A genetic search-based heuristic for a fleet size and periodic routing problem with application to offshore supply planning

  • Thomas Borthen
  • Henrik Loennechen
  • Xin WangEmail author
  • Kjetil Fagerholt
  • Thibaut Vidal
Research Paper


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.


Offshore 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.


  1. Aas B, Halskau Ø Sr, Wallace SW (2009) The role of supply vessels in offshore logistics. Marit. Econ. Logist 1(3):302–325CrossRefGoogle Scholar
  2. Ahuja RK, Magnanti TL, Orlin JB (1993) Network flows: theory, algorithms, and applications. Pearson Prentice-Hall, Upper Saddle RiverGoogle Scholar
  3. Cordeau J-F, Laporte G, Mercier A (2001) A unified tabu search heuristic for vehicle routing problems with time windows. J Oper Res Soc 52(8):928–936CrossRefGoogle Scholar
  4. Fagerholt K, Lindstad H (2000) Optimal policies for maintaining a supply service in the Norwegian Sea. Omega 28(3):269–275CrossRefGoogle Scholar
  5. Francis PM, Smilowitz KR, Tzur M (2008) The period vehicle routing problem and its extensions. In: Golden BL, Raghavan S, Wasil EA (eds) The vehicle routing problem: latest advances and new challenges. Springer, US, pp 73–102CrossRefGoogle Scholar
  6. Glover F, Laguna M (1997) Tabu search. Kluwer Academic Publishers, NorwellCrossRefGoogle Scholar
  7. Halvorsen-Weare EE, Fagerholt K (2011) Robust supply vessel planning. In: Pahl J, Reiners T, Voß S (eds) Network optimization. INOC 2011, Lecture Notes in Computer Science, vol 6701. Springer, Berlin, pp 559–573CrossRefGoogle Scholar
  8. Halvorsen-Weare EE, Fagerholt K (2017) Optimization in offshore supply vessel planning. Optim Eng 18(1):317–341CrossRefGoogle Scholar
  9. Halvorsen-Weare EE, Fagerholt K, Nonås LM, Asbjørnslett BE (2012) Optimal fleet composition and periodic routing of offshore supply vessels. Eur J Oper Res 223(2):508–517CrossRefGoogle Scholar
  10. Hoff A, Andersson H, Christiansen M, Hasle G, Løkketangen A (2010) Industrial aspects and literature survey: fleet composition and routing. Comput Oper Res 37(12):2041–2061CrossRefGoogle Scholar
  11. Irnich S, Desaulniers G (2005) Shortest path problems with resource constraints. In: Desaulniers G, Desrosiers J, Solomon MM (eds) Column generation, chap 2. Springer, Berlin, pp 33–65CrossRefGoogle Scholar
  12. Mitchell M (1998) An introduction to genetic algorithms. MIT press, CambridgeGoogle Scholar
  13. Norlund EK, Gribkovskaia I (2013) Reducing emissions through speed optimization in supply vessel operations. Trans Res Part D: Transp Environ 23:105–113CrossRefGoogle Scholar
  14. Norlund EK, Gribkovskaia I, Laporte G (2015) Supply vessel planning under cost, environment and robustness considerations. Omega 57:271–281CrossRefGoogle Scholar
  15. Pantuso G, Fagerholt K, Hvattum LM (2014) A survey on maritime fleet size and mix problems. Eur J Oper Res 235(2):341–349CrossRefGoogle Scholar
  16. 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
  17. Vidal T, Crainic TG, Gendreau M, Lahrichi N, Rei W (2012) A hybrid genetic algorithm for multidepot and periodic vehicle routing problems. Oper Res 60(3):611–624CrossRefGoogle Scholar
  18. Vidal T, Crainic TG, Gendreau M, Prins C (2014) A unified solution framework for multi-attribute vehicle routing problems. Eur J Oper Res 234(3):658–673CrossRefGoogle Scholar
  19. Vidal T, Crainic TG, Gendreau M, Prins C (2015) Time-window relaxations in vehicle routing heuristics. J Heuristics 21(3):329–358CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany and EURO - The Association of European Operational Research Societies 2017

Authors and Affiliations

  1. 1.Department of Industrial Economics and Technology ManagementNorwegian University of Science and Technology (NTNU)TrondheimNorway
  2. 2.SINTEF OceanTrondheimNorway
  3. 3.Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)Rio de JaneiroBrazil

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