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OPTIMIZING HELICOPTER TRANSPORT OF OIL RIG CREWS AT PETROBRAS
Ayberk Göksenin ÜLKER
Samet AKÇAFeyza KESKİN
OUTLINE1. Introduction2. Current Process3. Problem Definition4. Related Work5. Project Description6. Model7. Solution Methodology8. Evaluation and Benefits9. Additional Examples and Comparison10. Questioning and Conclusion11. References
INTRODUCTIONPetrobras
Founded in 1953Major oil producer of BrazilUnder management of government25,000 workers
Oil production of Brazil: 2 million barrels/day, 13th in world90% by PetrobrasMilestone: 1974 - Campos basin explored
CURRENT PROCESS80 offshore oil-production
platforms
1,900 workers to be
transported by helicopter
Between platforms and 4
mainland bases
2-weeks shift, 3-weeks rest
Largest non-military
helicopter operations
CURRENT PROCESSMacaé:
65 daily flights33 helicopters
São Tomé:30 daily flights7 helicopters
Jacarepaguá & Vitória15 daily flights5 helicopters
CURRENT PROCESSFlight and passenger
assignments done
manually, based on
Travel demands
Departure time and
destination
Selected from a fixed
timetable by passengers
Helicopter availability
PROBLEM DEFINITIONComplexities
Limited number of available helicopters
Strict operational rules
8 types of helicopter with different
Operational characteristics
Capacity
Cost
PROBLEM DEFINITIONObjectives
Output required each day at each
airport including
1. Flight scheduling
2. Helicopter routing
3. Assignment of workers to flights
Output required within 1 hour
PROBLEM DEFINITIONObjectives
1. Satisfy all demands
2. Improve safety
Reduce number of
landings
3. Minimize costs
Helps decreasing
flight time
PROBLEM DEFINITIONConstraints1. Flights start and finish at same base2. Max 5 fligths/day for each helicopter3. Inspection time between flights4. Limited number of landings for each flight5. Limited number of legs for each passenger6. Limited number of helicopters visiting same
platform for each departure time7. Lunch stops8. Helicopter capacity (determined by route
length)
RELATED WORKin Petrobras
Investments in IT to assist manual operation
Attempts to implement a decision support
system, by Galvão & Guimarães (1990)
Routes for fixed departure times
Unsuccessful due to worker resistance
Not fully automated, still required manual input
RELATED WORKin LiteratureHelicopter-scheduling studies
Timlin & Pulleyblank (1992)Heuristics, not concerned with time
factorTjissen (2000)
SDVRP, constant capacityHernadvolgyi (2004)
Single helicopter
PROJECT DESCRIPTIONContract signed with Gapso
Operational version of scheduling system (2005) – 50 weeks
IT functionality (2006) – 6 monthsMPROG
2005 – São Tomé2006 – Macaé2008 – Vitória & Jacarepaguá5 years contract for support and improvementTraining and assistance
MODEL
Billions of variablesNP-hard
Generalization of SDVRP
Solution MethodColumn-generation
Network flow formulation assign passengers to previously selected routes, employs heuristics
Which variables to use for a good solutionChallenge: maximum possible number of
passengers being picked for each demand adhf = qd or remaining capacity
required columns cannot be generatedSolution: Disaggregating demands
adhf = 1 if corresponding passenger is on the flight
Solution MethodColumn-generation sub-problem
Dual variables: , Computation of reduced cost of :
Determine h and f with minimum and satisfy landing number constratins NP-hard (prize collecting TSP)
Solution MethodHeuristic Procedure
Most departure times in timetable serve small
number of platforms
Max 5 landings in each flight
For each departure time and helicopter,
seeking profitable flights, with fixed number of
landings
Solution MethodHeuristic Procedure
Generate all possible routes with 1 or 2 landingsGenerate routes with 3,4 and 5 landings by
neighborhood searchFor each route, compute Solve minimum-cost-flow problem to assign
passengersSum and (optimal value of MCF) to find Incorporate with negative ’s into the restricted
integer programStop local search when a column with negative
reduced cost is found
Solution MethodMCF network:
Stop nodes: bases & platformsDemand nodes: passengersOptimum flow value: = (computed before)
Solution MethodMain Algorithm
Decompose the problem: Generation of flights & assembly
Assembly done by integer programming model
Solution MethodTo ease the solution of MIP constraints
are relaxedEquations to ≥ inequalitiesAllowing demand to be oversatisfied
Postoptimization:
Evaluation and Benefits18% fewer landings8% less flight time14% reduction in costsAnnual saving: ~ $24 million Scheduling process improved
In afternoon, schedules of next day can be generated
Time for analysis and adjustments if necessaryHuman factor eliminated
Evaluation and BenefitsBefore (manual method observation for 354
days):On 255 days: landings on same platform limit
violatedOn 202 days: inspection between flights
violatedOn 212 days: capacity was exceeded
In Macaé savings of $50,000/day estimated, compared to manual schedules.
Safety level increased
Additional Examples and ComparisonTurkey: Hierarchical analysis of helicopter logistics
in disaster relief operations by Gülay Barbarosoğlu, Linet Özdamar and Ahmet ÇevikAim: scheduling helicopter activities in a relief
disaster operationAssigning, scheduling and routing of pilots, flights
and helicoptersMixed Integer Programming was developed with makespan minimization objective
Additional Examples and ComparisonAbroad: Routing helicopters for crew exchangeson off-shore locations (North Sea-Holland)
Aim: determining a flight schedule for helicopters and exchanging crew with minimizing the cost of flights.
Determined as Split Delivery Vehicle Routing Problem(SDVRP)
Column generation procedure was used
ConclusionMPROG is used at Campos basin rigs
Planned integration with flight and passenger control
systems
5 years contract, still in use
Dynamic development and changes required due to
variabilities of recent reserve discoveries
2009 finalist in the Wagner Prize, an INFORMS award for
the best cases of practical use of Operational Research
Referenceshttp://www.gapso.com.br/en/the-gapso-
solution-could-save-us-24-million-per-year-in-aircraft-operations/
http://www.petrobras.com/en/about-us/