Case Study On Revenue Management By: Team 5

Revenue Management Involves managing the short term demand for a fixed perishable inventory in order to maximize the revenue potential for an organization. First developed for American Airlines, was used to determine how many airline seas to be sold at an early reservation discount fare and how many airline seats to be sold at full fare. Thereby helping the airline to increase its average number of passengers in each flight and to maximize the total revenue generated by the combined sale of discount fare and full fare seats.

The Case Leisure Air Leisure air has two Boeing 737-400, one based in Pittsburgh and the other in Newark. Both planes have a coach section with 132-seat capacity. Each morning the Pittsburgh based plane flies to Orlando with a stop over at Charlotte, and the Newark based plane flies to Myrtle beach , also with a stop over at Charlotte. At the end of the day both planes return back to their home bases. To restrict the size of the problem we will restrict our attention to Pittsburgh- Charlotte, Charlotte- Orlando, Newark- Charlotte, and Charlotte – Myrtle beach flight legs for the morning session. Leisure air uses two fare classes: a discount fare Q class & a full fare Y class. Reservations using the discount fare Q class must be made 14 days in advance . Reservations using the full fare Y class can be made anytime with no penalty for changing the reservation to a latter date.

Suppose a customer calls on April 4 and requests a Q class seat on May 5 flight from Pittsburgh to Myrtle Beach Should Leisure Air accept the reservation…? The difficulty in making this decision is that even though Leisure Air may have seats available ,the company may not want to accept this reservation at the Q class fare specially if its possible to sell the same reservation later at Y class fare. We need to develop a linear model that can be used to determine how many seats Leisure Air should allocate to each fare class depending on the projected demand for the various travel routes.

Decision Variables:- We need to allocate sixteen decision variables one for each origin destination itenary fare alternative. Using P for Pittsburgh, N for Newark, C for charlotte, M for Myrtle Beach and O for Orlando. Decision variables will take the following form PCQ= number of seats allocated to Pittsburgh- Charlotte Q class. PMQ= number of seats allocated to Pittsburgh- Myrtle Q class POQ= number of seats allocated to Pittsburgh- Orlando Q class PCY= Number of seats allocated to Pittsburgh- Charlotte Y class. * NCQ= number of seats allocated to Newark-Charlotte Q class COY= number of seats allocated to Charlotte- Orlando Y class.

ODIF ORIGIN DESTINATION FARE CLASS ODIF CODE FARE FORECASTED DEMAND 1 PITTSBURG CHARLOTTE Q PCQ 178 33 2 MYRTLE BEACH PMQ 268 44 3 ORLANDO POQ 228 45 4 Y PCY 380 16 5 PMY 456 6 POY 560 11 7 NEWARK NCQ 199 26 8 NMQ 249 56 9 NOQ 349 39 10 NCY 385 15 NMY 444 12 NOY 580 13 CMQ 179 64 14 CNY COQ 224 46 COY 582

Objective Function:- Maximize Z = 178PCQ +268PMQ+228POQ+380PCY+ 456PMY+560POY+199NCQ+249NMQ+ 349NOQ+385NCY+444NMY+580NOY+ 179CMQ+380CMY+224COQ+582COY

Constraints Capacity Constraint:- - PCQ+PMQ+POQ+PCY+PMY+POY<=132 Pittsburgh-Charlotte - PCQ+PMQ+POQ+PCY+PMY+POY<=132 Newark-Charlotte - NCQ+NMQ+NOQ+NCY+NMY+NOY<=132 Charlotte-Myrtle Beach - PMQ+PMY+NMQ+NMY+CMQ+CMY<=132 Charlotte-Orlando - POQ+POY+NOQ+NOY+COQ+COY<=132

Demand Constraint Non negativity Constraint: PCQ<=33 PMQ<=44 POQ<=45 PCY<=16 PMY<=6 POY<=11 NCQ<=26 NMQ<=56 NOQ<=39 NCY<=15 NMY<=7 NOY<=9 CMQ<=64 CMY<=8 COQ<=46 COY<=10 Non negativity Constraint: - PCQ,PMQ,POQ,PCY,……..COY>=0