&nbsp;Crude oil tanker fleet deployment optimization to improve ship energy efficiency under the background of carbon emission reduction

doi:10.16411/j.cnki.issn1006-7736.2023.03.003

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Journal of Dalian Maritime University ›› 2023, Vol. 49 ›› Issue (3) : 20-30. DOI: 10.16411/j.cnki.issn1006-7736.2023.03.003

Crude oil tanker fleet deployment optimization to improve ship energy efficiency under the background of carbon emission reduction

JIANG Weiqi，ZHONG Ming^*，ZHANG Mengdi

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Abstract

In response to the existing policies on ship energy efficiency coefficients and carbon intensity indicators proposed by the International Maritime Organization, it is expected that a certain number of existing ships will need to use new technologies or modify the propulsion plant components to improve ship energy efficiency. At the same time, more and more ships were choosing to install closed desulfurization devices to cope with sulfur emission control zone restrictions. In this context, in response to the problem of crude oil tanker fleet deployment, a mixed integer nonlinear optimization model was established by considering the transformation strategy of improving energy efficiency and installing desulfurization devices, with the goal of minimizing the sum of annual fuel cost and carbon emission cost, and considering carbon emission constraints. A self-adaptive-simulated annealing particle swarm optimization algorithm was selected to solve the model, and the sensitivity analysis of fuel price fluctuation and carbon tax rate was carried out, and the rationality and effectiveness of the model were verified through numerical examples.

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carbon emission reduction / crude oil tanker / optimization of fleet allocation / ship energy efficiency / self-adaptive simulated annealing particle swarm algorithm (ASAPSO)

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JIANG Weiqi, ZHONG Ming, ZHANG Mengdi. Crude oil tanker fleet deployment optimization to improve ship energy efficiency under the background of carbon emission reduction[J]. Journal of Dalian Maritime University. 2023, 49(3): 20-30 https://doi.org/10.16411/j.cnki.issn1006-7736.2023.03.003

References

[1]王林.航运业更严排放指标强势来袭 [N].中国能源报,2022-12-05(010).
[2]胡敬梁,田兆波,王兴,等.基于EEXI计算方法的大型矿砂船节能改造能效分析[J].航海,2023(1):29-33.
[3]PERAKIS A N, JARAMILLO D I. Fleet deployment optimization for liner shipping Part 1. Background, problem formulation and solution approaches[J]. Maritime Policy and Management, 1991, 18(3): 183-200.
[4]杨华龙,钟铭. 集装箱班轮航线配船优化决策研究[J]. 大连海事大学学报, 1996, 22(3): 51-54.
YANG H L, ZHONG M. Optimization of container liner route fleet deployment[J]. Journal of Dalian Maritime University,1996,22(3):51-54. (in Chinese)
[5]WANG S, MENG Q. Container liner fleet deployment: a systematic overview[J]. Transportation Research Part C: Emerging Technologies, 2017, 77: 389-404.
[6]谢新连,赵瑞嘉.船舶资源优化配置问题研究现状与展望[J].大连海事大学学报,2019,45(1):66-73.
XIE X L, ZHAO R J. Research status and prospect of optimizing ship resources distribution[J]. Journal of Dalian Maritime University, 2019,45(1):66-73.(in Chinese)
[7]余冠桥,钟铭,袁程,等. 需求不确定集装箱班轮航线配船—运输两阶段鲁棒优化模型[J]. 大连海事大学学报, 2020, 46(4): 69-75.
YU G Q, ZHONG M,YUAN C, et al. The two-stage robust optimization model of container liner ship allocation-transportation with demand uncertainty[J]. Journal of Dalian Maritime University, 2020, 46(4): 69-75（in Chinese）
[8]ZHAO Z, WANG X, WANG H, et al. Fleet Deployment optimization for LNG shipping vessels considering the influence of mixed factors[J]. Journal of Marine Science and Engineering, 2022, 10(12): 2034.
[9]许欢,刘伟,尚雨廷. 低碳经济下班轮航线配船模型及其算法实现[J]. 交通运输系统工程与信息, 2013, 13(4): 176-181.
XU H, LIU W, SHANG Y T. Fleet deployment model for liners under low-carbon economy and its algorithms implementation[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(4): 176-181.(in Chinese)
[10]Sheng D, Meng Q, Li Z C. Optimal vessel speed and fleet size for industrial shipping services under the emission control area regulation[J]. Transportation Research Part C: Emerging Technologies, 2019, 105: 37-53.
[11]蒋琳雅,胡坚堃.硫排放限制下航线配船优化与减排策略选择[J].上海海事大学学报,2021,42(2):81-87.
JIANG L Y, HU J K. Fleet deployment optimization and emission reduction strategy selection under sulfur emission limits[J]. Journal of Shanghai Maritime University, 2021,42(2):81-87.(in Chinese).
[12]王振宇,谢新连,许小卫.新冠疫情和双碳战略背景下班轮运输多目标优化[J].大连海事大学学报,2022,48(1):31-41.
WANG Z Y, XIE X L, XU X W. Multi objective optimization of liner transportation under COVID-19 and Dual Carbon Strategy background[J]. Journal of Dalian Maritime University, 2022,48(1):31-41.（in Chinese）
[13]吕靖,鲍乾.考虑硫排放限制和碳减排的集装箱班轮航线配船[J].上海海事大学学报,2022,43(2):25-31+39.
LYU J, BAO Q. Container liner route fleet deployment under sulphur emission limitation and carbon emission reduction[J]. Journal of Shanghai Maritime University, 2022,43(2):25-31+39.(in Chinese)
[14]WU Y, HUANG Y, WANG H, et al. Nonlinear programming for fleet deployment, voyage planning and speed optimization in sustainable liner shipping[J]. Electronic Research Archive, 2023, 31(1): 147-168.
[15]GEORGE MALLOUPPAS, ELIAS AR. YFANTIS. Decarbonization in Shipping Industry: A Review of Research, Technology Development, and Innovation Proposals[J] Journal of Marine Science and Engineering, 2021,9(4): 415.
[16]SCHINAS O, BERGMANN N. The short-term cost of greening the global fleet[J]. Sustainability,
2021, 13(16): 9439.
[17]闫群民,马瑞卿,马永翔,等.一种自适应模拟退火粒子群优化算法[J].西安电子科技大学学报,2021,48(4):120-127.
YAN Q M, MA R Q, MA Y X. Adaptive simulated annealing particle swarm optimization algorithm[J]. Journal of Xidian University, 2021,48(4):120-127.(in Chinese)
[18]刘华蓥,林玉娥,王淑云.粒子群算法的改进及其在求解约束优化问题中的应用[J].吉林大学学报(理学版),2005(4):472-476.
LIU H Y, LIN Y E, WANG S Y. A modified particle swarm optimization for solving constrained optimization problems[J].Journal of Jilin University(Science Edition), 2005(4):472-476.(in Chinese)
[19]WANG S, YANG D, LU J. A connectivity reliability-cost approach for path selection in the maritime transportation of China’s crude oil imports[J]. Maritime Policy & Management, 2018, 45(5): 567-584.
[20]CORBETT J J, WANG H, WINEBRAKE J J. The effectiveness and costs of speed reductions on emissions from international shipping[J]. Transportation Research Part D: Transport and Environment,
2009, 14(8): 593-598.
[21]王世进,赵钱阳,胡一竑.基于概率禁忌搜索算法的班轮航速和航线配船优化研究[J].工业工程与管理,2022,27(1):1-10.
WANG S J, ZHAO Q Y, HU Y H. Optimization of liner ship speed and route schedule based on a probabilistic tabu search algorithm[J]. Industrial Engineering and Management, 2022,27(1):1-10.(in Chinese)
[22]YADAV A. AEFA: Artificial electric field algorithm for global optimization[J]. Swarm and Evolutionary Computation, 2019, 48: 93-108