Logistics network planning for offshore air transport of oil rig crews

•We propose an optimization model to oil platforms crew transport network planning.•Model defines airports location, demand’s balance among airports and fleet profile.•We do a case study with Brazilian south and southeast oil platform real data.•We prove the necessity to consider helicopter capacity...

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Veröffentlicht in:Computers & industrial engineering 2014-09, Vol.75, p.41-54
Hauptverfasser: Hermeto, Nathália da Silva Sena, Ferreira Filho, Virgílio José Martins, Bahiense, Laura
Format: Artikel
Sprache:eng
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Zusammenfassung:•We propose an optimization model to oil platforms crew transport network planning.•Model defines airports location, demand’s balance among airports and fleet profile.•We do a case study with Brazilian south and southeast oil platform real data.•We prove the necessity to consider helicopter capacity change with flight distance.•The multiple model scenarios intend to enable managers to make better decisions. Oil discoveries of recent years, especially in the pre-salt Santos Basin, reflect a large increase in petroleum exploration and production in Brazil. Accordingly, drilling rig and production platform crew transport demands will increase. This transport will also become more complex as average distance between fields and Brazil’s coast increases. The helicopter, the modal most used for this purpose, is the most efficient means of transport in terms of speed and safety, but also entails high costs. Optimizing the crew transport logistics network thus becomes an economically significant issue. The study presents an optimization model for crew transport logistics network planning. That model aims to provide managers with accurate information to assist their decision making in logistics infrastructure planning. Such decisions involve airfield locations, distribution of demand among airfields and fleet profile. Since composing the fleet involves considerable expenditures, and once made, this composition is not easily changed, we built several scenarios varying in demand and fleet costs to evaluate the behavior of the model we are proposing as regards processing time and quality of the solution. We have obtained good results, despite the increasing complexity of the scenarios.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2014.05.021