IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v152y2021ics1366554521001812.html
   My bibliography  Save this article

Vessel routing optimization for floating macro-marine debris collection in the ocean considering dynamic velocity and direction

Author

Listed:
  • Duan, Gang
  • Aghalari, Amin
  • Chen, Li
  • Marufuzzaman, Mohammad
  • Ma, Junfeng

Abstract

Floating macro-marine debris becomes a global environmental problem when emitted into the ocean. It damages the marine ecosystem, threatens human health, and also causes incalculable economic losses.Due to the impacts of ocean currents and winds, marine debris has been transported to different locations over time and time window cannot be ignored to navigate the locations of marine debris. To effectively mitigate the risk, we propose the vessel routing optimization with a time window to collect and remove marine debris. Ocean currents and winds also affect the velocity and direction of the collecting vessel. We first employ GNOME software to determine the debris trajectory and set a time window for each debris location. A mixed-integer nonlinear programming model considering vessel velocity is proposed to minimize the total debris collection cost. We propose two customized solution approaches: Branch-and-Cut (B&C) algorithm and two-stage Adaptive Large Neighborhood Search (ALNS) based heuristic algorithm to solve the proposed mathematical model in a reasonable timeframe. A computational study in waters off Boston is used to validate the proposed model and the solution algorithms. The result indicates that the average optimality gap for GUROBI and B&C algorithm is 17.53% and 10.52%, respectively, while this gap is only 3.44% for the ALNS algorithm. Moreover, the average computing time of the ALNS algorithm is roughly 24 and 17 times faster than that of the GUROBI and the B&C algorithm, respectively. The experimental results show that distance from debris location to the harbor is positively related to the collection cost and negatively related to the average usage of vessels’ capacity, and the dispersion of debris is also positively related to the fuel consumption of vessels.

Suggested Citation

  • Duan, Gang & Aghalari, Amin & Chen, Li & Marufuzzaman, Mohammad & Ma, Junfeng, 2021. "Vessel routing optimization for floating macro-marine debris collection in the ocean considering dynamic velocity and direction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
  • Handle: RePEc:eee:transe:v:152:y:2021:i:c:s1366554521001812
    DOI: 10.1016/j.tre.2021.102414
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554521001812
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tre.2021.102414?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. David Pisinger & Stefan Ropke, 2010. "Large Neighborhood Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 399-419, Springer.
    2. Markov, Iliya & Varone, Sacha & Bierlaire, Michel, 2016. "Integrating a heterogeneous fixed fleet and a flexible assignment of destination depots in the waste collection VRP with intermediate facilities," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 256-273.
    3. Real, Luiza Bernardes & Contreras, Ivan & Cordeau, Jean-François & de Camargo, Ricardo Saraiva & de Miranda, Gilberto, 2021. "Multimodal hub network design with flexible routes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    4. Stefan Ropke & David Pisinger, 2006. "An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows," Transportation Science, INFORMS, vol. 40(4), pages 455-472, November.
    5. Ropke, Stefan & Pisinger, David, 2006. "A unified heuristic for a large class of Vehicle Routing Problems with Backhauls," European Journal of Operational Research, Elsevier, vol. 171(3), pages 750-775, June.
    6. 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.
    7. 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.
    8. Jean-François Cordeau, 2006. "A Branch-and-Cut Algorithm for the Dial-a-Ride Problem," Operations Research, INFORMS, vol. 54(3), pages 573-586, June.
    9. Homsi, Gabriel & Martinelli, Rafael & Vidal, Thibaut & Fagerholt, Kjetil, 2020. "Industrial and tramp ship routing problems: Closing the gap for real-scale instances," European Journal of Operational Research, Elsevier, vol. 283(3), pages 972-990.
    10. Karla L. Hoffman & Manfred Padberg, 1993. "Solving Airline Crew Scheduling Problems by Branch-and-Cut," Management Science, INFORMS, vol. 39(6), pages 657-682, June.
    11. Li, Lingyue & Gao, Suixiang & Yang, Wenguo & Xiong, Xing, 2020. "Ship’s response strategy to emission control areas: From the perspective of sailing pattern optimization and evasion strategy selection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    12. Pourhejazy, Pourya & Zhang, Dali & Zhu, Qinghua & Wei, Fangfang & Song, Shuang, 2021. "Integrated E-waste transportation using capacitated general routing problem with time-window," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    13. Zhao, Jun & Huang, Lixia & Lee, Der-Horng & Peng, Qiyuan, 2016. "Improved approaches to the network design problem in regional hazardous waste management systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 52-75.
    14. Miranda, Pablo A. & Blazquez, Carola A. & Vergara, Rodrigo & Weitzler, Sebastian, 2015. "A novel methodology for designing a household waste collection system for insular zones," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 227-247.
    15. Ivan Contreras, 2021. "Hub Network Design," Springer Books, in: Teodor Gabriel Crainic & Michel Gendreau & Bernard Gendron (ed.), Network Design with Applications to Transportation and Logistics, chapter 0, pages 567-598, Springer.
    16. Chen, Yenming J. & Sheu, Jiuh-Biing & Lirn, Taih-Cherng, 2012. "Fault tolerance modeling for an e-waste recycling supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(5), pages 897-906.
    17. Christiansen, Marielle & Fagerholt, Kjetil & Rachaniotis, Nikolaos P. & Stålhane, Magnus, 2017. "Operational planning of routes and schedules for a fleet of fuel supply vessels," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 163-175.
    18. Lin, Dung-Ying & Tsai, Yu-Yun, 2014. "The ship routing and freight assignment problem for daily frequency operation of maritime liner shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 52-70.
    19. Ramos, Tânia Rodrigues Pereira & Gomes, Maria Isabel & Barbosa-Póvoa, Ana Paula, 2014. "Economic and environmental concerns in planning recyclable waste collection systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 34-54.
    20. Wy, Juyoung & Kim, Byung-In & Kim, Seongbae, 2013. "The rollon–rolloff waste collection vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 224(3), pages 466-476.
    21. 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.
    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. Chen, Ran & Yang, Jingjing & Yu, Yugang & Guo, Xiaolong, 2023. "Retrieval request scheduling in a shuttle-based storage and retrieval system with two lifts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 174(C).
    2. Abdullah Battawi & Ellie Mallon & Anthony Vedral & Eric Sparks & Junfeng Ma & Mohammad Marufuzzaman, 2022. "In-Stream Marine Litter Collection Device Location Determination Using Bayesian Network," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    3. Yazdekhasti, Amin & Wang, Jun & Zhang, Li & Ma, Junfeng, 2021. "A multi-period multi-modal stochastic supply chain model under COVID pandemic: A poultry industry case study in Mississippi," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).

    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. Pourhejazy, Pourya & Zhang, Dali & Zhu, Qinghua & Wei, Fangfang & Song, Shuang, 2021. "Integrated E-waste transportation using capacitated general routing problem with time-window," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    2. Masmoudi, Mohamed Amine & Hosny, Manar & Braekers, Kris & Dammak, Abdelaziz, 2016. "Three effective metaheuristics to solve the multi-depot multi-trip heterogeneous dial-a-ride problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 60-80.
    3. Timo Hintsch, 2019. "Large Multiple Neighborhood Search for the Soft-Clustered Vehicle-Routing Problem," Working Papers 1904, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    4. Timo Gschwind & Michael Drexl, 2016. "Adaptive Large Neighborhood Search with a Constant-Time Feasibility Test for the Dial-a-Ride Problem," Working Papers 1624, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    5. Ruf, Moritz & Cordeau, Jean-François, 2021. "Adaptive large neighborhood search for integrated planning in railroad classification yards," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 26-51.
    6. Bergmann, Felix M. & Wagner, Stephan M. & Winkenbach, Matthias, 2020. "Integrating first-mile pickup and last-mile delivery on shared vehicle routes for efficient urban e-commerce distribution," Transportation Research Part B: Methodological, Elsevier, vol. 131(C), pages 26-62.
    7. Neves-Moreira, F. & Amorim, P. & Guimarães, L. & Almada-Lobo, B., 2016. "A long-haul freight transportation problem: Synchronizing resources to deliver requests passing through multiple transshipment locations," European Journal of Operational Research, Elsevier, vol. 248(2), pages 487-506.
    8. Ulsrud, Karl Petter & Vandvik, Anders Helgeland & Ormevik, Andreas Breivik & Fagerholt, Kjetil & Meisel, Frank, 2022. "A time-dependent vessel routing problem with speed optimization," European Journal of Operational Research, Elsevier, vol. 303(2), pages 891-907.
    9. Vidal, Thibaut & Crainic, Teodor Gabriel & Gendreau, Michel & Prins, Christian, 2013. "Heuristics for multi-attribute vehicle routing problems: A survey and synthesis," European Journal of Operational Research, Elsevier, vol. 231(1), pages 1-21.
    10. Hintsch, Timo & Irnich, Stefan, 2018. "Large multiple neighborhood search for the clustered vehicle-routing problem," European Journal of Operational Research, Elsevier, vol. 270(1), pages 118-131.
    11. Schaumann, Sarah K. & Bergmann, Felix M. & Wagner, Stephan M. & Winkenbach, Matthias, 2023. "Route efficiency implications of time windows and vehicle capacities in first- and last-mile logistics," European Journal of Operational Research, Elsevier, vol. 311(1), pages 88-111.
    12. Braekers, Kris & Kovacs, Attila A., 2016. "A multi-period dial-a-ride problem with driver consistency," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 355-377.
    13. Timo Hintsch & Stefan Irnich, 2017. "Large Multiple Neighborhood Search for the Clustered Vehicle-Routing Problem," Working Papers 1701, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    14. Timo Gschwind & Michael Drexl, 2019. "Adaptive Large Neighborhood Search with a Constant-Time Feasibility Test for the Dial-a-Ride Problem," Transportation Science, INFORMS, vol. 53(2), pages 480-491, March.
    15. Bach, Lukas & Hasle, Geir & Schulz, Christian, 2019. "Adaptive Large Neighborhood Search on the Graphics Processing Unit," European Journal of Operational Research, Elsevier, vol. 275(1), pages 53-66.
    16. Arpan Rijal & Marco Bijvank & Asvin Goel & René de Koster, 2021. "Workforce Scheduling with Order-Picking Assignments in Distribution Facilities," Transportation Science, INFORMS, vol. 55(3), pages 725-746, May.
    17. Mo, Pengli & Yao, Yu & D’Ariano, Andrea & Liu, Zhiyuan, 2023. "The vehicle routing problem with underground logistics: Formulation and algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    18. Masson, Renaud & Lahrichi, Nadia & Rousseau, Louis-Martin, 2016. "A two-stage solution method for the annual dairy transportation problem," European Journal of Operational Research, Elsevier, vol. 251(1), pages 36-43.
    19. Ulrike Ritzinger & Jakob Puchinger & Richard Hartl, 2016. "Dynamic programming based metaheuristics for the dial-a-ride problem," Annals of Operations Research, Springer, vol. 236(2), pages 341-358, January.
    20. Kallestad, Jakob & Hasibi, Ramin & Hemmati, Ahmad & Sörensen, Kenneth, 2023. "A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 309(1), pages 446-468.

    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:eee:transe:v:152:y:2021:i:c:s1366554521001812. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.