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Service-oriented operational decision optimization for dry bulk shipping fleet under stochastic demand

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Abstract

Dry bulk shipping plays a crucial role in intercontinental bulk cargo transport, with operators managing fleets to meet shippers’ transportation demand. A primary challenge for these operators is making optimal operational decisions about ship scheduling, routing, and sailing speed in the face of stochastic demand. We address this problem by developing a stochastic integer programming model designed to maximize revenue while maintaining high service levels for shippers. We quantify service levels for shippers using the probability of demand being fully satisfied. To solve this model, we introduce an innovative offline–online Lagrange relaxation framework. This framework leverages training data to determine the optimal Lagrange multiplier, which subsequently guides decision-making with test data. Numerical experiments show that our method closely matches the performance of Sampling Average Approximation (SAA) solutions while reducing computational time.

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Data availability

Data sets generated during the current study are available from the corresponding author on reasonable request. They were used under license for the current study, and so are not publicly available.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 72101042).

Funding

This work was supported by the National Natural Foundation Science of China (Grant Number 72101042).

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Authors and Affiliations

  1. College of Transportation Engineering, Dalian Maritime University, Dalian, China

    Jun Gao, Jie Wang & Jinpeng Liang

  2. School of Maritime Economics and Management, Dalian Maritime University, Dalian, China

    Liming Li

Authors
  1. Jun Gao
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  2. Jie Wang
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  3. Liming Li
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  4. Jinpeng Liang
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Correspondence to Jie Wang or Jinpeng Liang.

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Gao, J., Wang, J., Li, L. et al. Service-oriented operational decision optimization for dry bulk shipping fleet under stochastic demand. Optim Eng (2024). https://doi.org/10.1007/s11081-024-09884-6

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