Maritime transportation underpins global trade and supply chains, with bunker fuel comprising a major share of voyage operating costs. Optimizing ship refueling is thus critical for improving energy efficiency and reducing expenses. A common sequential (two-stage) framework first derives a cost-minimal bunkering plan and then maximizes profit from cargo flows under that plan. Yet, this decoupled approach often yields suboptimal solutions, as refueling and container decisions are optimized separately. We develop an integrated one-stage model that simultaneously optimizes refueling and container flows on fixed liner services, comparing it to the two-stage model on stylized, industry-calibrated networks of 3, 8, and 15 ports. Results reveal consistent profit superiority for the integrated model, with gaps expanding alongside network scale and complexity (1.02% for 3 ports to 12.99% for 15 ports). Sensitivity tests on fuel-tank capacity, revenue levels, and local fuel price shocks affirm robustness, with gains amplifying under high demand or capacity constraints. Although the two-stage model provides modular transparency, the integrated formulation better balances fuel costs and cargo revenues, delivering substantial economic benefits in complex, constrained settings. Both models solve to optimality in seconds to minutes on standard hardware for tested instances, underscoring the integrated model's viability as a tactical decision tool. Overall, findings illuminate refueling optimization dynamics, quantify the inefficiency of bunker-first sequencing, and advise adopting integrated methods for larger networks.
Citation: Tianfang Ma, Wei Wang, Xuecheng Tian, Yan Liu. A comparative analysis of sequential and integrated optimization models for ship refueling operations[J]. Electronic Research Archive, 2026, 34(1): 534-552. doi: 10.3934/era.2026025
Maritime transportation underpins global trade and supply chains, with bunker fuel comprising a major share of voyage operating costs. Optimizing ship refueling is thus critical for improving energy efficiency and reducing expenses. A common sequential (two-stage) framework first derives a cost-minimal bunkering plan and then maximizes profit from cargo flows under that plan. Yet, this decoupled approach often yields suboptimal solutions, as refueling and container decisions are optimized separately. We develop an integrated one-stage model that simultaneously optimizes refueling and container flows on fixed liner services, comparing it to the two-stage model on stylized, industry-calibrated networks of 3, 8, and 15 ports. Results reveal consistent profit superiority for the integrated model, with gaps expanding alongside network scale and complexity (1.02% for 3 ports to 12.99% for 15 ports). Sensitivity tests on fuel-tank capacity, revenue levels, and local fuel price shocks affirm robustness, with gains amplifying under high demand or capacity constraints. Although the two-stage model provides modular transparency, the integrated formulation better balances fuel costs and cargo revenues, delivering substantial economic benefits in complex, constrained settings. Both models solve to optimality in seconds to minutes on standard hardware for tested instances, underscoring the integrated model's viability as a tactical decision tool. Overall, findings illuminate refueling optimization dynamics, quantify the inefficiency of bunker-first sequencing, and advise adopting integrated methods for larger networks.
| [1] |
B. Li, P. Afkhami, R. Khayamim, M. Borowska-Stefańska, S. Wiśniewski, A. M. Fathollahi-Fard, et al., An intelligent hyperheuristic algorithm for the berth allocation and scheduling problem at marine container terminals, Transp. Res. Part E Logist. Transp. Rev., 198 (2025), 104104. https://doi.org/10.1016/j.tre.2025.104104 doi: 10.1016/j.tre.2025.104104
|
| [2] |
M. Mollaoglu, I. G. Yazar Okur, M. Gurturk, B. Doganer Duman, Review on Sustainable Development Goals in maritime transportation: current research trends, applications, and future research opportunities, Environ. Sci. Pollut. Res., 31 (2024), 8312–8329. https://doi.org/10.1007/s11356-023-31622-1 doi: 10.1007/s11356-023-31622-1
|
| [3] |
A. Ala, A. Goli, Incorporating machine learning and optimization techniques for assigning patients to operating rooms by considering fairness policies, Eng. Appl. Artif. Intell., 136 (2024), 108980. https://doi.org/10.1016/j.engappai.2024.108980 doi: 10.1016/j.engappai.2024.108980
|
| [4] |
M. Y. N. Attari, A. Ala, V. Simic, D. Pamucar, N. Aydin, A gravitational meta-heuristic algorithm for solving resource-constrained project scheduling problems, Sadhana - Acad. Proc. Eng. Sci., 50 (2025), 104. https://doi.org/10.1007/s12046-025-02745-7 doi: 10.1007/s12046-025-02745-7
|
| [5] |
M. Besbes, S. Savin, Going bunkers: The joint route selection and refueling problem, Manuf. Serv. Oper. Manag., 11 (2009), 694–711. https://doi.org/10.1287/msom.1080.0249 doi: 10.1287/msom.1080.0249
|
| [6] |
K. Fagerholt, G. Laporte, I. Norstad, Reducing fuel emissions by optimizing speed on shipping routes, J. Oper. Res. Soc., 61 (2010), 523–529. https://doi.org/10.1057/jors.2009.77 doi: 10.1057/jors.2009.77
|
| [7] |
S. Wang, Q. Meng, Robust bunker management for liner shipping networks, Eur. J. Oper. Res., 243 (2015), 789–797. https://doi.org/10.1016/j.ejor.2014.12.049 doi: 10.1016/j.ejor.2014.12.049
|
| [8] |
T. Wang, P. Cheng, L. Zhen, Green development of the maritime industry: Overview, perspectives, and future research opportunities, Transp. Res. Part E Logist. Transp. Rev., 179 (2023), 103322. https://doi.org/10.1016/j.tre.2023.103322 doi: 10.1016/j.tre.2023.103322
|
| [9] |
Q. Meng, S. Wang, Liner shipping service network design with empty container repositioning, Transp. Res. Part E Logist. Transp. Rev., 47 (2011), 695–708. https://doi.org/10.1016/j.tre.2011.02.004 doi: 10.1016/j.tre.2011.02.004
|
| [10] |
Y. Wang, Q. Meng, Y. Du, Y. Zhao, Liner shipping network design with deadlines, Comput. Oper. Res., 41 (2014), 140–149. https://doi.org/10.1016/j.cor.2013.06.010 doi: 10.1016/j.cor.2013.06.010
|
| [11] |
S. Wang, Q. Meng, Robust schedule design for liner shipping services, Transp. Res. Part E Logist. Transp. Rev., 48 (2012), 1093–1106. https://doi.org/10.1016/j.tre.2012.04.007 doi: 10.1016/j.tre.2012.04.007
|
| [12] |
D. Ronen, The effect of oil price on the optimal speed of ships, J. Oper. Res. Soc., 33 (1982), 1035–1040. https://doi.org/10.1057/jors.1982.215 doi: 10.1057/jors.1982.215
|
| [13] |
E. E. Halvorsen-Weare, K. Fagerholt, L. M. Nonås, B. E. Asbjørnslett, Optimal fleet composition and periodic routing of offshore supply vessels, Eur. J. Oper. Res., 223 (2012), 508–517. https://doi.org/10.1016/j.ejor.2012.06.017 doi: 10.1016/j.ejor.2012.06.017
|
| [14] |
Y. Du, Q. Meng, S. Wang, H. Kuang, Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data, Transp. Res. Part B Methodol., 122 (2019), 88–114. https://doi.org/10.1016/j.trb.2019.02.004 doi: 10.1016/j.trb.2019.02.004
|
| [15] |
H. Psaraftis, C. Kontovas, Speed models for energy-efficient maritime transportation: A taxonomy and survey, Transp. Res. Part C Emerg. Technol., 26 (2013), 331–351. https://doi.org/10.1016/j.trc.2012.09.012 doi: 10.1016/j.trc.2012.09.012
|
| [16] |
J. E. Korsvik, K. Fagerholt, G. Laporte, A tabu search heuristic for ship routing and scheduling, J. Oper. Res. Soc., 61 (2010), 594–603. https://doi.org/10.1057/jors.2008.192 doi: 10.1057/jors.2008.192
|
| [17] |
L. Zhen, S. Wang, D. Zhuge, Dynamic programming for optimal ship refueling decision, Transp. Res. Part E Logist. Transp. Rev., 100 (2017), 63–74. https://doi.org/10.1016/j.tre.2016.12.013 doi: 10.1016/j.tre.2016.12.013
|
| [18] |
S. Wang, Q. Meng, Z. Liu, Bunker consumption optimization methods in shipping: A critical review and extensions, Transp. Res. Part E Logist. Transp. Rev., 53 (2013), 49–62. https://doi.org/10.1016/j.tre.2013.02.003 doi: 10.1016/j.tre.2013.02.003
|
| [19] |
S. Xie, Z. Hu, J. Wang, Scenario-based comprehensive expansion planning model for a coupled transportation and active distribution system, Appl. Energy, 255 (2019), 113782. https://doi.org/10.1016/j.apenergy.2019.113782 doi: 10.1016/j.apenergy.2019.113782
|
| [20] |
T. Gerres, J. P. C. Ávila, P. L. Llamas, T. G. San Román, A review of cross-sector decarbonisation potentials in the European energy intensive industry, J. Clean. Prod., 210 (2019), 585–601. https://doi.org/10.1016/j.jclepro.2018.11.036 doi: 10.1016/j.jclepro.2018.11.036
|
| [21] |
L. Fan, W. W. Wilson, B. Dahl, Congestion, port expansion and spatial competition for US container imports, Transp. Res. Part E Logist. Transp. Rev., 48 (2012), 1121–1136. https://doi.org/10.1016/j.tre.2012.04.006 doi: 10.1016/j.tre.2012.04.006
|
| [22] |
C. E. M. Plum, P. N. Jensen, D. Pisinger, Bunker purchasing with contracts, Marit. Econ. Logist., 16 (2014), 418–435. https://doi.org/10.1057/mel.2014.15 doi: 10.1057/mel.2014.15
|
| [23] |
S. Devarapali, A. Manske, R. Khayamim, E. Jacobs, B. Li, Z. Elmi, et al., Electric tugboat deployment in maritime transportation: detailed analysis of advantages and disadvantages, Marit. Bus. Rev., 9 (2024), 263–291. https://doi.org/10.1108/MBR-09-2023-0067 doi: 10.1108/MBR-09-2023-0067
|