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Ayberk Göksenin ÜLKER Samet AKÇA Feyza KESKİN. OUTLINE 1. Introduction 2. Current Process 3. Problem Definition 4. Related Work 5. Project Description.

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Presentation on theme: "Ayberk Göksenin ÜLKER Samet AKÇA Feyza KESKİN. OUTLINE 1. Introduction 2. Current Process 3. Problem Definition 4. Related Work 5. Project Description."— Presentation transcript:

1 Ayberk Göksenin ÜLKER Samet AKÇA Feyza KESKİN

2 OUTLINE 1. Introduction 2. Current Process 3. Problem Definition 4. Related Work 5. Project Description 6. Model 7. Solution Methodology 8. Evaluation and Benefits 9. Additional Examples and Comparison 10. Questioning and Conclusion 11. References

3 INTRODUCTION Petrobras Founded in 1953 Major oil producer of Brazil Under management of government 25,000 workers Oil production of Brazil: 2 million barrels/day, 13 th in world 90% by Petrobras Milestone: Campos basin explored

4 CURRENT PROCESS 80 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

5 CURRENT PROCESS Macaé: 65 daily flights 33 helicopters São Tomé: 30 daily flights 7 helicopters Jacarepaguá & Vitória 15 daily flights 5 helicopters

6 CURRENT PROCESS Flight and passenger assignments done manually, based on Travel demands Departure time and destination Selected from a fixed timetable by passengers Helicopter availability

7 PROBLEM DEFINITION Complexities Limited number of available helicopters Strict operational rules 8 types of helicopter with different Operational characteristics Capacity Cost

8 PROBLEM DEFINITION Objectives 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

9 PROBLEM DEFINITION Objectives 1. Satisfy all demands 2. Improve safety Reduce number of landings 3. Minimize costs Helps decreasing flight time

10 PROBLEM DEFINITION Constraints 1. Flights start and finish at same base 2. Max 5 fligths/day for each helicopter 3. Inspection time between flights 4. Limited number of landings for each flight 5. Limited number of legs for each passenger 6. Limited number of helicopters visiting same platform for each departure time 7. Lunch stops 8. Helicopter capacity (determined by route length)

11 RELATED WORK in 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

12 RELATED WORK in Literature Helicopter-scheduling studies Timlin & Pulleyblank (1992) Heuristics, not concerned with time factor Tjissen (2000) SDVRP, constant capacity Hernadvolgyi (2004) Single helicopter

13 PROJECT DESCRIPTION Contract signed with Gapso Operational version of scheduling system (2005) – 50 weeks IT functionality (2006) – 6 months MPROG 2005 – São Tomé 2006 – Macaé 2008 – Vitória & Jacarepaguá 5 years contract for support and improvement Training and assistance

14 MODEL Billions of variables NP-hard Generalization of SDVRP

15 Solution Method Column-generation Network flow formulation assign passengers to previously selected routes, employs heuristics Which variables to use for a good solution Challenge: maximum possible number of passengers being picked for each demand a dhf = q d or remaining capacity required columns cannot be generated Solution: Disaggregating demands a dhf = 1 if corresponding passenger is on the flight

16 Solution Method

17 Heuristic 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

18 Solution Method

19

20 Main Algorithm Decompose the problem: Generation of flights & assembly Assembly done by integer programming model

21 Solution Method To ease the solution of MIP constraints are relaxed Equations to inequalities Allowing demand to be oversatisfied Postoptimization:

22 Evaluation and Benefits 18% fewer landings 8% less flight time 14% reduction in costs Annual saving: ~ $24 million Scheduling process improved In afternoon, schedules of next day can be generated Time for analysis and adjustments if necessary Human factor eliminated

23 Evaluation and Benefits Before (manual method observation for 354 days): On 255 days: landings on same platform limit violated On 202 days: inspection between flights violated On 212 days: capacity was exceeded In Macaé savings of $50,000/day estimated, compared to manual schedules. Safety level increased

24 Additional Examples and Comparison Turkey: Hierarchical analysis of helicopter logistics in disaster relief operations by Gülay Barbarosoğlu, Linet Özdamar and Ahmet Çevik Aim: scheduling helicopter activities in a relief disaster operation Assigning, scheduling and routing of pilots, flights and helicopters Mixed Integer Programming was developed with makespan minimization objective

25 Additional Examples and Comparison Abroad: Routing helicopters for crew exchanges on 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

26 Conclusion MPROG 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

27 References could-save-us-24-million-per-year-in-aircraft- operations/


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