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Optimizing Airline Operations for a Providence-Based Airline

This study evaluates the effective operation of a Providence-based airline by selecting optimal destinations and aircraft models to maximize profit. It analyzes potential destinations including Detroit, Chicago, Newark, and more, while considering aircraft choices such as the Embraer 175 and Boeing 737-500. Through mathematical modeling, the study establishes an objective function to maximize profit and sets constraints related to budget, capacity, and time. The results reveal key insights on flight frequency and market share based on aircraft utilized, highlighting areas for improvement in demand estimation and operational costs.

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Optimizing Airline Operations for a Providence-Based Airline

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  1. Air Providence A Study into the Effective Operation of a Providence-Based Airline Ilya Gofshteyn AM121/EN131 December, 2006

  2. Objectives • Choose optimal destination(s) • Choose optimal aircraft model(s) • Maximize profit for airline based out of Providence, RI

  3. Possible Destinations Detroit Chicago Newark Philadelphia Providence Baltimore DC Charlotte Phoenix Orlando

  4. Aircraft Choices Embraer 175 Boeing 737-500 Boeing 757-200

  5. The Model The Objective Function Max Z = Σij xij * (Pi * tij - 2.5 * Fi * dj) – (20 * Ci * xij + Ci * ui * 1000000) Where xij = Number of flights per year on aircraft i and route j Pi = Capacity of aircraft i dj = Distance from Providence to destination j Fi = Fuel cost per mile of aircraft i Ci = Capital cost of using aircraft i Nj = Demand for flight from PVD to destination j Si = Speed of aircraft i mij = Flight time from PVD to destination j on aircraft I mij = dj / Si +1 tij = Ticket price of for route from Providence to destination j tij = Ci * dj / 75

  6. Constraints • Budget Constraint Σ(Ciui) ≤ 100 • Non-negativity, Binary, and Integer Constraints xij ≥ 0 xij can only be integers ui is Binary • Capacity Constraint Σ(xijPj) ≤ Nj • Time Constraint xij ≤ (365 * 18) / (2 * mij) or xij * mij ≤ 3285

  7. Results • Flights per day FLIGHTSPERDAY( BOEING737, BAL) 5.000000 FLIGHTSPERDAY( BOEING737, CHA) 3.000000 FLIGHTSPERDAY( BOEING737, CHI) 3.000000 FLIGHTSPERDAY( BOEING737, DET) 2.000000 FLIGHTSPERDAY( BOEING737, NEW) 1.000000 FLIGHTSPERDAY( BOEING737, ORL) 2.000000 FLIGHTSPERDAY( BOEING737, PHI) 6.000000 FLIGHTSPERDAY( BOEING737, PHO) 1.000000 FLIGHTSPERDAY( BOEING737, WAS) 4.000000 • Flights per year and Market Share FLIGHTS( BOEING737, BAL) 1825.000 62.6% FLIGHTS( BOEING737, CHA) 1095.000 89.6% FLIGHTS( BOEING737, CHI) 1095.000 38.2% FLIGHTS( BOEING737, DET) 730.0000 70% FLIGHTS( BOEING737, NEW) 365.0000 69.1% FLIGHTS( BOEING737, ORL) 730.0000 49.7% FLIGHTS( BOEING737, PHI) 2190.000 80% FLIGHTS( BOEING737, PHO) 365.0000 61.8% FLIGHTS( BOEING737, WAS) 1460.000 84.4% Average = 67.26%

  8. More Results and Observations • Only Boeing 737’s used; reflective of reality. • The objective value is 0.1171709E+08 = ~ $11,717,090 • All routes are traveled. • Least traveled routes are shortest and longest: Newark and Phoenix. • Most traveled are medium-range routes: Philadelphia, Baltimore. • Flight duration not an important aspect; speed doesn’t vary much.

  9. Areas of Improvement • Demand function: there isn’t one. ~ Ticket prices ~ Competition ~ Season ~ Travel pattern variations • Cost of operation is more complicated ~ Personnel ~ Load Factor ~ Maintenance

  10. Bibliography • “Bureau of Transportation Statistics”. <www.bts.gov> November 16, 2006. • “The Boeing Company”. <www.boeing.com> November 27, 2006. • “Empresa Brasileira de Aeronáutica S.A.”. <www.embraer.com> November 28, 2006.

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