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A nonlinear approach to the multiorigin, multidestination fleet deployment problem

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  • Nikiforos A. Papadakis
  • Anastassios N. Perakis

Abstract

The problem of minimal‐cost operation of a fleet of ships carrying a specific amount of bulk cargo from several origin ports to several destination ports during a specified time interval is examined. The fuel oil cost, a major component of the total operating cost, is realistically modeled as a nonlinear function of the vessels' operating speeds. Introduction of both full load and ballast speeds as independent variables results in a nonlinear optimization problem in which the vessels' allocation to the available routes and the optimal speed selection problem are coupled. Within the framework of our model, each vessel of the fleet may load at any origin, unload at a destination and return to the same origin. The solution method developed utilizes specific features of the above fleet deployment model, and may reduce substantially the dimensionality of the problem. Under certain conditions, decoupling of the speed selection from the vessel allocation problem can be achieved, and linear programming can be used to obtain an optimal solution. In the general case, a projected Lagrangian method appears to be more appropriate for the problem under consideration.

Suggested Citation

  • Nikiforos A. Papadakis & Anastassios N. Perakis, 1989. "A nonlinear approach to the multiorigin, multidestination fleet deployment problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 36(4), pages 515-528, August.
  • Handle: RePEc:wly:navres:v:36:y:1989:i:4:p:515-528
    DOI: 10.1002/1520-6750(198908)36:43.0.CO;2-J
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    References listed on IDEAS

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    Cited by:

    1. Du, Yuquan & Chen, Qiushuang & Quan, Xiongwen & Long, Lei & Fung, Richard Y.K., 2011. "Berth allocation considering fuel consumption and vessel emissions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1021-1037.
    2. Marielle Christiansen, 1999. "Decomposition of a Combined Inventory and Time Constrained Ship Routing Problem," Transportation Science, INFORMS, vol. 33(1), pages 3-16, February.
    3. Bin Yu & Zixuan Peng & Zhihui Tian & Baozhen Yao, 2019. "Sailing speed optimization for tramp ships with fuzzy time window," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 308-330, June.
    4. Kai Li & Yongqiang Zhuo & Xiaoqing Luo, 2022. "Optimization method of fuel saving and cost reduction of tugboat main engine based on genetic algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 605-614, March.
    5. Qiang Meng & Tingsong Wang, 2010. "A chance constrained programming model for short-term liner ship fleet planning problems," Maritime Policy & Management, Taylor & Francis Journals, vol. 37(4), pages 329-346, July.
    6. Eleftherios Iakovou & Christos Douligeris & Huan Li & Chi Ip & Lalit Yudhbir, 1999. "A Maritime Global Route Planning Model for Hazardous Materials Transportation," Transportation Science, INFORMS, vol. 33(1), pages 34-48, February.
    7. Al-Khayyal, Faiz & Hwang, Seung-June, 2007. "Inventory constrained maritime routing and scheduling for multi-commodity liquid bulk, Part I: Applications and model," European Journal of Operational Research, Elsevier, vol. 176(1), pages 106-130, January.
    8. 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.
    9. Gelareh, Shahin & Pisinger, David, 2011. "Fleet deployment, network design and hub location of liner shipping companies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 947-964.

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