IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i11p1647-d1400919.html
   My bibliography  Save this article

Optimal Routing and Scheduling of Flag State Control Officers in Maritime Transportation

Author

Listed:
  • Xizi Qiao

    (Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China)

  • Ying Yang

    (Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China)

  • Yu Guo

    (Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China)

  • Yong Jin

    (Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China)

  • Shuaian Wang

    (Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China)

Abstract

Maritime transportation plays a pivotal role in the global merchandise trade. To improve maritime safety and protect the environment, every state must effectively control ships flying its flag, which is called flag state control (FSC). However, the existing FSC system is so inefficient that it cannot perform its intended function. In this study, we adopt an optimization method to tackle this problem by constructing an integer programming (IP) model to solve the FSC officer routing and scheduling problem, which aims to maximize the total weight of inspected ships with limited budget and human resources. Then we prove that the IP model can be reformulated into a partially relaxed IP model with the guarantee of the result optimality. Finally, we perform a case study using the Hong Kong port as an example. The results show that our model can be solved to optimality within one second at different scales of the problem, with the ship number ranging from 20 to 1000. Furthermore, our study can be extended by considering the arrangement of working timetables with finer granularity and the fatigue level of personnel.

Suggested Citation

  • Xizi Qiao & Ying Yang & Yu Guo & Yong Jin & Shuaian Wang, 2024. "Optimal Routing and Scheduling of Flag State Control Officers in Maritime Transportation," Mathematics, MDPI, vol. 12(11), pages 1-23, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:11:p:1647-:d:1400919
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/11/1647/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/11/1647/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vojtech Graf & Dusan Teichmann & Michal Dorda & Lenka Kontrikova, 2021. "Dynamic Model of Contingency Flight Crew Planning Extending to Crew Formation," Mathematics, MDPI, vol. 9(17), pages 1-28, September.
    2. Martin Desrochers & Jacques Desrosiers & Marius Solomon, 1992. "A New Optimization Algorithm for the Vehicle Routing Problem with Time Windows," Operations Research, INFORMS, vol. 40(2), pages 342-354, April.
    3. Naderi, Bahman & Begen, Mehmet A. & Zaric, Gregory S. & Roshanaei, Vahid, 2023. "A novel and efficient exact technique for integrated staffing, assignment, routing, and scheduling of home care services under uncertainty," Omega, Elsevier, vol. 116(C).
    4. Ernst, A. T. & Jiang, H. & Krishnamoorthy, M. & Sier, D., 2004. "Staff scheduling and rostering: A review of applications, methods and models," European Journal of Operational Research, Elsevier, vol. 153(1), pages 3-27, February.
    5. Hoong Chuin Lau & Zhi Yuan & Aldy Gunawan, 2016. "Patrol scheduling in urban rail network," Annals of Operations Research, Springer, vol. 239(1), pages 317-342, April.
    6. Wen, Xin & Sun, Xuting & Sun, Yige & Yue, Xiaohang, 2021. "Airline crew scheduling: Models and algorithms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    7. Wen, Xin & Ma, Hoi-Lam & Chung, Sai-Ho & Khan, Waqar Ahmed, 2020. "Robust airline crew scheduling with flight flying time variability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    8. Shubo Wu & Xinqiang Chen & Chaojian Shi & Junjie Fu & Ying Yan & Shengzheng Wang, 2022. "Ship detention prediction via feature selection scheme and support vector machine (SVM)," Maritime Policy & Management, Taylor & Francis Journals, vol. 49(1), pages 140-153, January.
    9. Zhiping Zuo & Yanhui Li & Jing Fu & Jianlin Wu, 2019. "Human Resource Scheduling Model and Algorithm with Time Windows and Multi-Skill Constraints," Mathematics, MDPI, vol. 7(7), pages 1-18, July.
    10. Guan, Yunlin & Xiang, Wang & Wang, Yun & Yan, Xuedong & Zhao, Yi, 2023. "Bi-level optimization for customized bus routing serving passengers with multiple-trips based on state–space–time network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).
    11. J. Arturo Castillo-Salazar & Dario Landa-Silva & Rong Qu, 2016. "Workforce scheduling and routing problems: literature survey and computational study," Annals of Operations Research, Springer, vol. 239(1), pages 39-67, April.
    12. Yang, Zhisen & Yang, Zaili & Yin, Jingbo, 2018. "Realising advanced risk-based port state control inspection using data-driven Bayesian networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 38-56.
    13. Alex Leggate & Seda Sucu & Kerem Akartunalı & Robert van der Meer, 2018. "Modelling crew scheduling in offshore supply vessels," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(6), pages 959-970, June.
    Full references (including those not matched with items on IDEAS)

    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. Tian, Xuecheng & Yan, Ran & Liu, Yannick & Wang, Shuaian, 2023. "A smart predict-then-optimize method for targeted and cost-effective maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 32-52.
    2. Xizi Qiao & Ying Yang & King-Wah Pang & Yong Jin & Shuaian Wang, 2024. "Ship Selection and Inspection Scheduling in Inland Waterway Transport," Mathematics, MDPI, vol. 12(15), pages 1-23, July.
    3. Ding, Yida & Wandelt, Sebastian & Wu, Guohua & Xu, Yifan & Sun, Xiaoqian, 2023. "Towards efficient airline disruption recovery with reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    4. Julien Maheut & Jose P. Garcia-Sabater & Julio J. Garcia-Sabater & Sofia Garcia-Manglano, 2024. "Solving the multisite staff planning and scheduling problem in a sheltered employment centre that employs workers with intellectual disabilities by MILP: a Spanish case study," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 32(3), pages 569-591, September.
    5. Xiang Li & Haoyue Fan & Jiaming Liu & Qifeng Xun, 2022. "Staff scheduling in blood collection problems," Annals of Operations Research, Springer, vol. 316(1), pages 365-400, September.
    6. Neda Tanoumand & Tonguç Ünlüyurt, 2021. "An exact algorithm for the resource constrained home health care vehicle routing problem," Annals of Operations Research, Springer, vol. 304(1), pages 397-425, September.
    7. Wen, Xin & Sun, Xuting & Ma, Hoi-Lam & Sun, Yige, 2022. "A column generation approach for operational flight scheduling and aircraft maintenance routing," Journal of Air Transport Management, Elsevier, vol. 105(C).
    8. Xin Wen & Sai-Ho Chung & Hoi-Lam Ma & Waqar Ahmed Khan, 2024. "Airline crew scheduling with sustainability enhancement by data analytics under circular economy," Annals of Operations Research, Springer, vol. 342(1), pages 959-985, November.
    9. Schrotenboer, Albert H. & Wenneker, Rob & Ursavas, Evrim & Zhu, Stuart X., 2023. "Reliable reserve-crew scheduling for airlines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 178(C).
    10. Liu, Kezhong & Yu, Qing & Yang, Zhisen & Wan, Chengpeng & Yang, Zaili, 2022. "BN-based port state control inspection for Paris MoU: New risk factors and probability training using big data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    11. Jaime Miranda & Pablo A. Rey & Antoine Sauré & Richard Weber, 2018. "Metro Uses a Simulation-Optimization Approach to Improve Fare-Collection Shift Scheduling," Interfaces, INFORMS, vol. 48(6), pages 529-542, November.
    12. J. Arturo Castillo-Salazar & Dario Landa-Silva & Rong Qu, 2016. "Workforce scheduling and routing problems: literature survey and computational study," Annals of Operations Research, Springer, vol. 239(1), pages 39-67, April.
    13. Caballini, Claudia & Paolucci, Massimo, 2020. "A rostering approach to minimize health risks for workers: An application to a container terminal in the Italian port of Genoa," Omega, Elsevier, vol. 95(C).
    14. Yan, Ran & Wang, Shuaian & Zhen, Lu, 2023. "An extended smart “predict, and optimize” (SPO) framework based on similar sets for ship inspection planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    15. Xuecheng Tian & Shuaian Wang, 2022. "Cost-Sensitive Laplacian Logistic Regression for Ship Detention Prediction," Mathematics, MDPI, vol. 11(1), pages 1-15, December.
    16. Zamorano, Emilio & Stolletz, Raik, 2017. "Branch-and-price approaches for the Multiperiod Technician Routing and Scheduling Problem," European Journal of Operational Research, Elsevier, vol. 257(1), pages 55-68.
    17. Wen, Xin & Chung, Sai-Ho & Ji, Ping & Sheu, Jiuh-Biing, 2022. "Individual scheduling approach for multi-class airline cabin crew with manpower requirement heterogeneity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    18. Gamermann, Ronaldo W. & Ferreira, Luciano & Borenstein, Denis, 2023. "Long-term audit staff scheduling and planning: A case study of Brazilian civil aviation authority," Journal of Air Transport Management, Elsevier, vol. 106(C).
    19. Maenhout, Broos & Vanhoucke, Mario, 2010. "A hybrid scatter search heuristic for personalized crew rostering in the airline industry," European Journal of Operational Research, Elsevier, vol. 206(1), pages 155-167, October.
    20. David Rea & Craig Froehle & Suzanne Masterson & Brian Stettler & Gregory Fermann & Arthur Pancioli, 2021. "Unequal but Fair: Incorporating Distributive Justice in Operational Allocation Models," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2304-2320, July.

    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:12:y:2024:i:11:p:1647-:d:1400919. 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.