IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i17p3621-d1222008.html
   My bibliography  Save this article

Optimal Selection of Multi-Fuel Engines for Ships Considering Fuel Price Uncertainty

Author

Listed:
  • Yiwei Wu

    (Department of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China)

  • Hongyu Zhang

    (Division of Logistics and Transportation, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China)

  • Fei Li

    (Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China)

  • Shuaian Wang

    (Department of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China)

  • Lu Zhen

    (School of Management, Shanghai University, Shanghai 200436, China)

Abstract

Maritime transport serves as the backbone of international trade, accounting for more than 90% of global trade. Although maritime transport is cheaper and safer than other modes of transport, it often means long sailing distances, which often results in substantial fuel consumption and emissions. Liner shipping, a vital component of maritime transport, plays an important role in achieving sustainable maritime operations, necessitating the implementation of green liner shipping practices. Therefore, this study formulates a nonlinear integer programming model for a multi-fuel engine selection optimization problem to optimally determine ship order choice in terms of the fuel engine type, fleet deployment, fuel selection, and speed optimization, with the aim of minimizing the total weekly cost containing the weekly investment cost for ship orders and the weekly fuel cost. Given the complexity of solving nonlinear models, several linearization techniques are applied to transform the nonlinear model into a linear model that can be directly solved by Gurobi. To evaluate the performance of the linear model, 20 sets of numerical instances with, at most, seven routes are conducted. The results show that among 20 numerical instances, 16 sets of numerical instances are solved to optimality within two hours. The average gap value of the remaining four sets of numerical instances that cannot be solved to optimality within two hours is 0.51%. Additionally, sensitivity analyses are performed to examine crucial parameters, such as the weekly investment cost for ordering ships, the ship ordering budget, and the potential application of new fuel engine types, thereby exploring managerial insights. In conclusion, our findings indicate that equipping ships with low-sulfur fuel oil engines proves to be the most economical advantageous option in the selected scenarios. Furthermore, ordering ships with low-sulfur fuel, oil + methanol + liquefied natural gas engines, is beneficial when the weekly investment cost for such engines does not exceed $13,000, under the current parameter value setting.

Suggested Citation

  • Yiwei Wu & Hongyu Zhang & Fei Li & Shuaian Wang & Lu Zhen, 2023. "Optimal Selection of Multi-Fuel Engines for Ships Considering Fuel Price Uncertainty," Mathematics, MDPI, vol. 11(17), pages 1-14, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3621-:d:1222008
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/17/3621/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/17/3621/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peter J. Stavroulakis & Stratos Papadimitriou, 2022. "Total cost of ownership in shipping: a framework for sustainability," Journal of Shipping and Trade, Springer, vol. 7(1), pages 1-14, December.
    2. Alam Md Moshiul & Roslina Mohammad & Fariha Anjum Hira & Nurazean Maarop, 2022. "Alternative Marine Fuel Research Advances and Future Trends: A Bibliometric Knowledge Mapping Approach," Sustainability, MDPI, vol. 14(9), pages 1-27, April.
    3. Perčić, Maja & Vladimir, Nikola & Jovanović, Ivana & Koričan, Marija, 2022. "Application of fuel cells with zero-carbon fuels in short-sea shipping," Applied Energy, Elsevier, vol. 309(C).
    4. Richa Agarwal & Özlem Ergun, 2008. "Ship Scheduling and Network Design for Cargo Routing in Liner Shipping," Transportation Science, INFORMS, vol. 42(2), pages 175-196, May.
    5. Wu, Wei-Ming, 2020. "The optimal speed in container shipping: Theory and empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    6. Wang, Shuaian & Qi, Jingwen & Laporte, Gilbert, 2022. "Governmental subsidy plan modeling and optimization for liquefied natural gas as fuel for maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 304-321.
    7. Qiang Meng & Shuaian Wang & Henrik Andersson & Kristian Thun, 2014. "Containership Routing and Scheduling in Liner Shipping: Overview and Future Research Directions," Transportation Science, INFORMS, vol. 48(2), pages 265-280, May.
    8. Christiansen, Marielle & Hellsten, Erik & Pisinger, David & Sacramento, David & Vilhelmsen, Charlotte, 2020. "Liner shipping network design," European Journal of Operational Research, Elsevier, vol. 286(1), pages 1-20.
    9. Zhen, Lu & Hu, Yi & Wang, Shuaian & Laporte, Gilbert & Wu, Yiwei, 2019. "Fleet deployment and demand fulfillment for container shipping liners," Transportation Research Part B: Methodological, Elsevier, vol. 120(C), pages 15-32.
    10. Jun Xia & Kevin X. Li & Hong Ma & Zhou Xu, 2015. "Joint Planning of Fleet Deployment, Speed Optimization, and Cargo Allocation for Liner Shipping," Transportation Science, INFORMS, vol. 49(4), pages 922-938, November.
    11. Wang, Shuaian & Liu, Zhiyuan & Meng, Qiang, 2015. "Segment-based alteration for container liner shipping network design," Transportation Research Part B: Methodological, Elsevier, vol. 72(C), pages 128-145.
    12. Lai, Xiaofan & Wu, Lingxiao & Wang, Kai & Wang, Fan, 2022. "Robust ship fleet deployment with shipping revenue management," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 169-196.
    13. Wang, Yadong & Wang, Shuaian, 2021. "Deploying, scheduling, and sequencing heterogeneous vessels in a liner container shipping route," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    14. Maxim A. Dulebenets & Junayed Pasha & Olumide F. Abioye & Masoud Kavoosi, 2021. "Vessel scheduling in liner shipping: a critical literature review and future research needs," Flexible Services and Manufacturing Journal, Springer, vol. 33(1), pages 43-106, March.
    15. Zhen, Lu & Wu, Yiwei & Wang, Shuaian & Laporte, Gilbert, 2020. "Green technology adoption for fleet deployment in a shipping network," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 388-410.
    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. Jesper Zwaginga & Benjamin Lagemann & Stein Ove Erikstad & Jeroen Pruyn, 2024. "Optimal Ship Fuel Selection under Life Cycle Uncertainty," Sustainability, MDPI, vol. 16(5), pages 1-18, February.

    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. Ksciuk, Jana & Kuhlemann, Stefan & Tierney, Kevin & Koberstein, Achim, 2023. "Uncertainty in maritime ship routing and scheduling: A Literature review," European Journal of Operational Research, Elsevier, vol. 308(2), pages 499-524.
    2. Lai, Xiaofan & Wu, Lingxiao & Wang, Kai & Wang, Fan, 2022. "Robust ship fleet deployment with shipping revenue management," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 169-196.
    3. Moussawi-Haidar, Lama & Nasr, Walid & Jalloul, Maya, 2021. "Standardized cargo network revenue management with dual channels under stochastic and time-dependent demand," European Journal of Operational Research, Elsevier, vol. 295(1), pages 275-291.
    4. Meng, Qiang & Lee, Chung-Yee, 2016. "Liner container assignment model with transit-time-sensitive container shipment demand and its applicationsAuthor-Name: Wang, Shuaian," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 135-155.
    5. Zheng, Jianfeng & Sun, Zhuo & Zhang, Fangjun, 2016. "Measuring the perceived container leasing prices in liner shipping network design with empty container repositioning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 94(C), pages 123-140.
    6. Wang, Yadong & Wang, Shuaian, 2021. "Deploying, scheduling, and sequencing heterogeneous vessels in a liner container shipping route," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    7. Zhen, Lu & Wu, Yiwei & Wang, Shuaian & Laporte, Gilbert, 2020. "Green technology adoption for fleet deployment in a shipping network," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 388-410.
    8. Wetzel, Daniel & Tierney, Kevin, 2020. "Integrating fleet deployment into liner shipping vessel repositioning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    9. Yu, Jingjing & Tang, Guolei & Song, Xiangqun, 2022. "Collaboration of vessel speed optimization with berth allocation and quay crane assignment considering vessel service differentiation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    10. Chen, Jingxu & Jia, Shuai & Wang, Shuaian & Liu, Zhiyuan, 2018. "Subloop-based reversal of port rotation directions for container liner shipping network alteration," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 336-361.
    11. Gang Du & Chuanwang Sun & Jinxian Weng, 2016. "Liner Shipping Fleet Deployment with Sustainable Collaborative Transportation," Sustainability, MDPI, vol. 8(2), pages 1-15, February.
    12. 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.
    13. Wang, Yadong & Meng, Qiang, 2021. "Optimizing freight rate of spot market containers with uncertainties in shipping demand and available ship capacity," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 314-332.
    14. Yuzhe Zhao & Jingmiao Zhou & Zhongxiu Peng & Peng Jia, 2024. "Joint planning for fuel switching ships in a liner shipping network with transit time," Operational Research, Springer, vol. 24(3), pages 1-36, September.
    15. Dongping Song, 2021. "A Literature Review, Container Shipping Supply Chain: Planning Problems and Research Opportunities," Logistics, MDPI, vol. 5(2), pages 1-26, June.
    16. Zhuge, Dan & Wang, Shuaian & Wang, David Z.W., 2021. "A joint liner ship path, speed and deployment problem under emission reduction measures," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 155-173.
    17. Xi Jiang & Haijun Mao & Yadong Wang & Hao Zhang, 2020. "Liner Shipping Schedule Design for Near-Sea Routes Considering Big Customers’ Preferences on Ship Arrival Time," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    18. Kristian Thun & Henrik Andersson & Magnus Stålhane, 2020. "A Branch-and-Price Algorithm for the Liner Shipping Network Design Problem," SN Operations Research Forum, Springer, vol. 1(4), pages 1-21, December.
    19. Du, Yuquan & Meng, Qiang & Wang, Shuaian & Kuang, Haibo, 2019. "Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 88-114.
    20. Guo, Liming & Zheng, Jianfeng & Du, Haoming & Du, Jian & Zhu, Zhihong, 2022. "The berth assignment and allocation problem considering cooperative liner carriers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).

    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:gam:jmathe:v:11:y:2023:i:17:p:3621-:d:1222008. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.