Chap014
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Chap014

Course Number: MGT 3121, Spring 2013

College/University: CUNY Baruch

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Chapter 11: Managing Capacity and Demand Chapter 11 Managing Capacity and Demand TEACHING NOTE Many of the approaches to matching capacity and demand discussed in the chapter should be familiar to students and prompt lively discussion. The controversial practice of "overbooking" is treated in economic terms, but the behavioral implications of this policy for both service personnel and consumers should...

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11: Chapter Managing Capacity and Demand Chapter 11 Managing Capacity and Demand TEACHING NOTE Many of the approaches to matching capacity and demand discussed in the chapter should be familiar to students and prompt lively discussion. The controversial practice of "overbooking" is treated in economic terms, but the behavioral implications of this policy for both service personnel and consumers should be discussed. This chapter presents a number of techniques to assist management in matching supply and demand, but leaves to the imaginative student the opportunity for other creative approaches. The chapter concludes with a discussion of a technique that was pioneered by American Airlines called yield management. Yield management is a computerbased approach for maximizing revenues. Instructions for playing the yield management game are given in the textbook and students should be encouraged to familiarize themselves with the material before game day. The tally sheets and other instructional materials are presented in this manual. SUPPLEMENTARY MATERIALS Case: American Airlines, Inc. Revenue Management (HBS 9-190-029) Following deregulation, American Airlines embarked on an ambitious program of revenue management by building on its SABRE reservation system. The case presents the reader with pricing decisions on two routes and describes the development of yield management. Case: University Health Services: Walk-In Clinic (HBS 9-681-061) In response to complaints of excessive waiting, a triage system was introduced and one year later the director reviews the results to decide whether or not the patient waiting time is acceptable. LECTURE OUTLINE I. Generic Strategies of Level Capacity and Chase Demand (Table 11.1) II. Strategies for Managing Demand (Figure 11.1) Customer-induced Variability (Table 11.2) Segmenting Demand (Figure 11.2) Offering Price Incentives (Tables 11.4 and 11.5) Promoting Off-peak Demand Developing Complementary Services Reservation Systems and Overbooking (Tables 11.9 and 11.7) 11-1 III. Strategies for Managing Capacity Defining Service Capacity Daily Workshift Scheduling (Figures 11.3, 11.4, 11.5) Weekly Workshift Scheduling with Days-off Constraint (Table 11.8) Increasing Consumer Participation Creating Adjustable Capacity Sharing Capacity Cross-training Employees Using Part-time Employees Scheduling Part-time Employees at a Drive-in-Bank (Figures 11.6 and 11.7) IV. Yield Management (Figures 11.8, 11.9, 11.10, 11.11, 11.12) Yield Management Applications TOPICS FOR DISCUSSION 1. What organizational problems can arise from the use of part-time employees ? The use of part-time employees can be very helpful to businesses that have peak demand periods such as restaurants, supermarkets, and banks. Part-time employees are usually paid lower wages and they enjoy fewer, if any, of the company benefits that are provided for full-time employees. Also, it is not generally feasible for a company to offer career development incentives to part-time employees and it is also more difficult to fit them into the organizational structure. In addition, part-time employees generally have lower experience levels than do full-time workers. As a result, part-time employees may have poorer attitudes and less loyalty and commitment, which could affect reliability, performance, and the quality of work. This situation can have a direct impact on customers and the business. In view of these conditions, part-time employees may require greater supervision and control than would be necessary for full-time employees. Also, there is usually a greater turnover in part-time employees, so more time must be spent in training new employees. Finally, the business must hire more part-time employees than full-time people to staff positions, which creates more administrative work for work scheduling, personnel records, and payroll. 2. How can computer-based reservation systems increase service capacity utilization ? The main function of the reservation system is to pre-sell the service. A reservation system allows the customer to reserve a service long before it is actually utilized. Allowing the customer to make reservations has numerous advantages. The reservation system can be used to deflect demand to other times or locations where service capacity is available. For example, if a passenger wants a flight that is full, a reservation clerk can suggest immediately alternative flights that are available. Thus, the demand for service capacity has been effectively rerouted to underutilized capacity. Reservation systems also allow the service to overbook its capacity when it reasonably expects to have no-shows. 11-2 Finally, when computer-based systems are standardized among different service organizations in the same industry, each participant may make use of a larger information base. For example, a passenger can call a Delta Air Lines reservation agent and book a flight on United Airlines if a Delta flight is unavailable. Such a system benefits all the participants and the customers. 3. Illustrate how a particular service has implemented successfully strategies for managing both demand and capacity. Because a service is consumed and produced simultaneously, a failure to provide enough capacity to serve results in idle servers and facilities. Public schools have been experiencing this variability in demand recently owing to fluctuations in the numbers of school age children, a move of families from central cities to the suburbs, and increasing enrollments in private schools. The agrarian pattern of closing school during summer months has contributed to idle servers and facilities during that period. As a public service, education has been somewhat slow at adapting to its fluctuating demand, but the recent trend of more competition for scarce resources in public services and the public outcry for educational accountability has spurred some strategies for managing the demand and supply. Some examples follow: Managing demand: Expand educational services by offering adult education, early childhood education programs, and before and after school childcare programs. Offer social services such as community education programs, job retraining programs, programs for senior citizens, and summer recreation programs. Offer incentives to attend particular schools by making them magnet schools, open-area schools, or openenrollment schools, which any child in the community may attend. Promote off-peak demand in some areas by offering summer school classes. Partition demand by using educational voucher systems. Managing capacity: Use school buildings where classes are no longer held because of declining enrollment for warehousing school supplies or for administrative offices or lease those to other organizations until school demographics change. Arrange flexible schedules on a daily basis and a school-year basis for crowded schools. For example, on a daily basis in an elementary school, schedule hours for kindergarten through third grade students from 8 a.m. to 2 p.m. and hours for fourth through sixth grade students from 10 a.m. to 4 p.m. Another alternative would be to stagger the students throughout the calendar year instead of offering classes only during the typical ninemonth school year, i.e., some students would attend school December to September, others would go from June to March, and still others would attend the typical September to June period. Merge two or more grades in under-utilized schools or, in a crowded grade school, shift sixth graders to a junior high/middle school. Hire teachers who are trained in more than one subject in order to cope with a fluctuating demand for courses. Hire part-time teachers in small schools for support areas such as art, music and physical education. Share capacity with other public service agencies to utilize space better. 4. What possible dangers are associated with developing complementary services ? 11-3 One of the possible dangers associated with developing complementary services is the possibility of increasing the firm's liability because of those added services. For instance, adding cold and hot sandwiches at convenience grocery stores incur the possibility that a customer might suffer food poisoning and take legal action against the 11-4 store. Another example is seen with the addition of self-service gas pumps, which involve a risk of fire, damage and injury. Another problem associated with offering complementary services may occur when the complementary service attracts customers who may hurt business. Consider a shopping mall that installs a video arcade. The arcade may become a haven for noisy and rowdy teenagers who will drive away those customers who want to shop in peace. Finally, a complementary service should enhance the firm's image; as noted above, a poorly selected complementary service could serious compromise business. 5. Will the widespread use of yield management eventually erode the concept of fixed prices for any service? Most services exhibit, to some extent, the capacity-constrained dilemma faced by airlines and hotels that are unable to inventory their products (seats on a flight or rooms for a night). To avoid losing the revenue from this perishable capacity, capacity- constrained services are motivated to pre-sell the inventory when possible by using reservations and giving discounts to avoid lost sales. For example, travelers have found that the published room rates (rack rates) for underutilized hotels are quickly abandoned if the guest requests a discount (e.g., government or AARP). The exceptions to businesses that practice this strategy are the budget motels that fill all their rooms each night. Yield management has alerted customers to the perishable nature of capacity-constrained services and this knowledge will destroy the myth of fixed prices for many such services and lead to price negotiation for all services. 6. Go to http://en.wikipedia.org/wiki/Yield_management/ and discuss the ethical issues associated with yield management. Yield management is a form of price discrimination, and thus could be seen as unfair. For example, why should two customers receive the identical service (coach seat on an airline flight) and pay different prices. However, demand-responsive pricing in which last minute purchases are more expensive than purchases far in advance is considered by most customers as reasonable. INTERACTIVE CLASS EXERCISE Watch the PowerPoint presentation concerning the overbooking experience at the Doubletree Hotel in Houston, Texas. How could this situation been handled differently? Clearly, the night desk clerk was not properly prepared to handle such a late arrival for a reserved room. The desk clerk should have apologized for the problem and immediately secured alternative accommodations and transportation, if necessary. We note that the guests contributed to the situation by not notifying the hotel of their late arrival. Figure 6.13 the service recovery framework could be displayed and used as a focus of the discussion. 11-5 EXERCISES 11.1 (a) Day Mon. Tue. Wed. Th. Fri. Capacity 75 75 75 75 75 Walk-ins 50 30 40 35 40 Appointments 25 45 35 40 35 (b) Appointments should not be scheduled at their maximum level because the walk-ins are only expected values. There will be some variation both in time of arrival and in the actual demand. We learned from queuing theory that capacity to serve must always exceed demand. Thus, some slack time should be scheduled into the system in order to avoid excessive patient waiting. (c) In order to avoid physician waiting, a few appointments should be made at the very beginning of the day. The majority of appointments should be made in the late afternoon to avoid excessive waits for walk-in patients. 11.2 Now C u = $100 and C o remains $100 P(d < x) Cu 100 .5 C u + C o 100 + 100 From Table 13.4 in the textbook, a strategy of overbooking three rooms guarantees that 48 percent of the no-shows will be covered. This represents a change from the previous policy of overbooking two rooms. 11.3 No Shows 0 1 2 3 4 Frequency 6 5 4 3 2 P(d < x) Probability .30 .25 .20 .15 .10 0 .30 .55 .75 .90 If overbook by 1, then P (d < x ) must be at least .30 and less than .55. Cu 20 P(d < x ) = = 0.30 Cu + Co 20 + Co Solving for Co : Co = [ 20 0.30(20)] = $46.67 (maximum overbooking opportunity loss) 0.30 11-6 11.4 Daily Demand 10 11 12 13 14 15 16 17 18 19 20 P(d < x ) Frequency 1 1 2 2 2 3 3 2 2 1 1 Probability .05 .05 .10 .10 .10 .15 .15 .10 .10 .05 .05 P(d < x) 0 .05 .10 .20 .30 .40 .55 .70 .80 .90 .95 Cu ( 30 10) 20 = = = .67 Cu + Co (30 10) + 10 30 Crazy Joe should lease 16 canoes. 11.5 Using a payoff matrix approach: Passengers Overbooked No Shows P(NS) 0 .30 1 .25 2 .20 3 .15 4 .10 Expected Profit: 0 180* 100 20 60 140 $ 60 1 130 180 100 20 60 $101 2 80 130 180 100 20 $109.50 3 30 80 130 180 100 $ 92 4 20 30 80 130 180 $ 55 * Cell value = 6 $80 $300 = $180 Thus, overbook 2 passengers (i.e., take 8 reservations) and expect a profit of $109.50 per flight. Using the critical fractile model: P(d < x) Cu 80 . 61 Cu + Co 80 + 50 Where: Cu = Cost of underestimating no-shows (i.e., there are more no-shows than expected and the airline must fly empty seats) 11-7 Co = Cost of overestimating no-shows (i.e., there are fewer no-shows than expected and the passengers who cannot be accommodated get free lift tickets) 11.6 Emergency Room Nurse Staffing Objective Function: Minimize x1 + x2 + x3 + x4 + x5 + x6 + x7 Constraints: Sun. Mon. Tue. Wed. Th. Fri. Sat. x1 0 0 1 1 1 1 1 x2 1 0 0 1 1 1 1 x3 1 1 0 0 1 1 1 x4 1 1 1 0 0 1 1 x5 1 1 1 1 0 0 1 x6 1 1 1 1 1 0 0 x7 0 1 1 1 1 1 0 RHS 3 6 5 6 6 6 5 For: i = 1 to 7, xi 0 and integer Where: xi = Number of nurses assigned to a shift that has two consecutive days off beginning with day i (i = 1 for Sunday, 2 for Monday, etc.) Solution Summary for Emergency Room Objective Variable Solution Coefficient Variable Solution x1 1 1 x5 2 x2 1 1 x6 0 x3 1 1 x7 3 x4 0 1 Minimized OBJ = 8 Iteration = 7 Elapsed CPU seconds = .109375 11-8 Objective Coefficient 1 1 1 Weekly Staff Schedule for Emergency Room x2 x3 x4 x5 x6 x7 Req. Staff 1 1 0 2 0 3 4 1 0 2 0 3 6 6 0 2 0 3 5 6 1 2 0 3 6 7 1 1 0 3 6 6 1 1 0 3 6 6 1 1 0 2 5 5 x1 Day Sun. Mon. Tue. Wed. Th. Fri. Sat. 1 1 1 1 1 Excess 1 0 1 1 0 0 0 11.7 Sheriff Patrol Problem: Objective Function: Minimize x1 + x2 + x3 + x4 + x5 + x6 + x7 Constraints: Sun. Mon. Tue. Wed. Th. Fri. Sat. x1 0 0 1 1 1 1 1 x2 1 0 0 1 1 1 1 x3 1 1 0 0 1 1 1 x4 1 1 1 0 0 1 1 x5 1 1 1 1 0 0 1 x6 1 1 1 1 1 0 0 x7 0 1 1 1 1 1 0 RHS 6 4 4 4 5 5 6 For: i = 1 to 7, xi 0 and integer Where: xi = Number of deputies assigned to a shift that has two consecutive days off beginning with day i (i = 1 for Sunday, 2 for Monday, etc.) 11-9 Summarized Report for Sheriff Patrol Opportunity Objective Number Variable Solution Cost Coefficient 1 x1 +1.0000000 0 +1.0000000 2 x2 +2.0000000 0 +1.0000000 3 x3 +1.0000000 0 +1.0000000 4 x4 +2.0000000 0 +1.0000000 5 x5 0 0 +1.0000000 6 x6 +1.0000000 0 +1.0000000 7 x7 0 0 +1.0000000 Minimized OBJ = 8 Iteration = 7 Elapsed CPU seconds = .109375 Weekly Staff Schedule for Summer Months x1 x2 x3 x4 x5 x6 Day Sun. 2 1 2 0 1 Mon. 1 2 0 1 Tue. 1 2 0 1 Wed. 1 2 0 1 Th. 1 2 1 1 Fri. 1 2 1 2 Sat. 1 2 1 2 0 x7 0 0 0 0 0 Req. 6 4 4 4 5 5 6 Minimum Obj. Coeff. 0 + .66666663 + 1.0000000 + .66666663 + 1.0000000 0 + 1.0000000 Staff 6 4 4 4 5 6 6 Maximum Obj. Coeff. +1.5000000 +1.0000000 +1.5000000 +1.0000000 +1.5000000 +1.0000000 + Infinity Excess 0 0 0 0 0 1 0 11.8 F = $79 D = $59 p = 0.8 = 75 = 15 P(d < x ) ( F D) (79 59) 0.316 p( F ) 0.8(79) Full-fare seats reserved = +z = 75 + (-.48)(15) = 67 11.9 (a) Model Car Compact Midsize Rental Rate (F) $30 $45 Discount Rate (D) $20 $30 % Discount Seekers (p) 80 60 Daily Demand () 50 30 11-10 Standard Deviation () 15 10 Fleet Size 60 30 P ( d compact xcompact ) P ( d midsize xmidsize ) 30 20 = . 417 xcompact = 50 (. 21)(15) 46 30(.8) 45 30 = .555 xmidsize = 30 + (.14) (10) 31 45(. 6) (b) Because the current fleet of midsize cars is smaller than the optimal number of midsize cars that should be reserved for full-price paying customers, an expansion of the midsize fleet should be considered. CASE: RIVER CITY NATIONAL BANK Assignment As Ms. Chen's top aide, you are assigned the task of providing an analysis of the situation and recommending a solution. This is your opportunity to serve your company and community as well as to make yourself "look good" and earn points toward your raise and promotion. Gary Miller is faced with the problem of fluctuating demand at the recently constructed drive-in facility. The customer complaints of excessive waiting occur for the most part on Fridays. Little congestion is experienced during the remainder of the week. This represents the classical service problem of managing capacity. The problem can be addressed from two perspectives: managing both demand and supply. Altering demand: The bank customers could be encouraged to use the facility on off-peak days, thus avoiding delays in service. The customers can be informed of the peak times with notices in their monthly statements or signs outside the remote facility. A further inducement to try the facility on an off-peak day could be accomplished by giving those customers some small gift such as a pocket calendar or pen. A more ambitious incentive would be an offer of free or reduced monthly service charges for customers who bank regularly on off-peak days. Cashing payroll checks represents a major transaction on Fridays. Customers should be informed of the convenient direct deposit option where employers can deposit paychecks directly into employee bank accounts. The bank may wish to approach large employers in the community to promote the direct deposit idea. Finally, customers may need encouragement to be better prepared to conduct their transactions as soon as they reach the tellers' windows. A preparation stand containing deposit slips and pens could be placed just before the entry to the drive-in station so customers could do their paperwork while waiting for the customer currently being served. Controlling capacity: With the use of part-time tellers and proper shift scheduling, the bank could better match service capacity with the fluctuations in daily demand. Because there are no problems on Saturdays, we will only consider weekdays. Our analysis begins with an assessment of teller availability. Tellers available for the remote drive-in consists of 2 full-time tellers who work the morning shift only (7 a.m. to 2 p.m.), Monday through Friday and 4 part-time tellers who work various combinations of morning shifts (10 a.m. to 2 p.m.) or afternoon shifts (2 p.m. to 7 p.m.). Each part-time teller works 20 hours per week, excluding Saturdays. This yields a total of 80 part-time teller hours per week to supplement the 70 full-time hours for the two full-time tellers. The problem involves allocating these 150 teller hours to meet the daily demand levels for the week. Table 1 is developed from the data provided in Table 11.8 of the case in the textbook. This table summarizes the daily transaction demand by calculating the mean and standard deviation for each day by morning and afternoon shift. The last column of the table gives the 95 percent demand level assuming normal distribution. This demand level divided by teller capacity represents the percent of utilization. From a queuing standpoint, this percent 11-11 utilization must be less than 100 percent and probably not more than 80 percent to avoid excessive customerwaiting times. Table 1 Statistical Analysis of Transactions by Day of Week and Shift Mean Standard 95 Percent Number Deviation Demand Level Monday: Morning 168.75 14.20 192.18 Afternoon 133.75 26.42 177.34 Tuesday: Morning 131.75 20.73 165.95 Afternoon 97.00 27.13 141.76 Wednesday: Morning 147.80 43.87 220.19 Afternoon 138.80 47.84 217.74 Thursday: Morning 140.80 25.74 183.27 Afternoon 125.40 30.07 175.02 Friday: Morning 227.80 20.63 261.84 Afternoon 232.00 39.11 296.53 In order to calculate teller capacity it is assumed that each teller could service two lanes and that all eight lanes could be used when needed. The number of transactions per lane that are possible is based on a typical transaction, which takes approximately five minutes per customer when we account for all the delays such as moving the car into position, filling out forms, counting money and pulling away. Thus, each open lane can accommodate 12 transactions per hour. For the morning shift (7 a.m. 2 p.m.), one teller can accommodate 168 transactions, a teller working during the midday period (l0 a.m. 2 p.m.) can process 96 transactions and one afternoon (2 p.m. 7 p.m.) teller can handle 120 transactions. Because of the prevailing policy, the two full-time tellers are scheduled to cover the morning shift each day. The remaining 80 hours of part-time teller capacity is allocated to the midday and afternoon to create a schedule that has a reasonably consistent percent utilization across the weekdays. Table 2 shows the proposed teller schedule. Note that 81 part-time teller hours have been scheduled. In Table 3 the percent utilization for-each period is calculated. It is interesting to note that now customers may perceive Monday and Thursday afternoons as being busier than Friday. 11-12 Table 2 Monday: Teller Schedule Morning Afternoon two full-time 7-2 two part-time 2-7 one part-time 10-2 Tuesday: two full-time 7-2 two part-time 2-7 Wednesday: two full-time 7-2 one part-time 10-2 three part-time 2-7 Thursday: two full-time 7-2 two part-time 2-7 Friday: two full-time 7-2 two part-time 10-2 four part-time 2-7 Total teller hours: 70 full-time and 81 part-time per week Monday: Table 3 Transaction Capabilities of Tellers as Scheduled in Table 2 Lanes Total Percent 1 2 Open Capacity Utilization3 Morning 5.14* 432 44 Afternoon 4.00 240 74 Tuesday: Morning Afternoon 4.00 4.00 336 240 49 59 Wednesday: Morning Afternoon 5.14* 6.00 432 360 51 60 Thursday: Morning Afternoon 4.00 4.00 336 240 54 73 528 480 50 62 Friday: Morning 6.29* Afternoon 8.00 1 Lanes Open assumes each teller serves two lanes. 2 Total Capacity is calculated as follows: (lanes open) (hours/shift) (12 transactions/hour/lane) 3 Percent Utilization is calculated as follows: 95 percent demand level/total capacity *Calculated using a weighted average, because the two full-time tellers work from 7 to 2 and the parttime teller(s) work(s) from 10 to 2. 11-13 CASE: GATEWAY INTERNATIONAL AIRPORT Questions 1. Assume that you are the assistant to the manager for operations at the FAA. Use the techniques of workshift scheduling to develop an analysis of the total workforce requirements and days-off schedule . The most restrictive problem assumes the following constraints: Each controller must be allowed two consecutive days off. All controllers must work five 8-hour days. All controllers on a shift begin work at the same time (i.e., no starting times may overlap. The shifts run from 00:01 to 08:00, 08:01 to 16:00, and 16:01 to 24:00. The ratio of take-offs and landings to the number of controllers on duty cannot exceed 16. Shown below is the number of operations and controller requirements for each hour of each day. Controller Requirements Hour 0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24 S 6.4 4. 3.2 1.6 2.4 7.2 9.6 16. 20. 26.4 32.8 35.2 27.2 30.4 32. 32.8 33.6 40.8 44. 50.4 28. 19.2 13.6 12. Hourly Demand Adjusted for Daily Variation M T W T F 8.64 8.32 8. 8.32 8.96 5.4 5.2 5. 5.2 5.6 4.32 4.16 4. 4.16 4.48 2.16 2.08 2. 2.08 2.24 3.24 3.12 3. 3.12 3.36 9.72 9.36 9. 9.36 10.08 12.96 12.48 12. 12.48 13.44 21.6 20.8 20. 20.8 22.4 27. 26. 25. 26. 28. 35.64 34.32 33. 34.32 36.96 44.28 42.64 41. 42.64 45.92 47.52 45.76 44. 45.76 49.28 36.72 35.36 34. 35.36 38.08 41.04 39.52 38. 39.52 42.56 43.2 41.6 40. 41.6 44.8 44.28 42.64 41. 42.64 45.92 45.36 43.68 42. 43.68 47.04 55.08 53.04 51. 53.04 57.12 59.4 57.2 55. 57.2 61.6 68.04 65.52 63. 65.52 70.56 37.8 36.4 35. 36.4 39.2 25.92 24.96 24. 24.96 26.88 18.36 17.68 17. 17.68 19.04 16.2 15.6 15. 15.6 16.8 Controllers Required (Integer Value of Demand S M T W T F 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 2 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 3 4 4 4 4 4 4 5 5 4 5 5 2 3 3 3 3 3 2 2 2 2 2 2 1 2 2 2 2 2 1 2 1 1 1 2 S 7.36 4.6 3.68 1.84 2.76 8.28 11.04 18.4 23. 30.36 37.72 40.48 31.28 34.96 36.8 37.72 38.64 46.92 50.6 57.96 32.2 22.08 15.64 13.8 16) S 1 1 1 1 1 1 1 2 2 2 3 3 2 3 3 3 3 3 4 4 3 2 1 1 The minimum number of controllers needed for the basic problem is shown below: Shift 00-08 (night) 08-16 (day) 16-24 (evening) S 1 3 4 M 2 3 5 T 2 3 5 W 2 3 4 T 2 3 5 F 2 4 5 S 2 3 4 The size of the workforce that is needed to meet the minimum requirements considering the constraints of this problem is determined using the following integer linear programming (ILP) model: 11-14 Let x j = Number of ATCs assigned to work schedule j Minimize: x1 + x2 + x3 + x4 + x5 + x6 + x7 Shift 1 8 a.m. - 4 p.m. Subject to: Sun. Mon. Tue. Wed. Th. Fri. Sat. x 2 + x 3 + x 4 + x5 + x 6 x 3 + x 4 + x5 + x 6 + x 7 x1 + x 4 + x5 + x 6 + x7 x1 + x2 + x5 + x 6 + x 7 x1 + x2 + x3 + x6 + x7 x1 + x2 + x3 + x4 + x7 x1 + x2 + x3 + x4 + x5 3 3 3 3 3 4 3 Right Hand Sides Shift 2 Shift 3 4 p.m. - midnight midnight - 8 a.m. 4 5 5 4 5 5 4 Summarized Results for GIA (8am4pm) Variables Obj. Fnctn. Variables Obj. Fnctn. No. Names Solution Coefficient No. Names Solution Coefficient 1 x1 +1.0000000 +1.0000000 5 x5 0 +1.0000000 2 x2 +1.0000000 +1.0000000 6 x6 +1.0000000 +1.0000000 3 x3 0 +1.0000000 7 x7 0 +1.0000000 4 x4 +2.0000000 +1.0000000 Minimized OBJ = 5 Iteration = 11 Elapsed CPU seconds = 10.38281 Summarized Results for GIA (4pmMN) Variables Obj. Fnctn. Variables Obj. Fnctn. No. Names Solution Coefficient No. Names Solution Coefficient 1 x1 +2.0000000 +1.0000000 5 x5 0 +1.0000000 2 x2 0 +1.0000000 6 x6 +2.0000000 +1.0000000 3 x3 +1.0000000 +1.0000000 7 x7 0 +1.0000000 4 x4 +2.0000000 +1.0000000 Minimized OBJ = 7 Iteration = 9 Elapsed CPU seconds = 8.953125 Summarized Results for GIA (MN8am) Variables Obj. Fnctn. Variables Obj. Fnctn. No. Names Solution Coefficient No. Names Solution Coefficient 1 x1 +1.0000000 +1.0000000 5 x5 0 +1.0000000 2 x2 0 +1.0000000 6 x6 +1.0000000 +1.0000000 3 x3 0 +1.0000000 7 x7 0 +1.0000000 4 x4 +1.0000000 +1.0000000 Minimized OBJ = 3 Iteration = 3 Elapsed CPU seconds = 3.234375 11-15 1 2 2 2 2 2 2 Work Schedules (W = workday, X = day off) 8 a.m.-4 p.m. Shift: S M T W T F S Jean X X W W W W W Laura W X X W W W W Patty W W W X X W W David M. W W W X X W W Chon W W W W W X X Staffing Requirements Excess 4 3 1 3 3 0 4 3 1 3 3 0 3 3 0 4 4 0 4 3 1 4 p.m.-midnight Shift: Mario Yao (Joe) Shan Ling Kathy Jeff Scott John X X W W W W W X X W W W W W W W X W W W W W W X X X W W W W W X X W W W W W W W X X W W W W W X X Staffing Requirements Excess 5 4 1 5 5 0 6 5 1 4 4 0 5 5 0 5 5 0 5 4 1 Midnight-8 a.m. Shift: Lynda Claudia Ron X W W X W W W W W W X W W X W W W X W W X Staffing Requirements Excess 2 1 1 2 2 0 3 2 1 2 2 0 2 2 0 2 2 0 2 2 0 Total Controllers Required: 15 If each shift is started one hour earlier, the workload will be smoothed enough to eliminate one controller's position. The new shifts begin at 7 a.m. (day), 3 p.m. (evening), and 11 p.m. (night) and yield the following ILP model: Let x j = Number of ATCs assigned to work schedule j Minimize: x1 + x2 + x3 + x4 + x5 + x6 + x7 11-16 Shift 1 7 a.m. - 3 p.m. Subject to: Sun. Mon. Tue. Wed. Th. Fri. Sat. x + 2 x 3 + x 4 + x5 + x 6 x 3 + x 4 + x5 + x 6 + x 7 x1 + x 4 + x5 + x 6 + x7 x1 + x2 + x5 + x 6 + x 7 x1 + x2 + x3 + x6 + x7 x1 + x2 + x3 + x4 + x7 x1 + x2 + x3 + x4 + x5 3 3 3 3 3 4 3 Right Hand Sides Shift 2 Shift 3 3 p.m. - 11 p.m. 11 p.m. - 7 a.m. 4 5 5 4 5 5 4 Summarized Results for GIA (7am3pm) Variables Obj. Fnctn. Variables Obj. Fnctn. No. Names Solution Coefficient No. Names Solution Coefficient 1 x1 +1.0000000 +1.0000000 5 x5 0 +1.0000000 2 x2 +1.0000000 +1.0000000 6 x6 +1.0000000 +1.0000000 3 x3 0 +1.0000000 7 x7 0 +1.0000000 4 x4 +2.0000000 +1.0000000 Minimized OBJ = 5 Iteration = 11 Elapsed CPU seconds = 10.32813 Summarized Results for GIA (3pm11pm) Variables Obj. Fnctn. Variables Obj. Fnctn. No. Names Solution Coefficient No. Names Solution Coefficient 1 x1 +2.0000000 +1.0000000 5 x5 0 +1.0000000 2 x2 0 +1.0000000 6 x6 +2.0000000 +1.0000000 3 x3 +1.0000000 +1.0000000 7 x7 0 +1.0000000 4 x4 +2.0000000 +1.0000000 Minimized OBJ = 7 Iteration = 9 Elapsed CPU seconds = 9.007813 Summarized Results for GIA (11pm7am) Variables Obj. Fnctn. Variables Obj. Fnctn. No. Names Solution Coefficient No. Names Solution Coefficient 1 x1 +1.0000000 +1.0000000 5 x5 0 +1.0000000 2 x2 0 +1.0000000 6 x6 +1.0000000 +1.0000000 3 x3 0 +1.0000000 7 x7 0 +1.0000000 4 x4 +1.0000000 +1.0000000 Minimized OBJ = 2 Iteration = 1 Elapsed CPU seconds = 1.320313 11-17 1 2 1 1 1 2 1 Work Schedules (W = workday, X = day off) 7 a.m.-3 p.m. Shift: Jean Laura Patty David M. Chon S X W W W W M X X W W W T W X W W W W W W X X W T W W X X W F W W W W X S W W W W X Staffing Requirements Excess 4 3 1 3 3 0 4 3 1 3 3 0 3 3 0 4 4 0 4 3 1 3 p.m.-11 p.m. Shift: Mario Yao (Joe) Shan Ling Kathy Jeff Scott John X X W W W W W X X W W W W W W W X W W W W W W X X X W W W W W X X W W W W W W W X X W W W W W X X Staffing Requirements Excess 5 4 1 5 5 0 6 5 1 4 4 0 5 5 0 5 5 0 5 4 1 11 p.m.-7 a.m. Shift: Lynda Ron W X W W W W X W X W W W W X Staffing Requirements Excess 1 1 0 2 2 0 2 1 1 1 1 0 1 1 0 2 2 0 1 1 0 Total Controllers Required: 14 2. On the basis of your primary analysis, discuss the potential implications for workforce requirements and daysoff scheduling if assumptions (a) and (b) above are relaxed so that analysis can be based on the hourly demand without the constraints of a preset number of shifts and no overlapping of shifts. In other words, discuss the effects of analyzing hourly demand requirements on the basis of each ATC position essentially having its own shift, which can overlap with any other ATC shift to meet that demand . Relaxing the shift time constraint: As shown above, beginning each shift one hour earlier smoothed the workload enough to eliminate one ATC position. Flexibility in shift times allows a better match between service requirements and employee scheduling. The most efficient shift times will minimize the number of controllers needed, subject to the operations/controllers ratio. 11-18 Relaxing the shift demand with no overlap of shifts constraint : Relaxing the shift profile of demand requirements and allowing shifts to overlap permit better utilization of the workforce, because the service requirements and workforce capacity can be matched better. The operations/controller ratio could be met with a smaller workforce. However, a tradeoff exists between efficient workforce utilization and scheduling difficulties. In this case, it will become easier to accommodate the general requirements of the problem, but the effort involved in scheduling the workshifts will increase. Relaxing other constraints: Relaxing the days-off constraint would equalize the workload and facilitate the scheduling task. Using part-time employees or a split-shift schedule can reduce workforce requirements and allow the work schedule to better match demand. However, implementing these two strategies might be inadvisable because they might have an adverse effect on employee morale. 3. Do you feel that this would result in a larger or smaller degree of difficulty in meeting the four general constraints? Why? Relaxing the shift time constraint seems an attractive option. It improves the match between service requirements and employee schedules and reduces the number of employees by one. Except for the sensitive issue of eliminating jobs, this option does not have major drawbacks. As noted in question 2, relaxing the shift demand and no-shift overlap constraint offers some benefits but the implementation of this policy requires considerable expertise on the part of the scheduler. Using part-time employees and split-shift scheduling, on the other hand, could make scheduling easier, but such a method might have adverse effects on employee morale. Moreover, these alternatives might also have implications for maintaining a high level of ATC skills. 4. What additional suggestions could you make to the manager of operations to minimize the workforce requirements level and days-off scheduling difficulty ? Allow workers to choose whether they want consecutive days or non-consecutive days off. Allow overlapping shift times. This will equalize workloads of all of the controllers, allow smooth shift changes, and create a better match between workload capacity and requirements. Decrease the length of workshifts or allow controllers to work split-shifts. For example, if a controller were to work from 5 to 8 p.m. on Wednesday, the airport would save more than one-half of a day's ATC pay and still meet FAA requirements. Consider allowing controllers to work more than five days per week (within the bounds of safety) or fewer than five days per week (within the bounds of maintaining skills). Attempt to alter demand. GIA could offer airlines incentives, such as lower take-off/landing fees during offpeak times, to alter timing of their operations. 11-19 CASE: THE YIELD MANAGEMENT ANALYST [This material is reprinted with permission of Kevin Baker and Robert Freund.] YIELD MANAGEMENT GAME TALLY SHEET Booking ID A (example) # Passengers 100 # Passengers Carried Given in Phase III Revenue Phase III Passengers Gross Revenue SUBTOTALS Oversale Adjustment (if over 100) Spoilage Adjustment (if under 100) NET REVENUE (Gross Revenue Adjustment) Airplane Capacity: 100 seats Historical Market Information: Average no-show, misconnect, and cancellation rate: 20 percent Average revenue per passenger: $400 Spoilage Penalty: Oversale Penalty: 1-5 seats 6-9 10-15 16+ $200 per empty seat $200 per passenger $500 per passenger $800 per passenger $1,000 per passenger Example Oversale Penalty (108 passengers): (5)($200)+(3)($500)=$2500 11-20 After reading the game instructions presented in the textbook and explaining the tally sheet, students should be ready to begin the game. The game begins with revealing each of the demand opportunities in Phase I in turn, one at a time, beginning with opportunity (A) and proceeding to the next opportunity on the list only after everyone has made his and her decision. This demand revelation process is accomplished using the PowerPoint presentation available on the CD-ROM. Students should make their decisions in ink, because once a passenger group has been accepted, it is final. Also, students cannot return to an earlier passenger group that has been previously rejected. Thus, as each group is revealed, the students must make a non-reversible YES or NO decision to accept or reject that opportunity and be ready to move on without knowledge of the future opportunities. Students record the group ID and number of passengers in the first two columns of the tally sheet, keeping in mind a running total of seats sold in order to hold some in reserve for Phase II, but also realizing that not all passengers will show up. Phase I: Passenger Demand Received Outside of 13 days Prior to Departure (A) (B) (C) (D) (E) (F) (G) (H) (I) (J) (K) (L) (M) (N) (O) (P) 100 60 25 5 45 35 70 20 40 80 25 15 10 40 45 20 Sales Representatives - $275 per passenger Professors - $200 per passenger MBAs - $150 per passenger Person Family - $300 per passenger Conventioneers - $180 per passenger Conventioneers - $300 per passenger Sportswriters - $350 per passenger Person High School Basketball Team - $300 per passenger High School Band Members - $275 per passenger Government Officials - $390 per passenger Marketing Managers - $450 per passenger Golfers - $325 per passenger Nurses for Convention - $275 per passenger Republican Congressmen - $450 per passenger Club Med Vacationers - $375 per passenger Shouldice Hernia Patients - $300 per passenger Phase II: Passenger Demand Between 13 days Prior to Departure and Day of Departure (1) (2) (3) (4) (5) (6) (7) (8) (9) 5 1 4 2 10 8 11 8 2 Business People - $875 per passenger CEO - $1000 per passenger IBM Executives - $850 per passenger Last-minute Persons - $1500 per passenger Passengers from another airlines oversold flight - $1000 per passenger Travel Agents - $875 per passenger Sales Mangers - $900 per passenger Passengers from another airlines oversold flight - $1300 per passenger CEOs running up to the plane at departure - $1500 per passenger 11-21 The final stage of the game requires each student to complete the last two columns of the tally sheet, given the actual passengers who show up for each of the demand groups they accepted (i.e., booking ID in column one). In Phase III the number of passengers who show up and the revenue contribution are reported, again using the PowerPoint presentation. Phase III: Actual Passengers Who Show Up for Flight and Resulting Revenue (A) 70 Sales Representatives Show Up $19,250 (B) 50 Professors Show Up $10,000 (C) 25 Reliable MBAs Show Up $3,750 (D) 5 Person Family Show Up $1,500 (E) 35 Conventioneers Show Up $6,300 (F) 15 Conventioneers Show Up $4,500 (G) 70 Sportswriters Show Up $24,500 (H) 20 Person High School Basketball Team Shows Up $6,000 (I) 25 High School Band Members Show Up $6,875 (J) 68 Government Officials Show Up $26,520 (K) 25 Marketing Managers Show Up $11,250 (L) 10 Golfers Show Up $3,250 (M 8 Nurses Show Up $2,200 (N) 30 Republican Congressmen Show Up $13,500 (O) 30 Club Med Vacationers Show Up $11,250 (P) 20 Shouldice Hernia Patients Show Up $6,000 (1) 2 Business People Show Up $1,750 (2) 1 CEO Shows Up $1,000 (3) 3 IBM Executives Show Up $2,550 (4) 2 Last-minute People Show Up $3,000 (5) 5 Oversold Passengers Show Up $5,000 (6) 8 Travel Agents Show Up $7,000 (7) 11 Sales Managers Show Up $9,900 (8) 4 Oversold Passengers Show Up $5,200 (9) 2 CEOs Show Up $3,000 After Phase III is revealed, the total number of passengers and gross revenue is known. Net revenue is determined by adjusting for the costs of oversale or spoilage. Upon completion of the game, the instructor records the total revenue for the flight for each student on the black board. We find a range of $20,000 to $60,000 is not uncommon. CASE: SEQUOIA AIRLINES 1. For the forecast period (i.e., July - December), determine the number of new trainees who must be hired at the beginning of each month so that total personnel costs for the flight attendant staff and training program are minimized. Formulate the problem as a linear programming model and solve . Sequoia Airlines faces the problem of planning the optimal use of its resources during the next six months. It must hire new employees and develop them through three stages of training, first as trainees, then as junior flight attendants, and finally as full flight attendants. Moreover, Sequoia must meet staff requirements for all flights during the training period. This problem is analyzed using total personnel cost as the selection criterion. Other resources in addition to the available personnel create constraints that must be considered when hiring and training new employees. We will solve the problem by using linear programming and then follow up with an 11-22 analysis of the objective function and the constraints and assumptions on which they are based. We will also offer an interpretation of the results. Because cost is the only criterion we will use to solve this problem, the objective function is straightforward. One assumption is noteworthy: we assume that once an experienced attendant becomes an instructor, he or she continues to receive the instructor's salary even when there is a surplus of instructors who act as attendants. The objective function, therefore, becomes the sum of the number of employees in each class during each period multiplied by the appropriate wage. Minimize: Z = 750( T1 + T2 + T3 + T4 + T5 + T6 ) + 1050( J 1 + J 2 + J 3 + J 4 + J 5 + J 6 ) + 1400( F1 + F2 + F3 + F4 + F5 + F6 ) + 1500( I 1 + I 2 + I 3 + I 4 + I 5 + I 6 ) We must also deal with several resource constraints. First, we must meet the requirement for hours of in-flight attendant service. We assume that a surplus instructor works the same number of hours as an experienced attendant does. 140 J1 + 125F1 + 125S1 14000 140 J 2 + 125F2 + 125S2 16000 140 J 3 + 125F3 + 125S3 13000 140 J 4 + 125F4 + 125S4 12000 140 J5 + 125F5 + 125S5 18000 140 J 6 + 125F6 + 125S6 20000 11-23 Next, we must satisfy the constraint that requires the total number of hours for junior attendants to be less than or equal to 25 percent of the total attendant hours in each month. 140 J i 0.25(140 J i + 125 Fi + 125S i ) fori = 1,2,3,4,5,6 Also, there must be one instructor for every five trainees. I i 0.2Ti fori = 1,2,3,4,5,6 The remaining constraints deal with the relationships between the status of employees during the current and prior periods. In this case we make assumptions that pertain to the initial staffing levels for the positions of junior attendant, full attendant, and instructor. Ji = 0.8Ti 1 Fi = 0.95 Ji 1 + 0.92 Fi 1 Ii = 0.95Ii 1 + 0.005Fi 1 Si = Ii 0.2Ti For i = 1,2,3,4,5,6 Except J1 = 8, F1 = 119, I1 = 6 We see a total of 42 constraints in the following computer model. 11-24 Min 1050J1+ 1050J2+ 1050J3+ 1050J4+ 1050J5+ 1050J6+ 1400F1+ 1400F2+ Subject to (1) 140J1+ 125F1+ 125S1 >= 14000 (2) 140J2+ 125F2+ 125S2 >= 16000 (3) 140J3+ 125F3+ 125S3 >= 13000 (4) 140J4+ 125F4+ 125S4 >= 12000 (5) 140J5+ 125F5+ 125S5 >= 18000 (6) 140J6+ 125F6+ 125S6 >= 20000 (7) 105Jl-31.25Fl-31.25S1 <= 0 (8) 105J2-31.25F2-31.25S2 <= 0 (9) 105J3-31.25F3-31.25S3 <= 0 (10) 105J4-31.25F4-31.25S4 <= 0 (11) 105J5-31.25F5-31.25S5 <= 0 (12) 105J6-31.25F6-31.25S6 <= 0 (13) 1I1-.2T1 >= 0 (14) 112-.2T2 >= 0 (15) 113-.2T3 >= 0 (16) 114-.2T4 >= 0 (17) 115-.2T5 >= 0 (18) 116-.2T6 >= 0 (19) 1J1 = 8 (20) 1F1 = 119 (21) 111 = 6 (22) -111+ 1Sl+ .2T1 = 0 (23) -112+ 1S2+ .2T2 = 0 (24) -113+ 1S3+ .2T3 = 0 (25) -114+ 1S4+ .2T4 = 0 (26) -115+ 1S5+ .2T5 = 0 (27) -116+ 1S6+ .iT6 = 0 (28) 1J2-.8T1 = 0 (29) 1J3-.8T2 = 0 (30) 1J4-.8T3 = 0 (31) 1J5-.8T4 = 0 (32) 1J6-.8T5 = 0 (33) -.95J1-.92F1+ 1F2 = 0 (34) -.95J2-.92F2+ 1F3 = 0 (35) -.95J3-.92F3+ 1F4 = 0 (36) -.95J4-.92F4+ 1F5 = 0 (37) -.95J5-.92F5+ 1F6 = 0 (38) -.005F1-.95I1+ 112 = 0 (39) -.005F2-.9512+ 113 = 0 (40) -.005F3-.9513+ 114 = 0 (41) -.005F4-.9514+ 115 = 0 (42) -.005F5-.9515+ 116 = 0 2. How would you deal with the noninteger results? The real number value solution for this problem is shown in Table 1. However, it should be noted that Sequoia could only hire whole people, not fractions of people. Ordinarily we would attempt to apply integer programming to this problem, but, unfortunately, an integer solution is not possible owing to the large number of constraints that have decimal parameters. Nevertheless, we can still obtain an integer solution by assigning ranges to some of the decimal parameters. An example integer solution is presented in Table 2. 11-25 TABLE 1 Sequoia Airlines Real Number Solution Variables No. Names 1 J1 2 J2 3 J3 4 J4 5 J5 6 J6 7 F1 8 F2 9 F3 10 F4 11 F5 12 F6 13 I1 14 I2 15 I3 Minimized OBJ Final Solution for SEQUOIA AIRLINES Page : 1 Opportunity Variables Solution Cost No. Names Solution +8.0000000 0 16 I4 +6.7955503 +4.1294594 0 17 I5 +6.9693007 0 0 18 I6 +7.1805620 +18.374796 0 19 S1 +4.9676352 +27.182201 0 20 S2 +6.2950001 +21.434549 0 21 S3 +1.9719510 +119.00001 0 22 S4 0 +117.08000 0 23 S5 +1.6106634 +111.63660 0 24 S6 +7.1805620 +102.70567 0 25 T1 +5.1618242 +111.94527 0 26 T2 0 +128.81274 0 27 T3 +22.968494 +6.0000000 0 28 T4 +33.977749 +6.2950001 0 29 T5 +26.793188 +6.5656500 0 30 0 T6 = 1177115 Iteration = 35 Elapsed CPU seconds = 16.85938 Opportunity Cost 0 0 0 0 0 0 0 0 0 0 +1686.3875 0 0 0 +1221.3268 Variables No. Names 31 S1 32 A1 33 S2 34 A2 35 S3 36 A3 37 S4 38 A4 39 S5 40 A5 41 S6 42 A6 43 S7 44 S8 45 S9 Minimized OBJ Final Solution for SEQUOIA AIRLINES Page : 2 Opportunity Variables Solution Cost No. Names Solution +2615.9539 0 46 S10 +1280.1985 0 0 47 S11 +694.49170 0 +20.099571 48 S12 +1999.1627 0 20.099571 49 S13 +4.9676352 +1201.0670 0 50 S14 +6.2950001 0 0 51 S15 +1.9719510 +3410.6787 0 52 S16 0 0 0 53 S17 +1.6106634 0 +20.861754 54 S18 +7.1805620 0 20.861754 55 T19 +5.1618242 0 +18.853069 56 T2O 0 0 18.853069 57 T21 0 +3033.9885 0 58 T22 0 +3421.8755 0 59 T23 0 +3550.2668 0 60 0 T24 = 1177115 Iteration = 35 Elapsed CPU seconds = 16.85938 Opportunity Cost 0 0 0 0 0 0 +7367.7900 0 0 725.91003 1053.4916 +4704.0610 0 +2512.4463 0 11-26 Final Solution for SEQUOIA AIRLINES Page : 3 Variables Opportunity Variables No. Names Solution Cost No. Names Solution 61 A25 0 0 70 A34 0 62 A26 0 +2607.7190 71 A35 0 63 A27 0 +2356.6335 72 A36 0 64 A28 0 +937.50000 73 A37 0 65 A29 0 542.37274 74 A38 0 66 A30 0 +937.50000 75 A39 0 67 A31 0 +2779.4475 76 A40 0 68 A32 0 +1589.4298 77 A41 0 69 A33 0 +341.14731 78 0 A42 Minimized OBJ = 1177115 Iteration = 35 Elapsed CPU seconds = 16.85938 J1 J2 J3 J4 J5 J6 =8 =0 =0 =0 = 32 = 13 F1 F2 F3 F4 F5 F6 Opportunity Cost 869.93689 +534.34448 +2092.1052 +956.63367 +6530.5908 +5808.5732 +7693.2349 +1921.5211 +856.63373 TABLE 2 Sequoia Airlines Integer Solution = 119 I1 = 6 T1 = 0 S1 = 6 = 122 I2 = 6 T2 = 0 S2 = 6 = 104 I3 = 6 T3 = 0 S3 = 6 = 102 I4 = 8 T4 = 36 S4 = 0 = 102 I5 = 10 T5 = 15 S5 = 7 = 134 I6 = 12 T6 = 0 S6 = 12 Note that the integer solution is radically different from the continuous solution. This difference is explained by the large ranges given to the constraint parameters. Which solution should be used? Perhaps rounding up the continuous solution and then adjusting for constraint requirements would provide the best solution. In fact, this solution is preferable because the integer solution implicitly assumes that management can control the number of employees who quit each month which is clearly not the case. In order to use the integer solution, we might first use forecasting methods to estimate the number of employees who will quit and then use integer programming to plan for hiring new employees. 3. Discuss how you would use the LP model to make your hiring decision for the next six months . Following the hiring decisions for the current month (July in this case), the program can be run again at a later date by dropping the July requirements and adding the January requirements. This "leap frog" approach may be used to fine tune proposed hiring levels by extending the planning horizon for another six months. The decision on future hiring (July through December in this case) is postponed until a commitment must be made. At that time the decision can be based on a six-month projection of requirements. CHAPTER QUIZ QUESTIONS True/False 1. The use of a ski-resort hotel for business conventions during the summer is an example of using the complementary service strategy. (F) 2. Overbooking is a strategy that can be used to smooth demand. (F) 3. The strategy of segmenting demand to reduce variation makes use of the fact that demand for a service is seldom from a homogeneous source. (T) 11-27 4. Differential pricing is an attempt to make peak usage periods unattractive by imposing a penalty on the consumer for using the service during peak periods. (F) 11-28 5. 6. Yield management is the process of allocating a fixed perishable resource to several market segments in the most profitable manner. (T) 7. The work shift-scheduling problem is important in service organizations that face a cyclic demand for their services. (T) 8. Work shift scheduling attempts to deal with the service utilization problem by controlling the demand for the service and partitioning it so that utilization is uniform. (F) 9. A drawback to increased consumer participation is the fact that service quality is no longer completely under the control of the provider of the service. (T) 10. When peaks of activity are persistent and predictable such as meal times for restaurants, off duty personnel can be placed on standby to supplement regular employees. (F) 11. Cross training of employees as a strategy to increase flexibility is feasible only when there exist tasks, the demands for which peak at different times. (T) 12. Time perishability of service capacity is a challenge for service managers because customers demand immediate service. (F) 13. Yield management is a pricing and capacity allocation system that was developed by American Airlines. (T) 14. Yield management is a strategy that manages both demand and capacity. (T) 15. An example of segmenting demand is movie theaters offering matinee prices before 6:00 p.m. (F) 16. Expected loss for an overbooking reservation strategy would be calculated by, multiplying the loss for each no-show possibility by its probability of occurrence, and then adding the products. (T) 17. The critical fractile is a cumulative probability of demand. (T) 18. Using part-time personnel at fast-food restaurants allows capacity to vary with demand. (T) 19. A disadvantage of yield management is that it cannot be implemented in real time. (F) 20. Some restaurants use tables and chairs instead of booths to create more flexible capacity. (T) 21. SABRE is the name for American Airlines yield management system. (F) 22. Service capacity is defined in terms of an achievable level of output per unit time. (T) 23. Level demand and chase capacity are the two generic strategies for capacity management. (F) 11-29 Multiple Choice 1. The strategy of segmenting demand is feasible only when a. demand is not from a homogeneous source.* b. demand is cyclic and predictable. c. arrivals for service are random. d. making appointments is impossible. 2. The purpose of differential pricing is to a. make peak period usage unattractive. b. make off-peak usage attractive.* c. charge customers according to their ability to pay. d. adjust capacity to demand. 3. A good overbooking strategy should a. minimize the expected opportunity cost of idle service capacity. b. balance the expected opportunity cost of idle service capacity and expected cost of turning away customers who have reservations.* c. minimize the expected cost of turning away reservations. d. none of the above; services should try to avoid overbooking. 4. Bars that offer happy hours in the afternoon are using the strategy of a. creating adjustable capacity. b. developing complementary services. c. increasing customer participation. d. promoting off-peak demand.* 5. Which of the following is not a strategy for managing capacity? a. Developing complementary services* b. Using part-time employees c. Forecasting demand d. Scheduling shifts 11-30 6. Which of the following strategies is inappropriate for managing capacity and demand? a. Smooth customer demand by offering price incentives. b. Scheduling staff to meet variations in forecasted customer demand. c. Decrease customer participation in the service process.* d. Promoting off-peak use of facilities. 7. A cruise ship has available a certain number of rooms in each of 10 categories of appointment ranging from a two-bunk inside cabin to a suite with an outside patio and indoor sitting area. If a passenger requests a sailing date in which the desired cabin is sold out, which of the following actions would be considered the least viable alternative for the cruise line? a. Offer an upgraded cabin at a reduced price. b. Attempt to steer the passenger to an available date. c. Overbook the cabin.* d. Offer a downgraded cabin with special privileges such as a $100 certificate for use in the casino. 8. What on of the following is not a characteristic of yield management? a. Capacity is relatively fixed. b. There is one homogeneous customer class.* c. The service is considered a perishable inventory. d. Demand fluctuates yet is somewhat predictable. 9. Which one of the following four steps in "daily workshift scheduling" is out of order? a. Forecast demand. b. Convert to operator requirements. c. Assign operators to shifts.* d. Schedule shifts. 11-31 10. A medical clinic has two doctors and each can treat 25 patients a day. The doctors see walk-in patients whose arrival times cannot be controlled, and also patients who have made appointments. Knowing the expected number of walk-ins per day, appointments are scheduled to utilize the doctors fully. The following table gives the expected number of walk-ins for a particular week: Day: Mon. Tue. Wed. Th. Fri. Expected Walk-ins: 45 35 40 45 40 What is the total number of appointments that can be scheduled during this week? a. 30 b. 35 c. 40 d. 45* 11. Faced with variable demand and a perishable capacity, a service manager can smooth demand by a. using part-time help during peak hours. b. scheduling workshifts to vary workforce needs according to demand. c. increasing the customer self-service content of the service. d. using reservations and appointments.* 12. Which of the following is not a strategy to manage demand? a. cross-training employees* b. offering price incentives c. developing reservation systems d. partitioning demand 13. A health club offering a reduced rate membership for students to workout before 4:00 p.m. on weekdays is a. promoting off-peak demand.* b. partitioning demand. c. using yield management. d. offering price incentives. 11-32 14. Several approaches to demand management exist, but only _______ seeks to maximize revenue. a. promoting off-peak demand b. reservation systems c. offering price incentives d. yield management* 15. Which of the following is not a characteristic of firms using yield management? a. ability to segment their market b. perishable inventory c. variable capacity* d. product sold in advance 16. Which of the following is not an example of the differential pricing policy? a. weekend and night rates for long-distance telephone calls b. difference in hospital fees for walk-in and scheduled services* c. peak-load pricing by utility companies d. none of the above 17. In using the critical fractile criterion P(d<x) = C u/(Cu + Co) for overbooking the d refers to: a. the cost of overestimating demand. b. the cost of underestimating demand. c. the number of rooms overbooked. d. the number of no-shows based on past experience.* 18. For a chase demand strategy which of the following does not have a high trade-off? a. Employee utilization b. Labor-skill level* c. Labor turnover d. Supervision required 11-33 19. A restaurant that features special lunchtime combo meals is providing all but one of the following benefits? a. Promotes off-peak demand* b. Increases customer satisfaction c. Decreases service times d. Segment demand 20. All but one of the following is an example of a yield management application: a. RyerFirst b. SABRE* c. HIRO d. Restaurant Catering Software 21. _________ variability is not one of the five sources of customer-induced variability. a. Arrival b. Capability c. Effort d. Demand* 22. ________ is not a strategy for reduction of customer-induced variability. a. Adapt to customer skill levels* b. Require reservations c. Limit service breadth d. Target customers based on capability 23. ________ is not a strategy for accommodating of customer-induced variability. e. Provide generous staffing f. Cross-train employees g. Reward increased effort* h. Do work for customers 11-34

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Fall 2009Professor FitzsimmonsMAN 335 SERVICE OPERATIONS MANAGEMENTSECOND EXAMTIME: 75 MinutesInstructions:This closed book examination is composed of short answer and problem questions withresponses confined to the space provided. Organize your th
CUNY Baruch - MGT - 3121
Fall 2009Professor FitzsimmonsSERVICE OPERATIONS MANAGEMENTFIRST EXAMTIME: 75 MinutesInstructions:This closed book examination is composed of short answer and problem questions withresponses confined to the space provided. Organize your thoughts be
CUNY Baruch - MGT - 3121
%PDF-1.4%7 0 obj&lt;/Linearized 1/L 62230/O 9/E 58032/N 1/T 61941/H [ 461 145]&gt;endobj16 0 obj&lt;/DecodeParms&lt;/Columns 4/Predictor12&gt;/Filter/FlateDecode/ID[&lt;A83588F8523BADDB5685F712DCC3674A&gt;&lt;C77391B2F19AD143BD9B8B1D71D7CDA5&gt;]/Index[7 17]/Info 6 0 R/Length 6
Maryland - FINANCE - BUFN 754
Lecture 12 Bond Pricing ExampleCouponTerm1010.0110.110.5111213109.999.99.598700051530$61.39$23.14$5.35$61.36$23.10$5.34$61.10$22.81$5.20$59.95$21.54$4.64$58.54$20.06$4.03$55.84$17.41$3.03$53.27$15.12$2.29$61.39
Maryland - FINANCE - BUFN 754
Stylized Gfee Calculation - 1 PathOriginal UPBLifetime PDLifetime LGDLifetime Loss RateNote rateTerm (Years)Loan Amort1500002000000.0860.08750.350.40.03010.0350.0450.04755534$122,581.25 $163,623.82$93,928.66 $125,519.77$63,986.71$
Maryland - FINANCE - BUFN 754
Black Scholes4.000InputsSKrTtd1d2N'(d1)N'(d2)Call39400.30.080.25000.040-0.1100.3990.3972.2350.250.9801990.456025Call Option against S3.000ExpectedReturn onS ()0.5160.068-0.017+Put+--0.4840.078+E( r)OptionSharpe
Maryland - FINANCE - BUFN 754
Transition Matrix AnalysisToBAFromA0.955B0.125D0.050Transition Probabilities after 2 YearsFrom0.0350.7250.1000.0100.1500.850ToAABDDB0.91690.21750.10275Sum111D0.0598 0.02330.545 0.23750.159250.738111Possible Year-en
Harvard - ACCT - 401
University of Illinois, Urbana Champaign - ASTR - 121
Lecture 3:Seasons and TimeWednesday, January 23, 131AnnouncementsHomework 1 due tomorrow, 1/25 @ 9amCovers material from Chap. 1 &amp; 2Can also submit directly to your discussion leaderQuiz 2 available todayPlanetarium shows begin next MondayReserv
University of Illinois, Urbana Champaign - ASTR - 121
Lecture 4:The Calendar &amp; Moon PhasesMonday, January 28, 131AnnouncementsHomework 2 due Thursday, 1/31 @ 9amCovers material from Chap. 3Quiz 2 closes tomorrow at 11pmPlanetarium shows begin todayReservations required: see websitePrint out &amp; read
University of Illinois, Urbana Champaign - ASTR - 121
Lecture 5: EclipsesWednesday, January 30, 131AnnouncementsHomework 2 due Thursday @ 9amCovers material from Chap. 3Online submissions: please give your name andAD# in your document, and have your surname inthe file namePlanetarium shows this week
University of Illinois, Urbana Champaign - ASTR - 121
Lecture 6: Models of the SolarSystemMonday, February 4, 131AnnouncementsQuiz 3 closes Tuesday @ 11pmCovers material from Chap. 4Homework 3 due Thursday @ 9amPlanetarium shows end this weekReservations required: go to http:/www.astro.uiuc.edu/aca
University of Illinois, Urbana Champaign - ASTR - 121
Lecture 7:Tycho, Kepler, and GalileoWednesday, February 6, 131AnnouncementsHomework 3 due tomorrow @ 9amPlanetarium shows end tonight!Reservations required: go to http:/www.astro.uiuc.edu/academics/courses/planetarium/Print out &amp; read worksheet
University of Illinois, Urbana Champaign - ASTR - 121
Lecture 8:Newtonian MechanicsMonday, February 11, 131AnnouncementsFirst Hour Exam on Wed., Feb. 20Covers material through Wed. lecturePractice exam available on CompassQuiz 4 closes tomorrow at 11pmCovers sections 4.8, 5.1 to 5.4Homework 4 due T
University of Illinois, Urbana Champaign - ASTR - 121
Lecture 9:Light &amp; Blackbody RadiationWednesday, February 13, 131AnnouncementsFirst hour exam on Wed., Feb. 20 in classCovers material through todays lectureQ&amp;A with TAs on Mon, Feb 18, 3-4pm in 134 AstronomyHomework 4 due tomorrow, 2/14No late HW
University of Illinois, Urbana Champaign - ASTR - 121
Lecture 10: Spectral LinesMonday, February 18, 131AnnouncementsFirst hour exam on Wednesday in classCovers material through Section 5-4 (blackbody radiation)Bring a calculator, pen/pencil, and your i-CardQ&amp;A with TAs on Mon, Feb 18, 3-4pm in 134 As
University of Illinois, Urbana Champaign - PLPA - 200
Dear Uncle,I understand that you now own an apple orchard and have been approached about anoffer to do on-field testing of genetically modified apples. I also understand that you have beenlosing profits because your apples are being bruised during tran
University of Illinois, Urbana Champaign - PLPA - 200
Available online at www.sciencedirect.comFood Policy 33 (2008) 99111www.elsevier.com/locate/foodpolConsumer acceptance, valuation of and attitudes towardsgenetically modied food: Review and implications for food policyMontserrat Costa-Font a,*, Jose
University of Illinois, Urbana Champaign - PLPA - 200
Reference listDaniels, Stephen. &quot;Organizers Confident Washington State Non-GMO Initiative Will HitSignature Goal.&quot; FoodNavigator-USA.com. William Reed Business Media SAS, n.d. Web. 26Feb. 2013. &lt;http:/www.foodnavigator-usa.com/Regulation/Organizers-con
University of Illinois, Urbana Champaign - PLPA - 200
UpdateTRENDS in BiotechnologyVol.22 No.3 March 2004107from the methyltransferase-overexpressing lines contain 65 IU of vitamin E. Because 100 IU is the recommended minimum therapeutic dose to decrease the risk of heart disease, the work of Van Eenenna
University of Illinois, Urbana Champaign - PLPA - 200
ORGANIC ALTERNATIVES FOR LATEBLIGHT CONTROL IN POTATOESNational Sustainable Agriculture Information Servicewww.attra.ncat.orgPEST MANAGEMENT TECHNICAL NOTEAbstract: New strains of late blight have emerged in recent years, making potato production esp
Northeastern - ACCT - 2301
Northeastern - ACCT - 2301
Effect of Volume and CostVolumeOriginal FlexibleBudget Budget100FlexSalesActual BudgetActivityTotalCost Variances Variances Variance90DM200297DL300252OverheadVariable10093Fixed300295Total9009378-1Standard Costs@ 100 unitsStd
Northeastern - ACCT - 2301
Atlas: Will they get their loan? Atlas Co. is beginning its third quarter inwhich peak sales occur. The company has requested a $40,000loan from the bank to meet cashrequirements. Atlas has had problems paying off loansin the past, so the bank has
Northeastern - ACCT - 2301
Jensen Corp. Budgeted Costsand Activity2010 BudgetSalesNumber of unitsManufacturing Overhead CostsDirect Labor- Hours$2,600,00024,000$900,00050,000Predetermined = $900,000 = $18/DLHOverhead Rate50,0006-1Traditional Cost AccountingSystemCu
Northeastern - ACCT - 2301
ACCT 2301Net Present Value and IRRAugust 15, 2012NatchezC o mpanyisc onsideringthepurchaseofnewe quip m entthatwillc ost$125,000.The e quip m entwillsavethec o mpany$42,000peryearincashoperatingc osts.The e quip m enthasanesti matedusefullifeoffiveye
Northeastern - ACCT - 2301
ACCT 2301August 15, 2012Payback MethodBob'sFoodStopisc onsideringinstallingvideoga m esinitsstores.Themachinesc ost$350,000andhaveane stimatedsevenyearusefullife.Ignoreinco m e taxes.Thefollowingprojectedinco m e state mentisprovided:Re quired:Bob's
Northeastern - ACCT - 2301
ACCT 2301Capital BudgetingAugust 15, 2012In2007,BiggsC o mpanypurchasede quip m entwithanexpe ctedusefullifeof5years.Theinitialc ostofthee quip mentwas$90,000.Biggs'sc ostofcapitalis10 %.Atthetimeitpurchasedthee quip ment,Biggsprojectedthefollowingca
Northeastern - ACCT - 2301
ACCT 2301August 14, 2012Variance AnalysisPfeifferCompanyproducesanumberofproducts,includingasmallAmericanflag.Thefirm,whichbeganoperationsatthebeginningofthecurrentyear,usesastandardcostsystem.ThestandardcostsforoneAmericanFlagareprovidedbelow:Direc
Northeastern - ACCT - 2301
ACCT 2301August 13, 2011Fixed Overhead VariancesFor2009,MexiaC orporationbudgetedfixedoverheadof$500,000.Itexpe ctedtomake 20,000unitsduringtheyear.A ctualfixedoverheadwas$508,000,andMexiaactuallymade22,000unitsofproduct.Re quired:(a)Calculatethefix
Northeastern - ACCT - 2301
ACCT 2301August 13, 2011Flexible Budgeting VariancesAtthebeginningoftheperiod,CanaanC o mpanyesti matedthatsaleswouldbe5,000units.A ctualsalestotaled7,000units.The managerwasveryexcitedabouttheunexpectedadditionalinco m e thattheextra2,000unitswouldp
Northeastern - ACCT - 2301
ACCT 2301August 13, 2011Material VariancesMarrickC o mpanymakesoneproduct,forwhichithasestablishedthefollowingstandardsformaterials:Averagequantityofmaterialperunitofproduct:4.5poundsPriceperpoundofmaterials,$8DuringMarch,Marrickmade10,000unitsofthe
Northeastern - ACCT - 2301
ACCT 2301Pro-forma Financial StatementsAugust 8, 2012McGriff Gifts Corporation begins business today, December 31, 2010. Janet Novak, thepresident, is trying to prepare the company's master budget for the first three months (January,February, and Mar
Northeastern - ACCT - 2301
ACCT 2301Pro-forma Financial StatementsAugust 8, 2012McGriff Gifts Corporation begins business today, December 31, 2010. Janet Novak, thepresident, is trying to prepare the company's master budget for the first three months (January,February, and Mar
Northeastern - ACCT - 2301
ACCT 2301August 7, 2012Inventory Purchases BudgetSoundOffAudioSyste mssellsandinstallscarstereosyste ms.Managersneedtoprepareaninventorypurchasesbudgetforthefirstquarterof2010.The c o mpany'ssalesbudgetforthefirstquarterisprovidedbelow:Basedonpastex
Northeastern - ACCT - 2301
ACCT 2301Cash BudgetingAugust 7, 2012Amanage m entaccountantwasworkingonacashbudgetforBarcelonaC o mpanywhenheaccidentallyspilledhisc offee.So m e oftheliquidsplatteredonhisworkingpapersrenderingafewoftheamountsillegible.Thebudgetwithmissingamountsin
Northeastern - ACCT - 2301
ACCT 2301Comprehensive ProblemRelevant CostingAugust 2, 2012Emerson Company's appliances division produces a food blender. The vice president in charge ofthe division is evaluating the income statement showing annual revenues and expensesassociated
Northeastern - ACCT - 2301
ACCT 2301Relevant CostingAugust 2, 2011The MendozaC o mpanyistryingtodecidewhethertoreplaceapackingmachinethatitusestopacksalsaintoindividualservingsizepackages.The followinginfor mationisprovided:NewMachine:Re quired:1)C o mparethec ostsoverafivey
Northeastern - ACCT - 2301
ACCT 2301Class Session August 1, 2012Special Order DecisionsDavisDriveways,Inc.(DDI)poursc oncretedrivewaysforsinglefamilyho m es.DDIusesa c ostpluspricingapproach.The c o mpanysaccountantpreparedthefollowingreportshowing howDDIe stablishedthepricepe
Northeastern - ACCT - 2301
ACCT 2301Class Session August 1, 2012Relevant CostsQuestion 1Andyistryingtodecidewhichoneoftwojoboffershewillaccept.S everalite msarepresented below:Sele cttheite msthatarerelevanttoAndy'sdecision. Question 2Robertisdecidingwhethertore mainintheho
Northeastern - ACCT - 2301
ACCT 2301Class Session July 10, 2012Cost AveragingHollywoodHitsoperatesam ovietheaterthathasm onthlyfixedexpensesof$15,000.The c o mpanypaysthefilmdistributor$2.00perticketsold.Intheupco mingyear,HollywoodHitsexpe ctstosell6,000ticketsperm onthinthew
Northeastern - ACCT - 2301
ACCT 2301Class Session July 10, 2012Contribution Income StatementsFlintC o mpanyoperatesaclothingstorethatreportedthefollowingoperatingresultsfor2012:Required:Prepareaninco m e state mentforFlintC o mpanyusingthec ontributionmarginfor mat.
Northeastern - ACCT - 2301
ACCT 2301Class Session July 23, 2012Cost ClassificationOfficeBestProductsproducesavarietyofofficeproductsincludingtwom od els(standardanddeluxe )ofahandheldstapler.Anumberofthec o mpany'sactivityrelatedc ostsaredescribedinthefollowingtable:Required:C
Northeastern - ACCT - 2301
ACCT 2301Monday, July 09, 2012Coststhatmightbeincurredbyservice,merchandising,andmanufacturingcompaniesaredescribedbelow:A.CommissionspaidtosalesassociatesatNordstromsdepartmentstoreB.InsuranceonafactoryproducingSonyflatscreentelevisionsC.Chickenbrea
Northeastern - ACCT - 2301
ACCT 2301Monday, July 9, 2012Problem 1C o mpletethefollowingtabletoindicateyourunderstandingoffixedandvariablec ostbehaviorbyinsertingoneofthefollowingresponsesineachbox:&quot; Re mainc onstant,&quot; &quot;Increase, &quot; or&quot;D e crease. &quot;Problem 2Far mingtonC orporat
Northeastern - ACCT - 2301
ACCT 2301Tuesday, July 3, 2012The JiffyManufacturingC o mpanywasstartedatthebe ginningofthecurrentyearwhenitacquired$100,000fro m itsowners.Duringtheyear,thec o mpanyincurredthefollowingc osts,allforcash:The c o mpanyproduced10,000unitsofproductandsol
Northeastern - ACCT - 2301
ACCT 2301Class Session July 25, 2012Case Analysis involving ABC CostingA dialysis clinic provides two types of treatment for its patients. Hemodialysis (HD), an in-housetreatment, requires that patients visit the clinic three times each week for dialy
Northeastern - ACCT - 2301
ACCT 2301Class Session July 24, 2012ABC Costing with Target PricingSchafferFurnitureCorporationmakestwotypesofdiningtables,EleganceforformaldiningandComfortforcasualdining,atitssinglefactory.Withtheeconomybeginningtoexperiencearecession, KenSchaffer,
Northeastern - ACCT - 2301
ACCT 2301Class Session July 23, 2012Overhead Cost AllocationBallentineIndustriesproducetwosurgeprotectors:VC620withsixoutletsandPH630witheight outletsandtwotelephonelineconnections.Becauseoftheseproductdifferences,thecompanyplans touseactivitybasedco
Northeastern - ACCT - 2301
ACCT 2301Class Session July 23, 2012Multiple Choice Questions on Cost Allocation1.Martinez Company makes two types of chairs.One of the chairs is a rocking chair. The other isa straight-back chair. Both chairs are made byhand. Martinez Company uses
Northeastern - ACCT - 2301
ACCT 2301July 18, 2012Cost AllocationPart 2Mallett Manufacturing Co. expects to make 30,000 chairs during the 2011 accounting period.The company made 3,200 chairs in January. Materials and labor costs for January were$16,000 and $24,000, respectivel
Northeastern - ACCT - 2301
ACCT 2301July 18, 2012Cost AllocationPart 1Stevens Air is a large airline company that pays a customer relations representative $4,000 permonth. The representative, who processed 1,000 customer complaints in January and 1,300complaints in February,
Northeastern - ACCT - 2301
ACCT 2301Class Session July 18, 2012Joint Process CostingBaileyMeatProcessingPlantprocesseshogstoproducethreejointproducts:bacon,sausage,andporkchops.The c o mpanyincursc o m m onproc essingc ostsof$50,000perbatch.Eachbatchyields7,000poundsofbacon,10