Round Tree Manor is a hotel that provides two types of rooms with three rental classes: Super Saver, Deluxe, and Business. The profit per night for each type of room and rental class is as follows: Rental Class Super Saver Deluxe Business Type I $30 $35 -- Room Type II $20 $30 $40 Type I rooms do not have Internet access and are not available for the Business rental class. Round Tree’s management makes a forecast of the demand by rental class for each night in the future. A linear programming model developed to maximize profit is used to determine how many reservations to accept for each rental class. The demand forecast for a particular night is 130 rentals in the Super Saver class, 60 rentals in the Deluxe class, and 50 rentals in the Business class. Round Tree has 100 Type I rooms and 120 Type II rooms.

Use linear programming to determine how many reservations to accept in each rental class and how the reservations should be allocated to room types. Summarize the model in algebraic form by defining the decision variables, the objective function and all the constraints.

Round Tree Manor is a hotel that provides two types of rooms with three rental classes: Super Saver, Deluxe, and Business. The profit per night for each type of room and rental class is as follows: Rental Class Super Saver Deluxe Business Type I $30 $35 -- Room Type II $20 $30 $40 Type I rooms do not have Internet access and are not available for the Business rental class. Round Tree’s management makes a forecast of the demand by rental class for each night in the future. A linear programming model developed to maximize profit is used to determine how many reservations to accept for each rental class. The demand forecast for a particular night is 130 rentals in the Super Saver class, 60 rentals in the Deluxe class, and 50 rentals in the Business class. Round Tree has 100 Type I rooms and 120 Type II rooms. Use linear programming to determine how many reservations to accept in each rental class and how the reservations should be allocated to room types. Summarize the model in algebraic form by defining the decision variables, the objective function and all the constraints. PLEASE DO NOT USE EXCEL TO SOLVE.

Round Tree Manor is a hotel that provides two types of rooms with three rental classes: Super Saver, Deluxe, and Business. The profit per night for each type of room and rental class is as follows:

Rental Class

Room Super Saver Deluxe    Business

Type I    $32 $43    —

Type II $17 $35 $39

Type I rooms do not have wireless Internet access and are not available for the Business rental class.

Round Tree's management makes a forecast of the demand by rental class for each night in the future. A linear programming model developed to maximize profit is used to determine how many reservations to accept for each rental class. The demand forecast for a particular night is 120 rentals in the Super Saver class, 70 rentals in the Deluxe class, and 55 rentals in the Business class. Round Tree has 105 Type I rooms and 120 Type II rooms.

Use linear programming to determine how many reservations to accept in each rental class and how the reservations should be allocated to room types.

Variable# of reservations

Supersaver rentals allocated to room type I ?

Supersaver rentals allocated to room type II ?

Deluxe rentals allocated to room type I ?

Deluxe rentals allocated to room type II ?

Business rentals allocated to room type II ?

Is the demand by any rental class not satisfied?
Explain.
How many reservations can be accommodated in each rental class?

Rental Class# of reservations

Supersaver ?

Deluxe ?

Business ?

Management is considering offering a free breakfast to anyone upgrading from a Super Saver reservation to Deluxe class. If the cost of the breakfast to Round Tree is $5, should this incentive be offered?

With a little work, an unused office area could be converted to a rental room. If the conversion cost is the same for both types of rooms, would you recommend converting the office to a Type I or a Type II room?

Why?

Could the linear programming model be modified to plan for the allocation of rental demand for the next night?

What information would be needed and how would the model change?

Successful hotel managers must have personality characteristics often thought of as feminine (such as "compassionate") as well as those often thought of as masculine (such as "forceful"). The Bem Sex-Role Inventory (BSRI) is a personality test that gives separate ratings for female and male stereotypes, both on a scale of 1 to 7. A sample of 148 male general managers of three-star and four-star hotels had mean BSRI femininity score x = 5.29. The mean score for the general male population is μ = 5.19. Before you trust your results, you would like more information about the data. What facts would you most like to know? (Select all that apply.)

1- Whether the chosen general managers are an SRS of the population.

2- Whether the margin of error takes into account the nonresponse rate.

3- What is the significance level?

4- Whether there are outliers in the sample.

Round Tree Manor is a hotel that provides two types of rooms with three rental classes: Super Saver, Deluxe, and Business. The profit per night for each type of room and rental class is as follows:

Round Tree's management makes a forecast of the demand by rental class for each night in the future. A linear programming model developed to maximize profit is used to determine how many reservations to accept for each rental class. The demand forecast for a particular night is 150 rentals in the Super Saver class, 55 in the Deluxe class, and 40 in the Business class. Since these are the forecasted demands, Round Tree will take no more than these amounts of each reservation for each rental class. Round Tree has a limited number of each type of room. There are 100 Type I rooms and 110 Type II rooms.

Group Exercise #7

The tourist industry is subject to enormous seasonal variation.   A hotel in Bermuda has recorded its occupancy rate for each quarter over a 5-year period.    These data are shown in the following table:

1. Calculate the seasonal indices for each quarter in order to measure seasonal variation.     Also, include the Trend Line Equation. (Hint:   Because the regression line yt = β0 + β1t    represents trend, it follows that the time series divided by the predicted values produces ytyt = S_t X R_tà seasonal & random variation.   And, because there is no cyclical effect, use this method to compute seasonal indices.)

2. What can you infer from the Seasonal Indices?

3. Deseasonalize the occupancy rates and assess.   (Hint:   Graphically compare the original and adjusted data sets.   Accordingly, provide data-supported inference/s.)

4. Forecast each quarter’s occupancy rate for 2000.

5. As an alternative to calculating and using seasonal indices to measure seasonal variations, indicator variables can be used in a multiple regression model.   Accordingly, use indicator variables and regression analysis to forecast hotel occupancy in 2000.   How does this compare to the forecast produced by using seasonal indices?  

A). Suppose Travel and Leisure reported the average hotel price in Miami, Florida, was $153.57 per night in 2019. Assume the population standard deviation is $26.86 and that a random sample of 30 hotels was selected. Calculate the standard error of the mean.

B). According to the US Labor Department, the average hourly wage for private-sector production and non-supervisory workers was $20.04 in February 2013. Assume the standard deviation for this population is $6.00 per hour. A random sample of 35 workers from this group was selected. What is the standard error of the mean?

C). According to the US Labor Department, the average hourly wage for private-sector production and non-supervisory workers was $20.04 in February 2013. Assume the standard deviation for this population is $6.00 per hour. A random sample of 35 workers from this group was selected. What is the probability that the mean for this sample is less than $19.00?

D). According to the US Labor Department, the average hourly wage for private-sector production and non-supervisory workers was $20.04 in February 2013. Assume the standard deviation for this population is $6.00 per hour. A random sample of 35 workers from this group was selected. What is the probability that the mean for this sample is more than $20.84??

How would we interpret the probability calculated in the questions D?

E). According to the US Labor Department, the average hourly wage for private-sector production and non-supervisory workers was $20.04 in February 2013. Assume the standard deviation for this population is $6.00 per hour. A random sample of 35 workers from this group was selected. What is the probability that the mean for this sample is exactly $20.00?

1- Assume that visitors of a hotel on average pay $20 for minibar per night per room, with a standard deviation of $3. Assume further that minibar expenses are normally distributed.
a- What percentage of rooms are expected to pay more than $25 per night, i.e. P(x > 25)
b- What percentage of rooms are expected to pay more than $40 per night, i.e. P( x > 40)?
c- What percentage of rooms are expected to pay less than $12 per night, i.e. P( x < 12)?
d- What percentage of rooms are expected to pay between $18 and $24, i.e. P(18 < x < 24)?
e- What percentage of rooms are expected to pay between $16 and $19, i.e. P (16 < x < 19)?

Forecasting labour costs is a key aspect of hotel revenue management that enables hoteliers to appropriately allocate hotel resources and fix pricing strategies. Mary, the President of Hellenic Hoteliers Federation (HHF) is interested in investigating how labour costs (variable L_COST) relate to the number of rooms in a hotel (variable Total_Rooms). Suppose that HHF has hired you as a business analyst to develop a linear model to predict hotel labour costs based on the total number of rooms per hotel using the data provided. 3.1 Use the least squares method to estimate the regression coefficients b0 and b1 3.2 State the regression equation 3.3 Plot on the same graph, the scatter diagram and the regression line3.4 Give the interpretation of the regression coefficients b0 and b1 as well as the result of the t-test on the individual variables (assume a significance level of 5%) Determine the correlation coefficient of the two variables and provide an interpretation of its meaning in the context of this problem 3.6 Check statistically, at the 0.05 level of significance whether there is any evidence of a linear relationship between labour cost and total number of rooms per hotel

Once upon a time a new hotel manager, whose staff was responsible for selling banquets and hotel packages, was highly motivated to take advantage of a year-end bonus program for managers. In order to win the bonus, he needed to bring in new business so he decided to initiate a contest for his sales agents. He announced that he would pay $100 to the agent who had brought in the most new clients by the end of the month. He then sat back in his chair to await the results and decide how he would spend his bonus money. While visions of bonuses danced through his head, his sales agents were busily belly-aching for the following reasons:

(1) They were used to working as a team and resented being encouraged to compete against each other.
(2) In the manager's last contest, a new sales agent had reportedly cheated and "stole" new clients from the old-timers.
(3) The winner of the last contest was paid the prize money several months late, only after she had "shaken" it out of the sales manager.
(4) One sales agent's position had been cut, so the agents felt they were already operating beyond full capacity and working extra hours.
(5) The sales manager had not endeared himself to the agents, and they felt he was just using them to get his bonus.
(6) The sales agents felt as if they were being manipulated and perceivd the $100 bonus as an insult.

Not surprisingly, then, the sales agents decided to ignore the contest. The sales manger was angry when he saw the low level of new business at the end of the month and concluded that the agents were lazy. He told them they were unprofessional and complained about them at staff meetings so that soon everyone in the organization had heard about their "laziness." Old-timers who knew better scratched their heads because they remembered how hard the sales agents used to work before the new manager was hired. Within a few months, some of the agents quit and went to work for a competitor.

Questions:

(1) Should this manager go back to school and learn about the theories of motivation? What mistakes did he make?

(2) Which motivation theories apply to this case? Explain your answer. Does Expectancy Theory apply, and if so, how (explain)? What about Reinforcement Theory or Self-Determination Theory? Be sure to explain your answers.

(3) What do you think the sales manager should have done to try to motivate his sales agents? Relate your motivational strategies to the theories that we have discussed in class.