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/