IDEAS home Printed from https://ideas.repec.org/a/spr/josatr/v2y2017i1d10.1186_s41072-017-0021-y.html
   My bibliography  Save this article

Shipping Optimisation Systems (SOS): tramp optimisation perspective

Author

Listed:
  • Said El Noshokaty

    (El Esteshary Information Systems (EIS))

Abstract

This research paper is to announce a new policy to all systems which are sensitive to time. In tramp cargo transportation, as an example, the current policy is to select for each ship the cargo mix which contributes more to a gross-profit objective, assuming deterministic cargo transport demand. Since tramp cargo transportation is sensitive to time, where time varies considerably from one alternative ship voyage to another. The new policy considers this objective less profitable than gross-profit-per-day objective, assuming both deterministic and stochastic cargo transport demand. To introduce this new policy, SOS; a suite of decision support systems, is developed to optimise tramp shipping using a stochastic gross-profit-per-day objective. For operational purposes, SOS selects the most profitable cargo mix. This mix is selected because of the higher gross profit it is expected to yield and the less number of days it takes to generate such profit. For long-term planning purposes, SOS uses the optimal gross profit of each ship voyage, created by the system, to allocate fleet units to cargo trade areas, specifying their frequency of calls to maximise fleet annual gross profit. A useful application of this fleet allocation is that the allocated frequency of calls may be considered as representing the demand on services of utilities of ports, canals, and straits, and may be used to assess the competitiveness of these utilities. Utility and logistics planner, via sensitivity and what-if analysis, can determine whether calling at a utility of a trade area is sensitive to changes made to utility dues and staying time, cargo quantities and freight rates, cargo handling rates and charges, and ship speed and fuel consumption. For appraising purposes, SOS includes new ships in the allocation process, in competition with old ones, to find the share each new ship adds to total gross profit each year. SOS then applies the Net Present Value formula to gross profit of each new ship, along with other cash flow and cost of investment. SOS similar systems may be tailored for other means of cargo transport; namely cargo airplanes, trains, and trucks. The impact of SOS on any logistics and supply chain system is that it maintains the shortest possible transportation time owners of transport units can afford. Case studies are brought to demonstrate research findings.

Suggested Citation

  • Said El Noshokaty, 2017. "Shipping Optimisation Systems (SOS): tramp optimisation perspective," Journal of Shipping and Trade, Springer, vol. 2(1), pages 1-36, December.
  • Handle: RePEc:spr:josatr:v:2:y:2017:i:1:d:10.1186_s41072-017-0021-y
    DOI: 10.1186/s41072-017-0021-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s41072-017-0021-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1186/s41072-017-0021-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Leif H. Appelgren, 1969. "A Column Generation Algorithm for a Ship Scheduling Problem," Transportation Science, INFORMS, vol. 3(1), pages 53-68, February.
    2. Marielle Christiansen & Kjetil Fagerholt & David Ronen, 2004. "Ship Routing and Scheduling: Status and Perspectives," Transportation Science, INFORMS, vol. 38(1), pages 1-18, February.
    3. Charlotte Vilhelmsen & Richard M. Lusby & Jesper Larsen, 2017. "Tramp ship routing and scheduling with voyage separation requirements," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(4), pages 913-943, October.
    4. Fagerholt, Kjetil, 2001. "Ship scheduling with soft time windows: An optimisation based approach," European Journal of Operational Research, Elsevier, vol. 131(3), pages 559-571, June.
    5. George B. Dantzig & Philip Wolfe, 1960. "Decomposition Principle for Linear Programs," Operations Research, INFORMS, vol. 8(1), pages 101-111, February.
    6. Christiansen, Marielle & Fagerholt, Kjetil & Nygreen, Bjørn & Ronen, David, 2013. "Ship routing and scheduling in the new millennium," European Journal of Operational Research, Elsevier, vol. 228(3), pages 467-483.
    7. Said El Noshokaty, 2014. "Block-angular linear ratio programmes," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 19(3), pages 338-357.
    8. B. J. Powell & A .N. Perkins, 1997. "Fleet deployment optimization for liner shipping: an integer programming model," Maritime Policy & Management, Taylor & Francis Journals, vol. 24(2), pages 183-192, January.
    9. Gerald G. Brown & Glenn W. Graves & David Ronen, 1987. "Scheduling Ocean Transportation of Crude Oil," Management Science, INFORMS, vol. 33(3), pages 335-346, March.
    10. Fagerholt, Kjetil & Christiansen, Marielle & Magnus Hvattum, Lars & Johnsen, Trond A.V. & Vabø, Thor J., 2010. "A decision support methodology for strategic planning in maritime transportation," Omega, Elsevier, vol. 38(6), pages 465-474, December.
    11. Lin, Dung-Ying & Liu, Hui-Yen, 2011. "Combined ship allocation, routing and freight assignment in tramp shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(4), pages 414-431, July.
    12. Dan O. Bausch & Gerald G. Brown & David Ronen, 1998. "Scheduling short-term marine transport of bulk products," Maritime Policy & Management, Taylor & Francis Journals, vol. 25(4), pages 335-348, October.
    13. Leif H. Appelgren, 1971. "Integer Programming Methods for a Vessel Scheduling Problem," Transportation Science, INFORMS, vol. 5(1), pages 64-78, February.
    14. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    15. Fagerholt, Kjetil & Lindstad, Håkon, 2000. "Optimal policies for maintaining a supply service in the Norwegian Sea," Omega, Elsevier, vol. 28(3), pages 269-275, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Oktaviani Turbaningsih, 2022. "The study of project cargo logistics operation: a general overview," Journal of Shipping and Trade, Springer, vol. 7(1), pages 1-19, December.
    2. Said El Noshokaty, 2021. "Shipping optimization systems (SOS) for tramp: stochastic cargo soft time windows," Journal of Shipping and Trade, Springer, vol. 6(1), pages 1-19, December.
    3. Maria Lambrou & Daisuke Watanabe & Junya Iida, 2019. "Shipping digitalization management: conceptualization, typology and antecedents," Journal of Shipping and Trade, Springer, vol. 4(1), pages 1-17, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meng, Qiang & Wang, Shuaian & Lee, Chung-Yee, 2015. "A tailored branch-and-price approach for a joint tramp ship routing and bunkering problem," Transportation Research Part B: Methodological, Elsevier, vol. 72(C), pages 1-19.
    2. Marielle Christiansen & Kjetil Fagerholt & David Ronen, 2004. "Ship Routing and Scheduling: Status and Perspectives," Transportation Science, INFORMS, vol. 38(1), pages 1-18, February.
    3. Wu, Lingxiao & Pan, Kai & Wang, Shuaian & Yang, Dong, 2018. "Bulk ship scheduling in industrial shipping with stochastic backhaul canvassing demand," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 117-136.
    4. Kjetil Fagerholt *, 2004. "Designing optimal routes in a liner shipping problem," Maritime Policy & Management, Taylor & Francis Journals, vol. 31(4), pages 259-268, October.
    5. Ricardo Gatica & Pablo Miranda, 2011. "Special Issue on Latin-American Research: A Time Based Discretization Approach for Ship Routing and Scheduling with Variable Speed," Networks and Spatial Economics, Springer, vol. 11(3), pages 465-485, September.
    6. Wu, Lingxiao & Wang, Shuaian & Laporte, Gilbert, 2021. "The Robust Bulk Ship Routing Problem with Batched Cargo Selection," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 124-159.
    7. Harilaos N. Psaraftis, 2019. "Ship routing and scheduling: the cart before the horse conjecture," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(1), pages 111-124, March.
    8. Said El Noshokaty, 2021. "Shipping optimization systems (SOS) for tramp: stochastic cargo soft time windows," Journal of Shipping and Trade, Springer, vol. 6(1), pages 1-19, December.
    9. Gustavo Diz & Luiz Felipe Scavarda & Roger Rocha & Silvio Hamacher, 2014. "Decision Support System for PETROBRAS Ship Scheduling," Interfaces, INFORMS, vol. 44(6), pages 555-566, December.
    10. Hennig, F. & Nygreen, B. & Christiansen, M. & Fagerholt, K. & Furman, K.C. & Song, J. & Kocis, G.R. & Warrick, P.H., 2012. "Maritime crude oil transportation – A split pickup and split delivery problem," European Journal of Operational Research, Elsevier, vol. 218(3), pages 764-774.
    11. K Fagerholt & G Laporte & I Norstad, 2010. "Reducing fuel emissions by optimizing speed on shipping routes," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 523-529, March.
    12. Pache, Hannah & Kastner, Marvin & Jahn, Carlos, 2019. "Current state and trends in tramp ship routing and scheduling," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Digital Transformation in Maritime and City Logistics: Smart Solutions for Logistics. Proceedings of the Hamburg International Conference of Logistics, volume 28, pages 369-394, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    13. Brønmo, Geir & Nygreen, Bjørn & Lysgaard, Jens, 2006. "Column generation approaches to ship scheduling with flexible cargo sizes," CORAL Working Papers L-2006-07, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    14. Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), 2019. "Digital Transformation in Maritime and City Logistics: Smart Solutions for Logistics," Proceedings of the Hamburg International Conference of Logistics (HICL), Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management, volume 28, number 28.
    15. Fagerholt, Kjetil & Christiansen, Marielle & Magnus Hvattum, Lars & Johnsen, Trond A.V. & Vabø, Thor J., 2010. "A decision support methodology for strategic planning in maritime transportation," Omega, Elsevier, vol. 38(6), pages 465-474, December.
    16. Hee-Su Hwang & Siriwat Visoldilokpun & Jay M. Rosenberger, 2008. "A Branch-and-Price-and-Cut Method for Ship Scheduling with Limited Risk," Transportation Science, INFORMS, vol. 42(3), pages 336-351, August.
    17. Rizvanolli, Anisa & Haupt, Alexander & Müller, Peter Marvin & Dornemann, Jorin, 2019. "Fleet based schedule optimisation for product tanker considering shipʼs stability," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Digital Transformation in Maritime and City Logistics: Smart Solutions for Logistics. Proceedings of the Hamburg International Conference of Logistics, volume 28, pages 395-426, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    18. Brønmo, Geir & Nygreen, Bjørn & Lysgaard, Jens, 2010. "Column generation approaches to ship scheduling with flexible cargo sizes," European Journal of Operational Research, Elsevier, vol. 200(1), pages 139-150, January.
    19. Roar Grønhaug & Marielle Christiansen & Guy Desaulniers & Jacques Desrosiers, 2010. "A Branch-and-Price Method for a Liquefied Natural Gas Inventory Routing Problem," Transportation Science, INFORMS, vol. 44(3), pages 400-415, August.
    20. Lin, Dung-Ying & Chang, Yu-Ting, 2018. "Ship routing and freight assignment problem for liner shipping: Application to the Northern Sea Route planning problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 47-70.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:josatr:v:2:y:2017:i:1:d:10.1186_s41072-017-0021-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.