sold $2.70 760 $3.40 515 $2.10 990 $4.10 250 $3.20 325 $4.10 475 Using simple linear regression LOADING... and given that the price per cup is $1.80, the forecasted demand for mocha latte coffees will be nothing cups (enter your response rounded to one decimal place).
Ayamas Food Corporation Sdn. Bhd. company needs to estimate its sales for next year. The most recent six years of revenue data for the company's most popular line of Ayamas are given below: Year Sales revenue (Millions of dollars) 1 10 2 13 3 15 4 16 5 18 6 20 a) Use simple linear regression to forecast the sales revenue for the next THREE (3) years. (14 Marks) b) Determine the correlation coefficient for the data and interpret its meaning. (4 Marks) c) Determine the coefficient of determination for the data and interpret its meaning. (2 Marks)
2. A major airline uses an airport in a large city as a hub for its connecting flights. Each day the airline faces a number of claims from its passengers who lost their baggage. The number of lost baggage claims in the last 14 days have been 67, 25, 57, 58, 35, 29, 77, 44, 38, 36, 23, 69, 53, and 45 a. Use moving average method with N = 3 to forecast the expected number of claims for tomorrow. b. Use exponential smoothing method with parameter α = 0.7 to forecast the number of lost baggage claims that the airline should expect in the next 2 days. c. If the criterion for accuracy is the mean squared error (MSE), which of the forecasting methods in parts (a) and (b) are preferred? d. If you become in charge of providing forecasts for the number of claims in the next 2 days using exponential smoothing method, what would be your point and 90-percent prediction interval forecasts for the number of claims in each of the next 2 days? Use MSE as your evaluation criterion.
4 Trinity General Hospital had the following number of patient admissions during the past 8 weeks Patient Week Admissions 1 120 145 3 95 4 112 5 130 6. 110 7 100 140 Develop a 3-weck weighted average forecast for Week 4 through 9 with weights a W1 = 0.2 W2 = 0.3 W3 = 0.5 Forecast patient admissions for week 9 using simple | exponential smoothing with a = 0.2. Assume that the forecast for Week 2 (F2) is the naïve forecast.
15. The Vision Hospital is considering the purchase of a new Advance Scan machine. The decision will rest partly on the anticipate patients in the coming months. The numbers of patents during the past years are as follows: Year Number of patents(Actual) 1600 2016 2017 2200 2018 2600 2019 2800 | 2020 3100 Compute the forecast for the year 2021 using exponential smoothing with a forecast for the year 2016 of 1500 and a = .3
Which methods to use depends on the type of variable(s) being analyzed.
Home-Style Cookies tracks monthly sales by type of cookie, see the following table. Month Chocolate Chip (000 units) Peanut Butter (000 units) Filled (000 units) Feb. 19 15 10 Mar. 18 14 9 Apr. 15 11 8 May 20 16 11 Jun. 18 14 10 Jul. 22 18 12 Aug. 20 16 10 Sep. Create a line chart in Excel showing monthly sales by type of cookies (each type will have its own line); then, forecast September sales volume for chocolate chip cookies using each of the following: the naïve approach; a 5-month moving average; a weighted average using .60 for August, .30 for July, and .10 for June; and a linear trend equation.
ic 2-Time Series Analysis and F eBook Problem 6-21 (Algorithmic) The Centers for Disease Control and Prevention Office on Smoking and Health (OSH) is the lead federal agency responsible for comprehensive tobacco prevention and control. OSH was established in 1965 to reduce the death and disease caused by tobacco use and exposure to secondhand smoke. One of the many responsibilities of the OSH is to collect data on tobacco use. The following data show the percentage of U.S. adults who were users of tobacco for a recent 11-year period (http://www.cdc.gov/tobacco/data_statistics/tables/trends/cig_smoking/index.htm). Year 1 2 3 4 5 6 7 8 9 10 11 Percentage of Adults Who Smoke 22.9 25 Peremt of Adults Who St 20 15 21.5 20 8 20.8 a. Choose the correct time series plot. (0) 10 20.1 20.1 18.0 18.9 19.4 19,4 20.3 20.3 20.3 19.4 18.8 4 Year (0) $ 10 12 (1) Percent of Adults Who Smoke 25 20 15 10 Year (0) S 10 12 4
Solve the following:1. Sales of Volkwagen’s popular Beetle have grown steadily at autodealership in Nevada during the past 5 years (see table below). The sales manager had predicted before the new model was introduced that first-year sales would be 410 VWs. Using exponential smoothing with a weight of α= .30, develop forecast for years 2 through 6.Year Sales1 450 4102 4953 5184 5635 5846?2. The following data come from regression line projections:ForecastPeriod 1234Forecast Values 410419428435Actual values 406423423440Compute the MAD and MSE.
The number of heart surgeries performed at HeartvilleGeneral Hospital has increased steadily over the past sev-eral years. The hospital’s administration is seeking the bestmethod to forecast the demand for such surgeries in year 6.The data for the past 5 years are shown. The hospital’s administration is considering the followingforecasting methods. Begin error measurement in year 3 soall methods are compared for the same years.i. Exponential smoothing, with a = 0.6. Let the initial fore-cast for year 1 be 45, the same as the actual demand.ii. Exponential smoothing, with a = 0.9. Let the initial fore-cast for year 1 be 45, the same as the actual demand.iii. Trend projection with regression.iv. Two-year moving average.v. Two-year weighted moving average, using weights 0.6 and0.4, with more recent data given more weight.vi. If MAD is the performance criterion chosen by the admin-istration, which forecasting method should it choose?vii. If MSE is the performance criterion chosen by the…