Skip to main content

A Bi-objective Problem of Scheduling Fuel Supply Vessels

  • Chapter
  • First Online:
Sustainable Freight Transport

Part of the book series: Operations Research/Computer Science Interfaces Series ((ORCS,volume 63))

  • 1367 Accesses

Abstract

Fleets of fuel supply vessels are used to provide ships anchored in ports with different oil products. Demand is satisfied based on a pull system, where oil shipments are triggered by orders placed by customers and delivered on a specific agreed time window. The aim of this paper is to suggest proper scheduling policies for a fleet of fuel supply vessels, under the vessels’ availability/capacity and the customers’ demand constraints, so as to obtain the Pareto optimal solutions that minimize the cost and the total environmental burden expressed in CO2 emissions. The methodology employed for solving the problem is the ∈-constraint approach combined with a heuristic algorithm. The model is tested and evaluated for a small Hellenic oil company’s data. The model can be easily instantiated according to the input data and adjusted to the fleet scheduler’s needs, thus making the decision process faster and leading to lower costs and higher environmental savings.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.00
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The International Maritime Organization is the UN’s specialized agency responsible for the global regulation of issues such as safety, security, and environmental performance of international shipping.

References

  • Agra, A., Andersson, H., Christiansen, M., & Wolsey, L. (2012a). A maritime inventory routing problem: Discrete time formulation and valid inequalities, Working paper. Norwegian University of Science and Technology, NTNU.

    Google Scholar 

  • Agra, A., Christiansen, M., & Delgado, A. (2012b). Mixed integer formulations for a short sea fuel oil distribution problem. Transportation Science. https://doi.org/10.1287/trsc.1120.0416.

  • Al-Khayyal, F., & Hwang, S.-J. (2007). Inventory constrained maritime routing and scheduling for multi-commodity liquid bulk, Part I: Applications and model. European Journal of Operational Research, 176, 106–130.

    Article  Google Scholar 

  • Brønmo, G., Christiansen, M., & Nygreen, B. (2007). Ship routing and scheduling with flexible cargo sizes. Journal of Operational Research Society, 58(9), 1167–1177.

    Article  Google Scholar 

  • Christiansen, M. (1999). Decomposition of a combined inventory and time constrained ship routing problem. Transportation Science, 33(1), 3–16.

    Article  Google Scholar 

  • Christiansen, M., Fagerholt, K., Nygreen, B., & Ronen, D. (2013). Ship routing and scheduling in a new millennium. European Journal of Operational Research, 228(3), 467–483.

    Article  Google Scholar 

  • Christiansen, M., Fagerholt, K., Nygreen, B., & Ronen, D. (2007). Maritime transportation. In C. Barnhart & G. Laporte (Eds.), Transportation, Handbooks in operational research and management science (Vol. 14, pp. 189–284). Amsterdam: North-Holland.

    Chapter  Google Scholar 

  • Christiansen, M., Fagerholt, K., & Ronen, D. (2004). Ship routing and scheduling: Status and perspectives. Transportation Science, 38(1), 1–18.

    Article  Google Scholar 

  • Christiansen, M., Fagerholt, K., Rachaniotis, N. P., Tveit, I., & Overdal, M. V. (2015). A decision support model for routing and scheduling a fleet of fuel supply vessels, ICCL’15. In: 6th international conference on computational logistics (pp. 46–60), Delft, The Netherlands, 23–25 September.

    Google Scholar 

  • Christiansen, M., Fagerholt, K., Rachaniotis, N. P., & Stålhane, M. (2016). Operational planning of routes and schedules for a fleet of fuel supply vessels. Transportation Research Part E. https://doi.org/10.1016/j.tre.2016.07.009.

  • Fagerholt, K., & Christiansen, M. (2000). A combined ship scheduling and allocation problem. The Journal of the Operational Research Society, 15(7), 834–842.

    Article  Google Scholar 

  • Fagerholt, K., & Lindstad, H. (2007). TurboRouter: An interactive optimisation-based decision support system for ship routing and scheduling. Maritime Economics & Logistics, 9, 214–233.

    Article  Google Scholar 

  • Haley, K. B. (1962). The solid transportation problem. Operations Research, 10(4), 448–463.

    Article  Google Scholar 

  • Halvorsen-Weare, E. E. (2012). Maritime fleet planning and optimization under uncertainty. Doctoral Thesis, Norwegian University of Science and Technology, Trondheim, pp. 55–58.

    Google Scholar 

  • Hvattum, L. M., Fagerholt, K., & Armentano, V. A. (2009). Tank allocation problems in maritime bulk shipping. Computers & Operations Research, 36, 3051–3060.

    Article  Google Scholar 

  • Kobayashi, K., & Kubo, M. (2010). Optimization of oil tanker schedules by decomposition column generation, and time-space network techniques. Japanese Journal of Industrial and Applied Mathematics, 27(1), 161–173.

    Article  Google Scholar 

  • Mansouri, S. A., Lee, H., & Aluko, O. (2015). Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions. Transportation Research Part E: Logistics and Transportation Review, 78, 3–18.

    Article  Google Scholar 

  • Miettinen, K. (1999). Nonlinear multiobjective optimization (pp. 85–87). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Oberthür, S., & Ott, H. E. (1999). The Kyoto protocol: International climate policy for the 21st century. Berlin: Springer.

    Book  Google Scholar 

  • Pang, K.-W., Xu, Z., & Li, C.-L. (2011). Ship routing problem with berthing time clash avoidance constraints. International Journal of Production Economics, 131, 752–762.

    Article  Google Scholar 

  • Rachaniotis, N. P., & Masvoula, M. (2015). A decision support system for scheduling fleets of fuel supply vessels, Working paper. Available at SSRN.: http://ssrn.com/abstract=2708171

  • Shi, Y. (2016). Reducing greenhouse gas emissions from international shipping: Is it time to consider market-based measures? Marine Policy, 64, 123–134.

    Article  Google Scholar 

  • Tveit, I., & Overdal, M.V. (2013). Optimization of a supply vessel scheduling and fuel type allocation problem for a Hellenic Oil Company. Master Degree Thesis, Norwegian University of Science and Technology.

    Google Scholar 

  • Walsh, C., & Bows, A. (2012). Size matters: Exploring the importance of vessel characteristics to inform estimates of shipping emissions. Applied Energy, 98, 128–137.

    Article  Google Scholar 

Web References

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. P. Rachaniotis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rachaniotis, N.P., Koutsoukis, N.S., Mourmouris, J.C., Tsoulfas, G.T. (2018). A Bi-objective Problem of Scheduling Fuel Supply Vessels. In: Zeimpekis, V., Aktas, E., Bourlakis, M., Minis, I. (eds) Sustainable Freight Transport. Operations Research/Computer Science Interfaces Series, vol 63. Springer, Cham. https://doi.org/10.1007/978-3-319-62917-9_4

Download citation

Publish with us

Policies and ethics