A genetic search-based heuristic for a fleet size and periodic routing problem with application to offshore supply planning
This paper introduces a genetic search-based heuristic to solve an offshore supply vessel planning problem (SVPP) faced by the Norwegian oil and gas company Statoil. The aim is to help the company in determining the optimal size of supply vessels to charter in and their corresponding voyages and schedules. We take inspiration from the hybrid genetic search with adaptive diversity control (HGSADC) algorithm of Vidal et al. (Oper Res 60(3):611–624, 2012), which successfully addresses a large class of vehicle routing problems, including the multi-period VRP (PVRP), and adapt it to account for some special features that are recurrent in maritime transportation but scarcely found in classical PVRPs, in particular, the possibility of having voyages spanning over multiple time periods in the planning horizon. Our computational experiments show that the proposed heuristic is scalable and stable, being able to solve industrial SVPPs of realistic size while significantly outperforming the existing approaches.
KeywordsOffshore supply vessel planning Fleet sizing Periodic vehicle routing Genetic algorithm
The authors acknowledge financial support from project Maritim Offshore Logistikk Optimering (MOLO) partly funded by the Research Council of Norway. The authors would also like to thank Tor Toftøy and Ellen Karoline Norlund at Statoil.
- Ahuja RK, Magnanti TL, Orlin JB (1993) Network flows: theory, algorithms, and applications. Pearson Prentice-Hall, Upper Saddle RiverGoogle Scholar
- Mitchell M (1998) An introduction to genetic algorithms. MIT press, CambridgeGoogle Scholar
- Shyshou A, Gribkovskaia I, Laporte G, Fagerholt K (2012) A large neighbourhood search heuristic for a periodic supply vessel planning problem arising in offshore oil and gas operations. INFOR: Inf Syst Oper Res 50(4):195–204Google Scholar